Scholars@Duke
Rishi Kamaleswaran

Rishi Kamaleswaran

Instructor in the Department of Surgery
Trauma, Acute, and Critical Care Surgery
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Selected Publications

Association between comorbidities at ICU admission and post-Sepsis physical impairment: A retrospective cohort study.

Journal Article J Crit Care · October 2024 PURPOSE: Few studies have measured the association between pre-existing comorbidities and post-sepsis physical impairment. The study aimed to estimate the risk of physical impairment at hospital discharge among sepsis patients, adjusting for pre-existing p ... Full text Link to item Cite

Derivation and validation of generalized sepsis-induced acute respiratory failure phenotypes among critically ill patients: a retrospective study.

Journal Article Critical care (London, England) · October 2024 BackgroundSeptic patients who develop acute respiratory failure (ARF) requiring mechanical ventilation represent a heterogenous subgroup of critically ill patients with widely variable clinical characteristics. Identifying distinct phenotypes of t ... Full text Cite

The Precision Resuscitation With Crystalloids in Sepsis (PRECISE) Trial: A Trial Protocol.

Journal Article JAMA Netw Open · September 3, 2024 IMPORTANCE: Intravenous fluids are an essential part of treatment in sepsis, but there remains clinical equipoise on which type of crystalloid fluids to use in sepsis. A previously reported sepsis subphenotype (ie, group D) has demonstrated a substantial m ... Full text Link to item Cite

Meta-learning in Healthcare: A Survey

Journal Article SN Computer Science · August 1, 2024 As a subset of machine learning, meta-learning, or learning to learn, aims at improving the model’s capabilities by employing prior knowledge and experience. A meta-learning paradigm can appropriately tackle the conventional challenges of traditional learn ... Full text Cite

Identification and transcriptomic assessment of latent profile pediatric septic shock phenotypes.

Journal Article Crit Care · July 17, 2024 BACKGROUND: Sepsis poses a grave threat, especially among children, but treatments are limited owing to heterogeneity among patients. We sought to test the clinical and biological relevance of pediatric septic shock subclasses identified using reproducible ... Full text Link to item Cite

Lung segment anything model (LuSAM): a decoupled prompt-integrated framework for automated lung segmentation on chest x-Ray images.

Journal Article Biomed Phys Eng Express · July 10, 2024 Accurate lung segmentation in chest x-ray images plays a pivotal role in early disease detection and clinical decision-making. In this study, we introduce an innovative approach to enhance the precision of lung segmentation using the Segment Anything Model ... Full text Link to item Cite

Plasma metabolomics identifies differing endotypes of recurrent wheezing in preschool children differentiated by symptoms and social disadvantage.

Journal Article Sci Rep · July 9, 2024 Preschool children with recurrent wheezing are a heterogeneous population with many underlying biological pathways that contribute to clinical presentations. Although the morbidity of recurrent wheezing in preschool children is significant, biological stud ... Full text Link to item Cite

A common data model for the standardization of intensive care unit medication features

Journal Article JAMIA Open · July 1, 2024 Objective: Common data models provide a standard means of describing data for artificial intelligence (AI) applications, but this process has never been undertaken for medications used in the intensive care unit (ICU). We sought to develop a common data mo ... Full text Cite

Parsimonious waveform-derived features consisting of pulse arrival time and heart rate variability predicts the onset of septic shock

Journal Article Biomedical Signal Processing and Control · June 1, 2024 Sepsis is a major public health emergency and one of the leading causes of morbidity and mortality in critically ill patients. For each hour treatment is delayed, shock-related mortality increases, so early diagnosis and intervention is of utmost importanc ... Full text Cite

Dysfunctional neutrophil type 1 interferon responses in preschool children with recurrent wheezing and IL-4–mediated aeroallergen sensitization

Journal Article Journal of Allergy and Clinical Immunology: Global · May 1, 2024 Background: The innate mechanisms associated with viral exacerbations in preschool children with recurrent wheezing are not understood. Objective: We sought to assess differential gene expression in blood neutrophils from preschool children with recurrent ... Full text Cite

The Pediatric Data Science and Analytics Subgroup of the Pediatric Acute Lung Injury and Sepsis Investigators Network: Use of Supervised Machine Learning Applications in Pediatric Critical Care Medicine Research.

Journal Article Pediatr Crit Care Med · April 1, 2024 OBJECTIVE: Perform a scoping review of supervised machine learning in pediatric critical care to identify published applications, methodologies, and implementation frequency to inform best practices for the development, validation, and reporting of predict ... Full text Link to item Cite

Validation of a screening panel for pediatric metabolic dysfunction-associated steatotic liver disease using metabolomics.

Journal Article Hepatol Commun · March 1, 2024 BACKGROUND: Metabolic dysfunction-associated steatotic liver disease (MASLD), formerly known as NAFLD, is the most common liver disease in children. Liver biopsy remains the gold standard for diagnosis, although more efficient screening methods are needed. ... Full text Link to item Cite

Development and Validation of a Model for Endotracheal Intubation and Mechanical Ventilation Prediction in PICU Patients.

Journal Article Pediatr Crit Care Med · March 1, 2024 OBJECTIVES: To develop and externally validate an intubation prediction model for children admitted to a PICU using objective and routinely available data from the electronic medical records (EMRs). DESIGN: Retrospective observational cohort study. SETTING ... Full text Link to item Cite

Racial Differences in Accuracy of Predictive Models for High-Flow Nasal Cannula Failure in COVID-19.

Journal Article Crit Care Explor · March 2024 OBJECTIVES: To develop and validate machine learning (ML) models to predict high-flow nasal cannula (HFNC) failure in COVID-19, compare their performance to the respiratory rate-oxygenation (ROX) index, and evaluate model accuracy by self-reported race. DE ... Full text Link to item Cite

Machine learning-driven identification of the gene-expression signature associated with a persistent multiple organ dysfunction trajectory in critical illness.

Journal Article EBioMedicine · January 2024 BACKGROUND: Multiple organ dysfunction syndrome (MODS) disproportionately drives morbidity and mortality among critically ill patients. However, we lack a comprehensive understanding of its pathobiology. Identification of genes associated with a persistent ... Full text Link to item Cite

Improving mixed-integer temporal modeling by generating synthetic data using conditional generative adversarial networks: A case study of fluid overload prediction in the intensive care unit.

Journal Article Comput Biol Med · January 2024 OBJECTIVE: The challenge of mixed-integer temporal data, which is particularly prominent for medication use in the critically ill, limits the performance of predictive models. The purpose of this evaluation was to pilot test integrating synthetic data with ... Full text Link to item Cite

Association between comorbidities at ICU admission and post-Sepsis physical impairment: A retrospective cohort study.

Journal Article J Crit Care · October 2024 PURPOSE: Few studies have measured the association between pre-existing comorbidities and post-sepsis physical impairment. The study aimed to estimate the risk of physical impairment at hospital discharge among sepsis patients, adjusting for pre-existing p ... Full text Link to item Cite

Derivation and validation of generalized sepsis-induced acute respiratory failure phenotypes among critically ill patients: a retrospective study.

Journal Article Critical care (London, England) · October 2024 BackgroundSeptic patients who develop acute respiratory failure (ARF) requiring mechanical ventilation represent a heterogenous subgroup of critically ill patients with widely variable clinical characteristics. Identifying distinct phenotypes of t ... Full text Cite

The Precision Resuscitation With Crystalloids in Sepsis (PRECISE) Trial: A Trial Protocol.

Journal Article JAMA Netw Open · September 3, 2024 IMPORTANCE: Intravenous fluids are an essential part of treatment in sepsis, but there remains clinical equipoise on which type of crystalloid fluids to use in sepsis. A previously reported sepsis subphenotype (ie, group D) has demonstrated a substantial m ... Full text Link to item Cite

Meta-learning in Healthcare: A Survey

Journal Article SN Computer Science · August 1, 2024 As a subset of machine learning, meta-learning, or learning to learn, aims at improving the model’s capabilities by employing prior knowledge and experience. A meta-learning paradigm can appropriately tackle the conventional challenges of traditional learn ... Full text Cite

Identification and transcriptomic assessment of latent profile pediatric septic shock phenotypes.

Journal Article Crit Care · July 17, 2024 BACKGROUND: Sepsis poses a grave threat, especially among children, but treatments are limited owing to heterogeneity among patients. We sought to test the clinical and biological relevance of pediatric septic shock subclasses identified using reproducible ... Full text Link to item Cite

Lung segment anything model (LuSAM): a decoupled prompt-integrated framework for automated lung segmentation on chest x-Ray images.

Journal Article Biomed Phys Eng Express · July 10, 2024 Accurate lung segmentation in chest x-ray images plays a pivotal role in early disease detection and clinical decision-making. In this study, we introduce an innovative approach to enhance the precision of lung segmentation using the Segment Anything Model ... Full text Link to item Cite

Plasma metabolomics identifies differing endotypes of recurrent wheezing in preschool children differentiated by symptoms and social disadvantage.

Journal Article Sci Rep · July 9, 2024 Preschool children with recurrent wheezing are a heterogeneous population with many underlying biological pathways that contribute to clinical presentations. Although the morbidity of recurrent wheezing in preschool children is significant, biological stud ... Full text Link to item Cite

A common data model for the standardization of intensive care unit medication features

Journal Article JAMIA Open · July 1, 2024 Objective: Common data models provide a standard means of describing data for artificial intelligence (AI) applications, but this process has never been undertaken for medications used in the intensive care unit (ICU). We sought to develop a common data mo ... Full text Cite

Parsimonious waveform-derived features consisting of pulse arrival time and heart rate variability predicts the onset of septic shock

Journal Article Biomedical Signal Processing and Control · June 1, 2024 Sepsis is a major public health emergency and one of the leading causes of morbidity and mortality in critically ill patients. For each hour treatment is delayed, shock-related mortality increases, so early diagnosis and intervention is of utmost importanc ... Full text Cite

Dysfunctional neutrophil type 1 interferon responses in preschool children with recurrent wheezing and IL-4–mediated aeroallergen sensitization

Journal Article Journal of Allergy and Clinical Immunology: Global · May 1, 2024 Background: The innate mechanisms associated with viral exacerbations in preschool children with recurrent wheezing are not understood. Objective: We sought to assess differential gene expression in blood neutrophils from preschool children with recurrent ... Full text Cite

The Pediatric Data Science and Analytics Subgroup of the Pediatric Acute Lung Injury and Sepsis Investigators Network: Use of Supervised Machine Learning Applications in Pediatric Critical Care Medicine Research.

Journal Article Pediatr Crit Care Med · April 1, 2024 OBJECTIVE: Perform a scoping review of supervised machine learning in pediatric critical care to identify published applications, methodologies, and implementation frequency to inform best practices for the development, validation, and reporting of predict ... Full text Link to item Cite

Validation of a screening panel for pediatric metabolic dysfunction-associated steatotic liver disease using metabolomics.

Journal Article Hepatol Commun · March 1, 2024 BACKGROUND: Metabolic dysfunction-associated steatotic liver disease (MASLD), formerly known as NAFLD, is the most common liver disease in children. Liver biopsy remains the gold standard for diagnosis, although more efficient screening methods are needed. ... Full text Link to item Cite

Development and Validation of a Model for Endotracheal Intubation and Mechanical Ventilation Prediction in PICU Patients.

Journal Article Pediatr Crit Care Med · March 1, 2024 OBJECTIVES: To develop and externally validate an intubation prediction model for children admitted to a PICU using objective and routinely available data from the electronic medical records (EMRs). DESIGN: Retrospective observational cohort study. SETTING ... Full text Link to item Cite

Racial Differences in Accuracy of Predictive Models for High-Flow Nasal Cannula Failure in COVID-19.

Journal Article Crit Care Explor · March 2024 OBJECTIVES: To develop and validate machine learning (ML) models to predict high-flow nasal cannula (HFNC) failure in COVID-19, compare their performance to the respiratory rate-oxygenation (ROX) index, and evaluate model accuracy by self-reported race. DE ... Full text Link to item Cite

Machine learning-driven identification of the gene-expression signature associated with a persistent multiple organ dysfunction trajectory in critical illness.

Journal Article EBioMedicine · January 2024 BACKGROUND: Multiple organ dysfunction syndrome (MODS) disproportionately drives morbidity and mortality among critically ill patients. However, we lack a comprehensive understanding of its pathobiology. Identification of genes associated with a persistent ... Full text Link to item Cite

Improving mixed-integer temporal modeling by generating synthetic data using conditional generative adversarial networks: A case study of fluid overload prediction in the intensive care unit.

Journal Article Comput Biol Med · January 2024 OBJECTIVE: The challenge of mixed-integer temporal data, which is particularly prominent for medication use in the critically ill, limits the performance of predictive models. The purpose of this evaluation was to pilot test integrating synthetic data with ... Full text Link to item Cite

Social Media as a Sensor: Analyzing Twitter Data for Breast Cancer Medication Effects Using Natural Language Processing

Chapter · January 1, 2024 Breast cancer is a significant public health concern and is the leading cause of cancer-related deaths among women. Despite advances in breast cancer treatments, medication non-adherence remains a major problem. As electronic health records do not typicall ... Full text Cite

Enabling Continuous Breathing-Phase Contextualization via Wearable-Based Impedance Pneumography and Lung Sounds: A Feasibility Study.

Journal Article IEEE J Biomed Health Inform · December 2023 Chronic respiratory diseases affect millions and are leading causes of death in the US and worldwide. Pulmonary auscultation provides clinicians with critical respiratory health information through the study of Lung Sounds (LS) and the context of the breat ... Full text Link to item Cite

Functional immunophenotyping of blood neutrophils identifies novel endotypes of viral response in preschool children with recurrent wheezing.

Journal Article J Allergy Clin Immunol · December 2023 BACKGROUND: Preschool children with recurrent wheezing are heterogeneous, with differing responses to respiratory viral infections. Although neutrophils are crucial for host defense, their function has not been studied in this population. OBJECTIVE: We per ... Full text Link to item Cite

ClotCatcher: a novel natural language model to accurately adjudicate venous thromboembolism from radiology reports.

Journal Article BMC Med Inform Decis Mak · November 16, 2023 INTRODUCTION: Accurate identification of venous thromboembolism (VTE) is critical to develop replicable epidemiological studies and rigorous predictions models. Traditionally, VTE studies have relied on international classification of diseases (ICD) codes ... Full text Link to item Cite

Machine learning vs. traditional regression analysis for fluid overload prediction in the ICU.

Journal Article Sci Rep · November 10, 2023 Fluid overload, while common in the ICU and associated with serious sequelae, is hard to predict and may be influenced by ICU medication use. Machine learning (ML) approaches may offer advantages over traditional regression techniques to predict it. We com ... Full text Link to item Cite

A MACHINE LEARNING MODEL DERIVED FROM ANALYSIS OF TIME-COURSE GENE-EXPRESSION DATASETS REVEALS TEMPORALLY STABLE GENE MARKERS PREDICTIVE OF SEPSIS MORTALITY.

Journal Article Shock · November 1, 2023 Sepsis is associated with significant mortality and morbidity among critically ill patients admitted to intensive care units and represents a major health challenge globally. Given the significant clinical and biological heterogeneity among patients and th ... Full text Link to item Cite

A machine learning model for predicting congenital heart defects from administrative data.

Journal Article Birth Defects Res · November 1, 2023 INTRODUCTION: International Classification of Diseases (ICD) codes recorded in administrative data are often used to identify congenital heart defects (CHD). However, these codes may inaccurately identify true positive (TP) CHD individuals. CHD surveillanc ... Full text Link to item Cite

HIRA: Heart Rate Interval based Rapid Alert score to characterize autonomic dysfunction among patients with sepsis-related acute respiratory failure (ARF).

Journal Article Physiol Meas · October 13, 2023 Objective. To examine whether heart rate interval based rapid alert (HIRA) score derived from a combination model of heart rate variability (HRV) and modified early warning score (MEWS) is a surrogate for the detection of acute respiratory failure (ARF) in ... Full text Link to item Cite

Who is pregnant? Defining real-world data-based pregnancy episodes in the National COVID Cohort Collaborative (N3C)

Journal Article JAMIA Open · October 1, 2023 Objectives: To define pregnancy episodes and estimate gestational age within electronic health record (EHR) data from the National COVID Cohort Collaborative (N3C). Materials and Methods: We developed a comprehensive approach, named Hierarchy and rule-base ... Full text Cite

Cluster analysis driven by unsupervised latent feature learning of medications to identify novel pharmacophenotypes of critically ill patients.

Journal Article Sci Rep · September 20, 2023 Unsupervised clustering of intensive care unit (ICU) medications may identify unique medication clusters (i.e., pharmacophenotypes) in critically ill adults. We performed an unsupervised analysis with Restricted Boltzmann Machine of 991 medications profile ... Full text Link to item Cite

Risk for stillbirth among pregnant individuals with SARS-CoV-2 infection varied by gestational age.

Journal Article Am J Obstet Gynecol · September 2023 BACKGROUND: Despite previous research findings on higher risks of stillbirth among pregnant individuals with SARS-CoV-2 infection, it is unclear whether the gestational timing of viral infection modulates this risk. OBJECTIVE: This study aimed to examine t ... Full text Link to item Cite

Uncertainty-Aware Convolutional Neural Network for Identifying Bilateral Opacities on Chest X-rays: A Tool to Aid Diagnosis of Acute Respiratory Distress Syndrome.

Journal Article Bioengineering (Basel) · August 8, 2023 Acute Respiratory Distress Syndrome (ARDS) is a severe lung injury with high mortality, primarily characterized by bilateral pulmonary opacities on chest radiographs and hypoxemia. In this work, we trained a convolutional neural network (CNN) model that ca ... Full text Link to item Cite

Granger Causal Chain Discovery for Sepsis-Associated Derangements via Continuous-Time Hawkes Processes

Conference Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · August 6, 2023 Modern health care systems are conducting continuous, automated surveillance of the electronic medical record (EMR) to identify adverse events with increasing frequency; however, many events such as sepsis do not have elucidated prodromes (i.e., event chai ... Full text Cite

Metabolomics identifies disturbances in arginine, phenylalanine, and glycine metabolism as differentiating features of exacerbating atopic asthma in children

Journal Article Journal of Allergy and Clinical Immunology: Global · August 1, 2023 Background: Asthma exacerbations are highly prevalent in children, but only a few studies have examined the biologic mechanisms underlying exacerbations in this population. Objective: High-resolution metabolomics analyses were performed to understand the d ... Full text Cite

Externally validated deep learning model to identify prodromal Parkinson's disease from electrocardiogram.

Journal Article Sci Rep · July 29, 2023 Little is known about electrocardiogram (ECG) markers of Parkinson's disease (PD) during the prodromal stage. The aim of the study was to build a generalizable ECG-based fully automatic artificial intelligence (AI) model to predict PD risk during the prodr ... Full text Link to item Cite

Hormone replacement therapy and COVID-19 outcomes in solid organ transplant recipients compared with the general population.

Journal Article Am J Transplant · July 2023 Exogenous estrogen is associated with reduced coronavirus disease (COVID) mortality in nonimmunosuppressed/immunocompromised (non-ISC) postmenopausal females. Here, we examined the association of estrogen or testosterone hormone replacement therapy (HRT) w ... Full text Link to item Cite

Improving irregular temporal modeling by integrating synthetic data to the electronic medical record using conditional GANs: a case study of fluid overload prediction in the intensive care unit.

Journal Article medRxiv · June 27, 2023 OBJECTIVE: The challenge of irregular temporal data, which is particularly prominent for medication use in the critically ill, limits the performance of predictive models. The purpose of this evaluation was to pilot test integrating synthetic data within a ... Full text Link to item Cite

RNA Sequencing Analysis of CD4+T Cells Exposed to Airway Fluid from Children with Pediatric Acute Respiratory Distress Syndrome

Journal Article Critical Care Explorations · June 23, 2023 IMPORTANCE: CD4+T cells contribute to lung inflammation in acute respiratory distress syndrome. The CD4+T-cell response in pediatric acute respiratory distress syndrome (PARDS) is unknown. OBJECTIVES: To identify differentially expressed genes and networks ... Full text Cite

Synthetic seismocardiogram generation using a transformer-based neural network.

Journal Article J Am Med Inform Assoc · June 20, 2023 OBJECTIVE: To design and validate a novel deep generative model for seismocardiogram (SCG) dataset augmentation. SCG is a noninvasively acquired cardiomechanical signal used in a wide range of cardivascular monitoring tasks; however, these approaches are l ... Full text Link to item Cite

Identification of a pediatric acute hypoxemic respiratory failure signature in peripheral blood leukocytes at 24 hours post-ICU admission with machine learning

Journal Article Frontiers in Pediatrics · May 3, 2023 Background: There is no generalizable transcriptomics signature of pediatric acute respiratory distress syndrome. Our goal was to identify a whole blood differential gene expression signature for pediatric acute hypoxemic respiratory failure (AHRF) using t ... Full text Cite

Pharmacophenotype identification of intensive care unit medications using unsupervised cluster analysis of the ICURx common data model.

Journal Article Crit Care · May 2, 2023 BACKGROUND: Identifying patterns within ICU medication regimens may help artificial intelligence algorithms to better predict patient outcomes; however, machine learning methods incorporating medications require further development, including standardized ... Full text Link to item Cite

Cluster analysis of plasma cytokines identifies two unique endotypes of children with asthma in the pediatric intensive care unit.

Journal Article Sci Rep · March 2, 2023 Children with life-threatening asthma exacerbations who are admitted to a pediatric intensive care unit (PICU) are a heterogeneous group with poorly studied inflammatory features. We hypothesized that distinct clusters of children with asthma in a PICU wou ... Full text Link to item Cite

Untargeted, High-Resolution Metabolomics in Pediatric Eosinophilic Esophagitis.

Journal Article J Pediatr Gastroenterol Nutr · March 1, 2023 BACKGROUND/OBJECTIVES: Eosinophilic esophagitis (EoE) is an inflammatory disease of unclear etiology. The aim of this study was to use untargeted plasma metabolomics to identify metabolic pathway alterations associated with EoE to better understand the pat ... Full text Link to item Cite

Integrating structured and unstructured data for timely prediction of bloodstream infection among children.

Journal Article Pediatr Res · March 2023 BACKGROUND: Hospitalized children with central venous lines (CVLs) are at higher risk of hospital-acquired infections. Information in electronic health records (EHRs) can be employed in training deep learning models to predict the onset of these infections ... Full text Link to item Cite

Overt and Occult Hypoxemia in Patients Hospitalized with COVID-19

Journal Article Critical Care Explorations · January 20, 2023 IMPORTANCE: Progressive hypoxemia is the predominant mode of deterioration in COVID-19. Among hypoxemia measures, the ratio of the Pao2to the Fio2(P/F ratio) has optimal construct validity but poor availability because it requires arterial blood sampling. ... Full text Cite

Investigating the Impact of Temporal Labeling of Emergency Department Visits for COVID-19: Comparing Healthcare Disparities Analyses Using Comprehensive, Single-Site Data with National COVID Cohort Collaborative (N3C) Data

Conference 2023 Systems and Information Engineering Design Symposium, SIEDS 2023 · January 1, 2023 National COVID Cohort Collaborative (N3C) enclave provides health researchers with a rich dataset from 76 contributing clinical sites. However, the harmonized data lacks certain details available in sites' local electronic health records (EHRs), such as th ... Full text Cite

Optimizing the Synergistic Potential of Pseudo-Labels from Radiology Notes and Annotated Ground Truth in Identifying Pulmonary Opacities on Chest Radiographs for Early Detection of Acute Respiratory Distress Syndrome.

Journal Article AMIA Annu Symp Proc · 2023 Acute Respiratory Distress Syndrome (ARDS) is a life-threatening lung injury, hallmarks of which are bilateral radiographic opacities. Studies have shown that early recognition of ARDS could reduce severity and lethal clinical sequela. A Convolutional Neur ... Link to item Cite

Exacerbation-prone pediatric asthma is associated with arginine, lysine, and methionine pathway alterations.

Journal Article J Allergy Clin Immunol · January 2023 BACKGROUND: The asthma of some children remains poorly controlled, with recurrent exacerbations despite treatment with inhaled corticosteroids. Aside from prior exacerbations, there are currently no reliable predictors of exacerbation-prone asthma in these ... Full text Link to item Cite

Causal Graph Discovery From Self and Mutually Exciting Time Series

Journal Article IEEE Journal on Selected Areas in Information Theory · January 1, 2023 We present a generalized linear structural causal model, coupled with a novel data-adaptive linear regularization, to recover causal directed acyclic graphs (DAGs) from time series. By leveraging a recently developed stochastic monotone Variational Inequal ... Full text Cite

Bioenergetic Crisis in ICU-Acquired Weakness Gene Signatures Was Associated with Sepsis-Related Mortality: A Brief Report

Journal Article Critical Care Explorations · December 14, 2022 OBJECTIVES: To investigate the relationship between ICU-acquired weakness (ICUAW) signatures and sepsis-related mortality using gene expression from the blood within 24 hours of sepsis onset. DESIGN: Observational study using differential gene expression a ... Full text Cite

Features derived from blood pressure and intracranial pressure predict elevated intracranial pressure events in critically ill children.

Journal Article Sci Rep · December 12, 2022 Clinicians frequently observe hemodynamic changes preceding elevated intracranial pressure events. We employed a machine learning approach to identify novel and differentially expressed features associated with elevated intracranial pressure events in chil ... Full text Link to item Cite

Estimating the Prevalence of Intimate Partner Violence at an Urban Hospital Before and During the COVID-19 Pandemic Using a Novel Natural Language Processing Algorithm

Journal Article Violence and Gender · December 1, 2022 The impact of COVID-19 on intimate partner violence (IPV) in the United States is still relatively unknown, although some early data demonstrate that cases of IPV increased during COVID-19. The objective of this study was to measure the prevalence of IPV b ... Full text Cite

Functional immunophenotyping of children with critical status asthmaticus identifies differential gene expression responses in neutrophils exposed to a poly(I:C) stimulus.

Journal Article Sci Rep · November 16, 2022 The host immune response to a viral immune stimulus has not been examined in children during a life-threatening asthma attack. We determined whether we could identify clusters of children with critical asthma by functional immunophenotyping using an intrac ... Full text Link to item Cite

Pivotal challenges in artificial intelligence and machine learning applications for neonatal care.

Journal Article Semin Fetal Neonatal Med · October 2022 Clinical decision support systems (CDSS) that are developed based on artificial intelligence and machine learning (AI/ML) approaches carry transformative potentials in improving the way neonatal care is practiced. From the use of the data available from el ... Full text Link to item Cite

A Novel Technique to Identify Intimate Partner Violence in a Hospital Setting.

Journal Article West J Emerg Med · September 12, 2022 INTRODUCTION: Intimate partner violence (IPV) is defined as sexual, physical, psychological, or economic violence that occurs between current or former intimate partners. Victims of IPV may seek care for violence-related injuries in healthcare settings, wh ... Full text Link to item Cite

Neurocritical Care Performance Measures Derived from Electronic Health Record Data are Feasible and Reveal Site-Specific Variation: A CHoRUS Pilot Project.

Journal Article Neurocrit Care · August 2022 BACKGROUND: We evaluated the feasibility and discriminability of recently proposed Clinical Performance Measures for Neurocritical Care (Neurocritical Care Society) and Quality Indicators for Traumatic Brain Injury (Collaborative European NeuroTrauma Effec ... Full text Link to item Cite

A Machine Learning–Enabled Partially Observable Markov Decision Process Framework for Early Sepsis Prediction

Journal Article INFORMS Journal on Computing · July 1, 2022 Sepsis is a life-threatening condition, caused by the body’s extreme response to an infection. In the United States, 1.7 million cases of sepsis occur annually, resulting in 265,000 deaths. Delayed diagnosis and treatment are associated with higher mortali ... Full text Cite

Intimate Partner Violence at a Level-1 Trauma Center During the COVID-19 Pandemic: An Interrupted Time Series Analysis.

Journal Article Am Surg · July 2022 Risks of intimate partner violence (IPV) escalated during the COVID-19 pandemic given mitigation measures, socioeconomic hardships, and isolation concerns. The objective of this study was to explore the impact of COVID-19 on the incidence of IPV. We conduc ... Full text Link to item Cite

Automated Identification of Immunocompromised Status in Critically Ill Children.

Journal Article Methods Inf Med · May 2022 BACKGROUND: Easy identification of immunocompromised hosts (ICHs) would allow for stratification of culture results based on host type. METHODS: We utilized antimicrobial stewardship program (ASP) team notes written during handshake stewardship rounds in t ... Full text Link to item Cite

Machine Learning Approaches to Identify Discriminative Signatures of Volatile Organic Compounds (VOCs) from Bacteria and Fungi Using SPME-DART-MS

Journal Article Metabolites · March 1, 2022 Point-of-care screening tools are essential to expedite patient care and decrease reliance on slow diagnostic tools (e.g., microbial cultures) to identify pathogens and their associated antibiotic resistance. Analysis of volatile organic compounds (VOC) em ... Full text Cite

A Wearable Multimodal Sensing System for Tracking Changes in Pulmonary Fluid Status, Lung Sounds, and Respiratory Markers.

Journal Article Sensors (Basel) · February 2, 2022 Heart failure (HF) exacerbations, characterized by pulmonary congestion and breathlessness, require frequent hospitalizations, often resulting in poor outcomes. Current methods for tracking lung fluid and respiratory distress are unable to produce continuo ... Full text Link to item Cite

Coronavirus Disease 2019 Temperature Trajectories Correlate With Hyperinflammatory and Hypercoagulable Subphenotypes.

Journal Article Crit Care Med · February 1, 2022 OBJECTIVES: Body temperature trajectories of infected patients are associated with specific immune profiles and survival. We determined the association between temperature trajectories and distinct manifestations of coronavirus disease 2019. DESIGN: Retros ... Full text Link to item Cite

Improving sepsis prediction model generalization with optimal transport

Conference Proceedings of Machine Learning Research · January 1, 2022 Sepsis is a deadly condition affecting many patients in the hospital. There have been many efforts to build models that predict the onset of sepsis, but these models tend to perform terribly when validated on external data from different hospitals due to d ... Cite

UnfoldML: Cost-Aware and Uncertainty-Based Dynamic 2D Prediction for Multi-Stage Classification

Conference Advances in Neural Information Processing Systems · January 1, 2022 Machine Learning (ML) research has focused on maximizing the accuracy of predictive tasks. ML models, however, are increasingly more complex, resource intensive, and costlier to deploy in resource-constrained environments. These issues are exacerbated for ... Cite

Initial Validation of Multi-Frequency Patch-Based Impedance Pneumography in Hospital Settings

Conference Proceedings of IEEE Sensors · January 1, 2022 Heart Failure (HF) is a progressive condition that accounts for nearly one million hospitalizations per year in the United States. HF is typified by respiratory distress symptoms that are associated with increased hospitalizations and mortality. Respirator ... Full text Cite

Predicting Parkinson's Disease and Its Pathology via Simple Clinical Variables.

Journal Article J Parkinsons Dis · 2022 BACKGROUND: Parkinson's disease (PD) is a chronic, disabling neurodegenerative disorder. OBJECTIVE: To predict a future diagnosis of PD using questionnaires and simple non-invasive clinical tests. METHODS: Participants in the prospective Kuakini Honolulu-A ... Full text Link to item Cite

OnAI-Comp: An Online AI Experts Competing Framework for Early Sepsis Detection.

Journal Article IEEE/ACM Trans Comput Biol Bioinform · 2022 Sepsis is a major public concern due to its high mortality, morbidity, and financial cost. There are many existing works of early sepsis prediction using different machine learning models to mitigate the outcomes brought by sepsis. In the practical scenari ... Full text Link to item Cite

Ideal algorithms in healthcare: Explainable, dynamic, precise, autonomous, fair, and reproducible.

Journal Article PLOS Digit Health · 2022 Established guidelines describe minimum requirements for reporting algorithms in healthcare; it is equally important to objectify the characteristics of ideal algorithms that confer maximum potential benefits to patients, clinicians, and investigators. We ... Full text Link to item Cite

A deep learning approach for predicting severity of COVID-19 patients using a parsimonious set of laboratory markers

Journal Article iScience · December 17, 2021 The SARS-CoV-2 virus has caused tremendous healthcare burden worldwide. Our focus was to develop a practical and easy-to-deploy system to predict the severe manifestation of disease in patients with COVID-19 with an aim to assist clinicians in triage and t ... Full text Cite

Altered Heart Rate Variability Early in ICU Admission Differentiates Critically Ill Coronavirus Disease 2019 and All-Cause Sepsis Patients

Journal Article Critical Care Explorations · December 2, 2021 IMPORTANCE: Altered heart rate variability has been associated with autonomic dysfunction in a number of disease profiles, in this work we elucidate differences in the biomarker among patients with all-cause sepsis and coronavirus disease 2019. OBJECTIVES: ... Full text Cite

Cluster analysis and profiling of airway fluid metabolites in pediatric acute hypoxemic respiratory failure.

Journal Article Sci Rep · November 26, 2021 Hierarchal clustering of amino acid metabolites may identify a metabolic signature in children with pediatric acute hypoxemic respiratory failure. Seventy-four immunocompetent children, 41 (55.4%) with pediatric acute respiratory distress syndrome (PARDS), ... Full text Link to item Cite

Analysis of Discrepancies Between Pulse Oximetry and Arterial Oxygen Saturation Measurements by Race and Ethnicity and Association With Organ Dysfunction and Mortality.

Journal Article JAMA network open · November 2021 ImportanceDiscrepancies in oxygen saturation measured by pulse oximetry (Spo2), when compared with arterial oxygen saturation (Sao2) measured by arterial blood gas (ABG), may differentially affect patients according to race and ethnicity. However, ... Full text Cite

Deep Learning Model to Predict Serious Infection Among Children With Central Venous Lines

Journal Article Frontiers in Pediatrics · September 15, 2021 Objective: Predict the onset of presumed serious infection, defined as a positive blood culture drawn and new antibiotic course of at least 4 days (PSI*), among pediatric patients with Central Venous Lines (CVLs). Design: Retrospective cohort study. Settin ... Full text Cite

Artificial Intelligence May Predict Early Sepsis After Liver Transplantation

Journal Article Frontiers in Physiology · September 6, 2021 Background: Sepsis, post-liver transplantation, is a frequent challenge that impacts patient outcomes. We aimed to develop an artificial intelligence method to predict the onset of post-operative sepsis earlier. Methods: This pilot study aimed to identify ... Full text Cite

Associations between remote patient monitoring programme responsiveness and clinical outcomes for patients with COVID-19.

Journal Article BMJ Open Qual · September 2021 OBJECTIVE: To assess whether engagement in a COVID-19 remote patient monitoring (RPM) programme or telemedicine programme improves patient outcomes. METHODS: This is a retrospective cohort study analysing patient responsiveness to our RPM survey or telemed ... Full text Link to item Cite

Temporal Differential Expression of Physiomarkers Predicts Sepsis in Critically Ill Adults.

Journal Article Shock · July 1, 2021 BACKGROUND: Sepsis is a life-threatening condition with high mortality rates. Early detection and treatment are critical to improving outcomes. Our primary objective was to develop artificial intelligence capable of predicting sepsis earlier using a minima ... Full text Link to item Cite

Generalization in Clinical Prediction Models: The Blessing and Curse of Measurement Indicator Variables

Journal Article Critical Care Explorations · June 25, 2021 OBJECTIVE: Specific factors affecting generalizability of clinical prediction models are poorly understood. Our main objective was to investigate how measurement indicator variables affect external validity in clinical prediction models for predicting onse ... Full text Cite

Machine Learning-Based Discovery of a Gene Expression Signature in Pediatric Acute Respiratory Distress Syndrome

Journal Article Critical Care Explorations · June 15, 2021 Objectives: To identify differentially expressed genes and networks from the airway cells within 72 hours of intubation of children with and without pediatric acute respiratory distress syndrome. To test the use of a neutrophil transcription reporter assay ... Full text Cite

Prediction of Acute Respiratory Failure Requiring Advanced Respiratory Support in Advance of Interventions and Treatment: A Multivariable Prediction Model From Electronic Medical Record Data.

Journal Article Crit Care Explor · May 2021 BACKGROUND: Acute respiratory failure occurs frequently in hospitalized patients and often begins outside the ICU, associated with increased length of stay, cost, and mortality. Delays in decompensation recognition are associated with worse outcomes. OBJEC ... Full text Link to item Cite

Predicting presumed serious infection among hospitalized children on central venous lines with machine learning.

Journal Article Comput Biol Med · May 2021 BACKGROUND: Presumed serious infection (PSI) is defined as a blood culture drawn and new antibiotic course of at least 4 days among pediatric patients with Central Venous Lines (CVLs). Early PSI prediction and use of medical interventions can prevent adver ... Full text Link to item Cite

HeMA: A hierarchically enriched machine learning approach for managing false alarms in real time: A sepsis prediction case study.

Journal Article Comput Biol Med · April 2021 Early detection of sepsis can be life-saving. Machine learning models have shown great promise in early sepsis prediction when applied to patient physiological data in real-time. However, these existing models often under-perform in terms of positive predi ... Full text Link to item Cite

The National COVID Cohort Collaborative (N3C): Rationale, design, infrastructure, and deployment.

Journal Article J Am Med Inform Assoc · March 1, 2021 OBJECTIVE: Coronavirus disease 2019 (COVID-19) poses societal challenges that require expeditious data and knowledge sharing. Though organizational clinical data are abundant, these are largely inaccessible to outside researchers. Statistical, machine lear ... Full text Link to item Cite

Detectroops - A Street Mascarar

Conference Proceedings of the 2021 4th International Conference on Computing and Communications Technologies, ICCCT 2021 · January 1, 2021 The proposed Detectroops system would promote public safety by measuring a person's temperature and assessing whether or not they are wearing a mask in public settings. The person will be able to enter public space after sterilising himself and meeting the ... Full text Cite

Predicting Same Hospital Readmission following Fontan Cavopulmonary Anastomosis using Machine Learning

Conference Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021 · January 1, 2021 Hospital readmission after third-stage palliation for single ventricle physiology (Fontan cavopulmonary anastomosis) approaches 25%. The cause for readmissions is varied, and there is no clear way to predict those at risk for readmission. If an effective p ... Full text Cite

eARDS: A multi-center validation of an interpretable machine learning algorithm of early onset Acute Respiratory Distress Syndrome (ARDS) among critically ill adults with COVID-19.

Journal Article PLoS One · 2021 We present an interpretable machine learning algorithm called 'eARDS' for predicting ARDS in an ICU population comprising COVID-19 patients, up to 12-hours before satisfying the Berlin clinical criteria. The analysis was conducted on data collected from th ... Full text Link to item Cite

Machine Learning Identifies Complicated Sepsis Course and Subsequent Mortality Based on 20 Genes in Peripheral Blood Immune Cells at 24 H Post-ICU Admission.

Journal Article Front Immunol · 2021 A complicated clinical course for critically ill patients admitted to the intensive care unit (ICU) usually includes multiorgan dysfunction and subsequent death. Owing to the heterogeneity, complexity, and unpredictability of the disease progression, ICU p ... Full text Link to item Cite

Body Temperature Trajectory Associated with Venous Thromboembolism in COVID-19 Patients

Conference AMERICAN JOURNAL OF RESPIRATORY AND CRITICAL CARE MEDICINE · 2021 Cite

Machine Learning Methods to Predict Acute Respiratory Failure and Acute Respiratory Distress Syndrome

Journal Article Frontiers in Big Data · November 23, 2020 Acute respiratory failure (ARF) is a common problem in medicine that utilizes significant healthcare resources and is associated with high morbidity and mortality. Classification of acute respiratory failure is complicated, and it is often determined by th ... Full text Cite

Electrocardiographic changes predate Parkinson's disease onset.

Journal Article Sci Rep · July 9, 2020 Autonomic nervous system involvement precedes the motor features of Parkinson's disease (PD). Our goal was to develop a proof-of-concept model for identifying subjects at high risk of developing PD by analysis of cardiac electrical activity. We used standa ... Full text Link to item Cite

Comparative analysis between convolutional neural network learned and engineered features: A case study on cardiac arrhythmia detection

Journal Article Cardiovascular Digital Health Journal · July 1, 2020 Background: Atrial fibrillation (AF) is one of the most common cardiovascular problems, and its asymptomatic tendency makes AF detection challenging. Machine and deep learning methods are commonly used in AF detection. Objective: The purpose of this study ... Full text Cite

Using Machine Learning to Predict Early Onset Acute Organ Failure in Critically Ill Intensive Care Unit Patients With Sickle Cell Disease: Retrospective Study.

Journal Article J Med Internet Res · May 13, 2020 BACKGROUND: Sickle cell disease (SCD) is a genetic disorder of the red blood cells, resulting in multiple acute and chronic complications, including pain episodes, stroke, and kidney disease. Patients with SCD develop chronic organ dysfunction, which may p ... Full text Link to item Cite

Big Data in the Assessment of Pediatric Medication Safety.

Journal Article Pediatrics · February 2020 Big data (BD) in pediatric medication safety research provides many opportunities to improve the safety and health of children. The number of pediatric medication and device trials has increased in part because of the past 20 years of US legislation requir ... Full text Link to item Cite

Predicting Volume Responsiveness Among Sepsis Patients Using Clinical Data and Continuous Physiological Waveforms.

Journal Article AMIA Annu Symp Proc · 2020 The efficacy of early fluid treatment in patients with sepsis is unclear and may contribute to serious adverse events due to fluid non-responsiveness. The current method of deciding if patients are responsive to fluid administration is often subjective and ... Link to item Cite

Differential gene expression analysis reveals novel genes and pathways in pediatric septic shock patients.

Journal Article Sci Rep · August 2, 2019 Septic shock is a devastating health condition caused by uncontrolled sepsis. Advancements in high-throughput sequencing techniques have increased the number of potential genetic biomarkers under review. Multiple genetic markers and functional pathways pla ... Full text Link to item Cite

A Cost-Benefit Analysis of Automated Physiological Data Acquisition Systems Using Data-Driven Modeling

Journal Article Journal of Healthcare Informatics Research · June 15, 2019 Precision medicine and the continuous analysis of “Big data” promises to improve patient outcomes dramatically in the near future. Very recently, healthcare facilities have started to explore automatic collection of patient-specific physiological data with ... Full text Cite

Correction to: A Cost-Benefit Analysis of Automated Physiological Data Acquisition Systems Using Data-Driven Modeling (Journal of Healthcare Informatics Research, (2019), 3, 2, (245-263), 10.1007/s41666-018-0040-y)

Journal Article Journal of Healthcare Informatics Research · June 15, 2019 In the original version of this article, the incorrect version of Fig. 3 was published. Following is the correct figure. The publisher regrets the error: (Figure presented.). ... Full text Cite

Improving Prediction Performance Using Hierarchical Analysis of Real-Time Data: A Sepsis Case Study.

Journal Article IEEE J Biomed Health Inform · May 2019 This paper presents a novel method for hierarchical analysis of machine learning algorithms to improve predictions of at risk patients, thus further enabling prompt therapy. Specifically, we develop a multi-layer machine learning approach to analyze contin ... Full text Link to item Cite

A minimal set of physiomarkers in continuous high frequency data streams predict adult sepsis onset earlier.

Journal Article Int J Med Inform · February 2019 PURPOSE: Sepsis is a life-threatening condition with high mortality rates and expensive treatment costs. To improve short- and long-term outcomes, it is critical to detect at-risk sepsis patients at an early stage. METHODS: A data-set consisting of high-fr ... Full text Link to item Cite

Health intelligence

Chapter · January 1, 2019 Artificial intelligence (AI) enables machines to extract, integrate, exchange, and analyze large heterogeneous datasets to answer complex problems in a timely manner. The promise of AI in healthcare and medicine has been embraced by many computer scientist ... Full text Cite

PhysOnline: An Open Source Machine Learning Pipeline for Real-Time Analysis of Streaming Physiological Waveform.

Journal Article IEEE J Biomed Health Inform · January 2019 Real-time analysis of streaming physiological data to identify earlier abnormal conditions is an important aspect of precision medicine. However, open-source systems supporting this workflow are lacking. In this paper, we present PhysOnline, a pipeline bui ... Full text Link to item Cite

Applying Artificial Intelligence to Identify Physiomarkers Predicting Severe Sepsis in the PICU.

Journal Article Pediatr Crit Care Med · October 2018 OBJECTIVES: We used artificial intelligence to develop a novel algorithm using physiomarkers to predict the onset of severe sepsis in critically ill children. DESIGN: Observational cohort study. SETTING: PICU. PATIENTS: Children age between 6 and 18 years ... Full text Link to item Cite

A hybrid feature extraction method to detect Atrial Fibrillation from single lead ECG recording

Conference 2018 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2018 · April 6, 2018 Identifying patients with Atrial Fibrillation (AFib) is one of the most challenging and prevailing problems in cardiology. In this study, we propose a novel feature extraction method hybridizing probabilistic symbolic pattern recognition (PSPR) and Sample ... Full text Cite

A robust deep convolutional neural network for the classification of abnormal cardiac rhythm using single lead electrocardiograms of variable length.

Journal Article Physiol Meas · March 27, 2018 OBJECTIVE: Atrial fibrillation (AF) is a major cause of hospitalization and death in the United States. Moreover, as the average age of individuals increases around the world, early detection and diagnosis of AF become even more pressing. In this paper, we ... Full text Link to item Cite

How much data should we collect? A case study in sepsis detection using deep learning

Conference 2017 IEEE Healthcare Innovations and Point of Care Technologies, HI-POCT 2017 · December 19, 2017 Sepsis is an acute, life-threatening condition that results from bacterial infections, often acquired in the hospital. Undetected, sepsis can progress to severe sepsis and septic shock, with a risk of death as high as 30% to 80%. Early detection of sepsis ... Full text Cite

Cardiac rhythm classification from a short single lead ECG recording via random forest

Conference Computing in Cardiology · January 1, 2017 Detection of atrial fibrillation (AF) from electrocardiogram (ECG) recordings is one of the prevailing challenges in the field of cardiac computing. The task of the PhysioNet/Computing in Cardiology 2017 challenge is to distinguish the AF rhythms from non- ... Full text Cite

Effects of varying sampling frequency on the analysis of continuous ECG data streams

Conference Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) · January 1, 2017 A myriad of data is produced in intensive care units (ICU) even for short periods of time. This data is frequently used for monitoring patient’s immediate health status, not for real-time analysis because of technical challenges in real-time processing of ... Full text Cite

CoRAD: Visual Analytics for Cohort Analysis

Conference Proceedings - 2016 IEEE International Conference on Healthcare Informatics, ICHI 2016 · December 6, 2016 In this paper, we introduce a novel dynamic visual analytic tool called the Cohort Relative Aligned Dashboard (CoRAD). We present the design components of CoRAD, along with alternatives that lead to the final instantiation. We also present an evaluation in ... Full text Cite

A Review of Visual Representations of Physiologic Data.

Journal Article JMIR Med Inform · November 21, 2016 BACKGROUND: Physiological data is derived from electrodes attached directly to patients. Modern patient monitors are capable of sampling data at frequencies in the range of several million bits every hour. Hence the potential for cognitive threat arising f ... Full text Link to item Cite

PhysioEx: Visual Analysis of Physiological Event Streams

Journal Article Computer Graphics Forum · June 1, 2016 In this work, we introduce a novel visualization technique, the Temporal Intensity Map, which visually integrates data values over time to reveal the frequency, duration, and timing of significant features in streaming data. We combine the Temporal Intensi ... Full text Cite

Collaborative multi-touch clinical handover system for the neonatal intensive care unit

Journal Article Electronic Journal of Health Informatics · January 1, 2015 Background: A critically ill infant admitted to a neonatal intensive care unit requires complex, critical, and coordinated care performed by multidisciplinary healthcare teams. Since the infant's care is not provided by a single, individual physician durin ... Cite

Environmental factors in an Ontario community with disparities in colorectal cancer incidence.

Journal Article Glob J Health Sci · March 24, 2014 OBJECTIVE: In Ontario, there are significant geographical disparities in colorectal cancer incidence. In particular, the northern region of Timiskaming has the highest incidence of colorectal cancer in Ontario while the southern region of Peel displays the ... Full text Link to item Cite

A real-time multi-dimensional visualization framework for critical and complex environments

Conference Proceedings - IEEE Symposium on Computer-Based Medical Systems · January 1, 2014 The critical care environment is a complex and critical environment, containing numerous body sensors attached to critically ill patients and producing continuous streams of physiological data. In conjunction, human-generated clinical data are produced by ... Full text Cite

Cloud framework for real-time synchronous physiological streams to support rural and remote critical care

Conference Proceedings of CBMS 2013 - 26th IEEE International Symposium on Computer-Based Medical Systems · December 9, 2013 We present a method for transmission and processing of real-time trans-continental medical data streams. We apply fundamentals of existing network technologies to create a secure tunnel from a remote hospital through an open-network to the Artemis Cloud. W ... Full text Cite

Abstract 4819: Assessing the environmental factors in two Ontario communities with diverging colorectal cancer incidence rates .

Conference Cancer Research · April 15, 2013 AbstractColorectal cancer is the third most diagnosed cancer and second leading cause of cancer related deaths in Canada. As Ontario has the largest population in Canada, it also has great disparities in col ... Full text Cite

Cloud framework for real-time synchronous physiological streams to support rural and remote Critical Care

Conference Proceedings - IEEE Symposium on Computer-Based Medical Systems · January 1, 2013 We present a method for transmission and processing of real-time trans-continental medical data streams. We apply fundamentals of existing network technologies to create a secure tunnel from a remote hospital through an open-network to the Artemis Cloud. W ... Cite

CBPSP: Complex business processes for stream processing

Conference 2012 25th IEEE Canadian Conference on Electrical and Computer Engineering: Vision for a Greener Future, CCECE 2012 · December 7, 2012 This paper presents an extension called the Complex Business Processes for Stream Processing (CBPsp) to the Solution Manager Service (SMS) framework to support the definition and enactment of complex business processes for event stream processing. The crit ... Full text Cite

A novel framework for event stream processing of clinical practice guidelines

Conference Proceedings - IEEE-EMBS International Conference on Biomedical and Health Informatics: Global Grand Challenge of Health Informatics, BHI 2012 · July 30, 2012 Clinical Decision Support Systems (CDSSs) play important roles aiding in patient care; they provide accurate data analysis and timely evidence-informed recommendations. Although the availability of biomedical data continues to flourish, there have been lim ... Full text Cite

Integrating complex business processes for knowledge-driven clinical decision support systems.

Conference Annu Int Conf IEEE Eng Med Biol Soc · 2012 This paper presents in detail the component of the Complex Business Process for Stream Processing framework that is responsible for integrating complex business processes to enable knowledge-driven Clinical Decision Support System (CDSS) recommendations. C ... Full text Link to item Cite

CBPSP: COMPLEX BUSINESS PROCESSES FOR STREAM PROCESSING

Conference 2012 25TH IEEE CANADIAN CONFERENCE ON ELECTRICAL & COMPUTER ENGINEERING (CCECE) · 2012 Cite

A method for interactive hypothesis testing for clinical decision support systems using Ptolemy II

Conference Canadian Conference on Electrical and Computer Engineering · October 17, 2011 This paper introduces a method for interactive knowledge testing for a clinical decision support system developed as a part of the Artemis Project. Knowledge within the medical domain is vast and continuously being defined and re-defined. The volume of mod ... Full text Cite

A method for interactive hypothesis testing for Clinical Decision Support Systems using Ptolemy II

Conference 2011 24TH CANADIAN CONFERENCE ON ELECTRICAL AND COMPUTER ENGINEERING (CCECE) · 2011 Cite

A framework for nursing documentation enabling integration with HER and real-time patient monitoring

Conference Proceedings - IEEE Symposium on Computer-Based Medical Systems · December 1, 2010 This paper proposes a framework for mobile nursing documentation enabling the integration of clinical intervention data with both electronic health record systems and real-time intelligent decision support systems for patient monitoring. A brief discussion ... Full text Cite

On the integration of an artifact system and a real-time healthcare analytics system

Conference IHI'10 - Proceedings of the 1st ACM International Health Informatics Symposium · December 1, 2010 As a result of advances in software technology, particularly stream computing, it is now possible to implement scalable systems capable of real-time analysis of multiple physiological data streams of multiple patients. There is a growing body of evidence s ... Full text Cite

A method for clinical and physiological event stream processing.

Conference Annu Int Conf IEEE Eng Med Biol Soc · 2010 This paper proposes a methodology for the event stream processing of synchronous (physiological) and asynchronous (clinical) health data streams. The purpose is to illustrate the feasibility of Artemis, our extension of IBM's InfoSphere Streams, to appropr ... Full text Link to item Cite

Service oriented architecture for the integration of clinical and physiological data for real-time event stream processing.

Conference Annu Int Conf IEEE Eng Med Biol Soc · 2009 This paper proposes a framework for the integration of physiological and clinical health data within a Service-Oriented architecture framework. This integration will subsequently be used in real-time event stream processing in intelligent patient monitorin ... Full text Link to item Cite