Rishi Kamaleswaran

Preprint · 2023 ABSTRACTBackground 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 regre ... Full text Cite

ALRt: An Active Learning Framework for Irregularly Sampled Temporal Data

Preprint · December 12, 2022 Link to item Cite

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

Preprint · September 9, 2022 Link to item Cite

A Machine Learning Algorithm to Predict Hypoxic Respiratory Failure and risk of Acute Respiratory Distress Syndrome (ARDS) by Utilizing Features Derived from Electrocardiogram (ECG) and Routinely Clinical Data

Preprint · 2022 Full text Cite

Causal Graph Discovery from Self and Mutually Exciting Time Series

Preprint · June 4, 2021 Link to item Cite

Autonomic dysfunction characterized by Heart Rate Variability among patients with Sepsis-related Acute Respiratory Failure

Preprint · 2021 Full text Cite

Machine learning predicts early onset of fever from continuous physiological data of critically ill patients

Preprint · September 14, 2020 Link to item Cite

Changes in non-linear and time-domain heart rate variability indices between critically ill COVID-19 and all-cause sepsis patients – a retrospective study

Preprint · 2020 Full text Cite

Machine Learning Identifies Complicated Sepsis Course and Subsequent Mortality Based on 20 Genes in Peripheral Blood Immune Cells at 24 Hours post ICU admission

Preprint · 2020 Full text Cite

AutoTriage - An Open Source Edge Computing Raspberry Pi-based Clinical Screening System

Preprint · 2020 With the recent COVID-19 pandemic, healthcare systems all over the world are struggling to manage the massive increase in emergency department (ED) visits. This has put an enormous demand on medical professionals. Increased wait times in the ED increases t ... 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 (Preprint)

Preprint · May 13, 2019 Full text Cite

Rishi Kamaleswaran

Scholarly Works - Preprints

NeuroSep-CP-LCB: A Deep Learning-based Contextual Multi-armed Bandit Algorithm with Uncertainty Quantification for Early Sepsis Prediction

Sepsyn-OLCP: An Online Learning-based Framework for Early Sepsis Prediction with Uncertainty Quantification using Conformal Prediction

Unsupervised machine learning analysis to identify patterns of ICU medication use for fluid overload prediction

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

Augmenting mortality prediction with medication data and machine learning models

Robust Meta-Model for Predicting the Need for Blood Transfusion in Non-traumatic ICU Patients

Lung Grounded-SAM (LuGSAM): A Novel Framework for Integrating Text prompts to Segment Anything Model (SAM) for Segmentation Tasks of ICU Chest X-Rays

Lung Grounded-SAM (LuGSAM): A Novel Framework for Integrating Text prompts to Segment Anything Model (SAM) for Segmentation Tasks of ICU Chest X-Rays

Improving Clinical Decision Support through Interpretable Machine Learning and Error Handling in Electronic Health Records

Meta-learning in healthcare: A survey

Transfer Learning for Causal Effect Estimation

Lung Segment Anything Model (LuSAM): A Prompt-integrated Framework for Automated Lung Segmentation on ICU Chest X-Ray Images

Lung Segment Anything Model (LuSAM): A Prompt-integrated Framework for Automated Lung Segmentation on ICU Chest X-Ray Images

Causal Graph Discovery from Self and Mutually Exciting Time Series

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

ALRt: An Active Learning Framework for Irregularly Sampled Temporal Data

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

A Machine Learning Algorithm to Predict Hypoxic Respiratory Failure and risk of Acute Respiratory Distress Syndrome (ARDS) by Utilizing Features Derived from Electrocardiogram (ECG) and Routinely Clinical Data

Causal Graph Discovery from Self and Mutually Exciting Time Series

Autonomic dysfunction characterized by Heart Rate Variability among patients with Sepsis-related Acute Respiratory Failure

Machine learning predicts early onset of fever from continuous physiological data of critically ill patients

Changes in non-linear and time-domain heart rate variability indices between critically ill COVID-19 and all-cause sepsis patients – a retrospective study

Machine Learning Identifies Complicated Sepsis Course and Subsequent Mortality Based on 20 Genes in Peripheral Blood Immune Cells at 24 Hours post ICU admission

AutoTriage - An Open Source Edge Computing Raspberry Pi-based Clinical Screening System

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

Machine learning predicts early-onset acute organ failure in critically ill patients with sickle cell disease

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

Physiomarkers in Real-Time Physiological Data Streams Predict Adult Sepsis Onset Earlier Than Clinical Practice