Natural Language Processing

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  Subject Areas on Research

  • A case for developing domain-specific vocabularies for extracting suicide factors from healthcare notes. 
  • Annotation of phenotypes using ontologies: a gold standard for the training and evaluation of natural language processing systems. 
  • Association Between Patient Survival and Clinician Variability in Treatment Rates for Aortic Valve Stenosis. 
  • Automated problem list generation and physicians perspective from a pilot study. 
  • Building gold standard corpora for medical natural language processing tasks. 
  • COVID-19 SignSym: a fast adaptation of a general clinical NLP tool to identify and normalize COVID-19 signs and symptoms to OMOP common data model. 
  • Classifying Pseudogout Using Machine Learning Approaches With Electronic Health Record Data. 
  • ClinPhen extracts and prioritizes patient phenotypes directly from medical records to expedite genetic disease diagnosis. 
  • Combining natural language processing and network analysis to examine how advocacy organizations stimulate conversation on social media. 
  • Context-Based Identification of Muscle Invasion Status in Patients With Bladder Cancer Using Natural Language Processing. 
  • DIG--a system for gene annotation and functional discovery. 
  • Deep Learning to Classify Radiology Free-Text Reports. 
  • Design, implementation, and evaluation of a computerized system to communicate with patients with limited native language proficiency in the perioperative period. 
  • Development and Validation of a Natural Language Processing Tool to Generate the CONSORT Reporting Checklist for Randomized Clinical Trials. 
  • Effects of age and sex on the distribution and symmetry of lumbar spinal and neural foraminal stenosis: a natural language processing analysis of 43,255 lumbar MRI reports. 
  • Evaluation of a chief complaint pre-processor for biosurveillance. 
  • Evaluation of preprocessing techniques for chief complaint classification. 
  • Forty-two Million Ways to Describe Pain: Topic Modeling of 200,000 PubMed Pain-Related Abstracts Using Natural Language Processing and Deep Learning-Based Text Generation. 
  • High-throughput multimodal automated phenotyping (MAP) with application to PheWAS. 
  • High-throughput phenotyping with electronic medical record data using a common semi-supervised approach (PheCAP). 
  • Identification of Patients With Metastatic Prostate Cancer With Natural Language Processing and Machine Learning. 
  • Identifying lupus patients in electronic health records: Development and validation of machine learning algorithms and application of rule-based algorithms. 
  • Incorporating natural language processing to improve classification of axial spondyloarthritis using electronic health records. 
  • Large-scale evaluation of automated clinical note de-identification and its impact on information extraction. 
  • Large-scale identification of patients with cerebral aneurysms using natural language processing. 
  • Measuring Exposure to Incarceration Using the Electronic Health Record. 
  • Mining biomedical data using MetaMap Transfer (MMtx) and the Unified Medical Language System (UMLS). 
  • Moving Away From Error-Related Potentials to Achieve Spelling Correction in P300 Spellers. 
  • Moving the mountain: analysis of the effort required to transform comparative anatomy into computable anatomy. 
  • Multi-ancestry genome- and phenome-wide association studies of diverticular disease in electronic health records with natural language processing enriched phenotyping algorithm. 
  • Natural language processing and entrustable professional activity text feedback in surgery: A machine learning model of resident autonomy. 
  • NeuroBlu, an electronic health record (EHR) trusted research environment (TRE) to support mental healthcare analytics with real-world data. 
  • New Frontiers of Natural Language Processing in Surgery. 
  • Patient clustering with uncoded text in electronic medical records. 
  • Performance of a Machine Learning Classifier of Knee MRI Reports in Two Large Academic Radiology Practices: A Tool to Estimate Diagnostic Yield. 
  • Scalable relevance ranking algorithm via semantic similarity assessment improves efficiency of medical chart review. 
  • Symptom-based patient stratification in mental illness using clinical notes. 
  • Systematic review of current natural language processing methods and applications in cardiology. 
  • Tracking financing for global common goods for health: A machine learning approach using natural language processing techniques. 
  • Use of Natural Language Processing to Improve Identification of Patients With Peripheral Artery Disease. 
  • Using nurses' natural language entries to build a concept-oriented terminology for patients' chief complaints in the emergency department. 
  • Utilizing a language model to improve online dynamic data collection in P300 spellers. 
  • Visual annotation of the gene database. 
  • sureLDA: A multidisease automated phenotyping method for the electronic health record. 
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  Keywords of People

  • Wiseman, Samuel Joshua, Assistant Professor of Computer Science, Computer Science