AI Medical Compendium Topic:
Electronic Health Records

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Patient Cohort Retrieval using Transformer Language Models.

AMIA ... Annual Symposium proceedings. AMIA Symposium Jan 25, 2021
We apply deep learning-based language models to the task of patient cohort retrieval (CR) with the aim to assess their efficacy. The task ofCR requires the extraction of relevant documents from the electronic health records (EHRs) on the basis of a g...
Cohort Studies Natural Language Processing Information Storage and Retrieval Humans Electronic Health Records Language Deep Learning
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EffiCare: Better Prognostic Models via Resource-Efficient Health Embeddings.

AMIA ... Annual Symposium proceedings. AMIA Symposium Jan 25, 2021
Recent medical prognostic models adapted from high data-resource fields like language processing have quickly grown in complexity and size. However, since medical data typically constitute low data-resource settings, performances on tasks like clinic...
Humans Machine Learning Prognosis Data Mining Intensive Care Units Information Storage and Retrieval Electronic Health Records Clinical Decision Rules Benchmarking
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User-Centered Design of a Machine Learning Intervention for Suicide Risk Prediction in a Military Setting.

AMIA ... Annual Symposium proceedings. AMIA Symposium Jan 25, 2021
Primary care represents a major opportunity for suicide prevention in the military. Significant advances have been made in using electronic health record data to predict suicide attempts in patient populations. With a user-centered design approach, w...
Suicide, Attempted Electronic Health Records Suicide Prevention Humans Military Personnel User-Centered Design Predictive Value of Tests Risk Factors Risk Assessment Machine Learning
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Multi-task Learning via Adaptation to Similar Tasks for Mortality Prediction of Diverse Rare Diseases.

AMIA ... Annual Symposium proceedings. AMIA Symposium Jan 25, 2021
The mortality prediction of diverse rare diseases using electronic health record (EHR) data is a crucial task for intelligent healthcare. However, data insufficiency and the clinical diversity of rare diseases make it hard for deep learning models to...
Rare Diseases Humans Electronic Health Records Deep Learning
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Improving the Utility of Tobacco-Related Problem List Entries Using Natural Language Processing.

AMIA ... Annual Symposium proceedings. AMIA Symposium Jan 25, 2021
We present findings on using natural language processing to classify tobacco-related entries from problem lists found within patient's electronic health records. Problem lists describe health-related issues recorded during a patient's medical visit; ...
Tobacco Use Humans Natural Language Processing Electronic Health Records Tobacco Products Medical Records, Problem-Oriented
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Extracting Angina Symptoms from Clinical Notes Using Pre-Trained Transformer Architectures.

AMIA ... Annual Symposium proceedings. AMIA Symposium Jan 25, 2021
Anginal symptoms can connote increased cardiac risk and a need for change in cardiovascular management. In this study, a pre-trained transformer architecture was used to automatically detect and characterize anginal symptoms from within the history o...
Angina Pectoris Primary Health Care Natural Language Processing Chest Pain Data Collection Documentation Physicians, Primary Care Male Middle Aged Computer Systems Humans Electronic Health Records
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Catch Me if You Can: Acute Events Hidden in Structured Chronic Disease Diagnosis Descriptions Show Detectable Recording Patterns in EHR.

AMIA ... Annual Symposium proceedings. AMIA Symposium Jan 25, 2021
Our previous research shows that structured cancer DX description data accuracy varied across electronic health record (EHR) segments (e.g. encounter DX, problem list, etc.). We provide initial evidence corroborating these findings in EHRs from patie...
Electronic Health Records Diabetes Mellitus Models, Statistical Humans Machine Learning Medical Informatics Odds Ratio Chronic Disease Glycated Hemoglobin Datasets as Topic
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Application of Machine Learning to Predict Patient No-Shows in an Academic Pediatric Ophthalmology Clinic.

AMIA ... Annual Symposium proceedings. AMIA Symposium Jan 25, 2021
Patient "no-shows" are missed appointments resulting in clinical inefficiencies, revenue loss, and discontinuity of care. Using secondary electronic health record (EHR) data, we used machine learning to predict patient no-shows in follow-up and new p...
ROC Curve Ophthalmology Humans No-Show Patients Machine Learning Appointments and Schedules Ambulatory Care Facilities Office Visits Electronic Health Records Efficiency, Organizational Child Academic Medical Centers
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Selection of Clinical Text Features for Classifying Suicide Attempts.

AMIA ... Annual Symposium proceedings. AMIA Symposium Jan 25, 2021
Research has demonstrated cohort misclassification when studies of suicidal thoughts and behaviors (STBs) rely on ICD-9/10-CM diagnosis codes. Electronic health record (EHR) data are being explored to better identify patients, a process called EHR ph...
Humans International Classification of Diseases Prevalence Suicide, Attempted Cohort Studies ROC Curve Natural Language Processing Data Mining Machine Learning Electronic Health Records Algorithms Female Phenotype Male
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Deep Learning Approach to Parse Eligibility Criteria in Dietary Supplements Clinical Trials Following OMOP Common Data Model.

AMIA ... Annual Symposium proceedings. AMIA Symposium Jan 25, 2021
Dietary supplements (DSs) have been widely used in the U.S. and evaluated in clinical trials as potential interventions for various diseases. However, many clinical trials face challenges in recruiting enough eligible patients in a timely fashion, ca...
Electronic Health Records Deep Learning Models, Theoretical Humans Databases, Factual Patient Selection Clinical Trials as Topic Partnership Practice Dietary Supplements Natural Language Processing Feasibility Studies
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