AIMC Topic: Electronic Health Records

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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; ...
Medical Records, Problem-Oriented Humans Tobacco Use Electronic Health Records Natural Language Processing Tobacco Products
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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 Data Collection Documentation Physicians, Primary Care Humans Electronic Health Records Computer Systems Natural Language Processing Male Primary Health Care Chest Pain Middle Aged
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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 Datasets as Topic Medical Informatics Chronic Disease Glycated Hemoglobin Humans Odds Ratio Machine Learning
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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...
Appointments and Schedules Child Humans Ambulatory Care Facilities ROC Curve Efficiency, Organizational Machine Learning Ophthalmology Electronic Health Records Academic Medical Centers No-Show Patients Office Visits
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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...
International Classification of Diseases Male Prevalence Suicide, Attempted Natural Language Processing Cohort Studies Data Mining Algorithms ROC Curve Phenotype Humans Electronic Health Records Machine Learning Female
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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...
Dietary Supplements Natural Language Processing Feasibility Studies Partnership Practice Patient Selection Clinical Trials as Topic Electronic Health Records Deep Learning Models, Theoretical Humans Databases, Factual
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Contextual Embeddings from Clinical Notes Improves Prediction of Sepsis.

AMIA ... Annual Symposium proceedings. AMIA Symposium Jan 25, 2021
Sepsis, a life-threatening organ dysfunction, is a clinical syndrome triggered by acute infection and affects over 1 million Americans every year. Untreated sepsis can progress to septic shock and organ failure, making sepsis one of the leading cause...
Severity of Illness Index Deep Learning Sepsis Algorithms Electronic Health Records Language Clinical Decision Rules Decision Support Systems, Clinical Shock, Septic Natural Language Processing Humans
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Logistic regression and machine learning predicted patient mortality from large sets of diagnosis codes comparably.

Journal of clinical epidemiology Jan 22, 2021
OBJECTIVE: The objective of the study was to compare the performance of logistic regression and boosted trees for predicting patient mortality from large sets of diagnosis codes in electronic healthcare records.
Humans Machine Learning Forecasting International Classification of Diseases Mortality Aged, 80 and over Middle Aged Colorectal Neoplasms Electronic Health Records England Male Age Factors Female Logistic Models Sex Factors Myocardial Infarction Aged Hip Fractures
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Secure and Robust Machine Learning for Healthcare: A Survey.

IEEE reviews in biomedical engineering Jan 22, 2021
Recent years have witnessed widespread adoption of machine learning (ML)/deep learning (DL) techniques due to their superior performance for a variety of healthcare applications ranging from the prediction of cardiac arrest from one-dimensional heart...
Machine Learning Electronic Health Records Diagnosis, Computer-Assisted Confidentiality Humans
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Machine Learning for Clinical Outcome Prediction.

IEEE reviews in biomedical engineering Jan 22, 2021
Clinical decision-making in healthcare is already being influenced by predictions or recommendations made by data-driven machines. Numerous machine learning applications have appeared in the latest clinical literature, especially for outcome predicti...
Electronic Health Records Big Data Treatment Outcome Decision Making, Computer-Assisted Humans Machine Learning Decision Support Techniques
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