AIMC Topic: Electronic Health Records

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Graphical Presentations of Clinical Data in a Learning Electronic Medical Record.

Applied clinical informatics Oct 14, 2020
BACKGROUND: Complex electronic medical records (EMRs) presenting large amounts of data create risks of cognitive overload. We are designing a Learning EMR (LEMR) system that utilizes models of intensive care unit (ICU) physicians' data access pattern...
Computer Graphics Electronic Health Records Humans User-Computer Interface Machine Learning Medical Informatics Intensive Care Units
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Use of Natural Language Processing to Improve Identification of Patients With Peripheral Artery Disease.

Circulation. Cardiovascular interventions Oct 12, 2020
BACKGROUND: Peripheral artery disease (PAD) is underrecognized, undertreated, and understudied: each of these endeavors requires efficient and accurate identification of patients with PAD. Currently, PAD patient identification relies on diagnosis/pro...
Natural Language Processing Data Mining Ankle Brachial Index Diagnosis, Computer-Assisted Amputation, Surgical Electronic Health Records Peripheral Arterial Disease Humans Endovascular Procedures Aged, 80 and over Female Reproducibility of Results Vascular Surgical Procedures Predictive Value of Tests Male Aged Middle Aged
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Personalized treatment for coronary artery disease patients: a machine learning approach.

Health care management science Oct 10, 2020
Current clinical practice guidelines for managing Coronary Artery Disease (CAD) account for general cardiovascular risk factors. However, they do not present a framework that considers personalized patient-specific characteristics. Using the electron...
Humans Coronary Artery Disease Electronic Health Records Ethnicity Female Machine Learning Male Algorithms Precision Medicine
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Utilization of Deep Learning for Subphenotype Identification in Sepsis-Associated Acute Kidney Injury.

Clinical journal of the American Society of Nephrology : CJASN Oct 8, 2020
BACKGROUND AND OBJECTIVES: Sepsis-associated AKI is a heterogeneous clinical entity. We aimed to agnostically identify sepsis-associated AKI subphenotypes using deep learning on routinely collected data in electronic health records.
Liver Diseases Blood Urea Nitrogen Leukocyte Count Alanine Transaminase Bilirubin Lactic Acid Simplified Acute Physiology Score Glutamyl Aminopeptidase Deep Learning Sepsis Humans Renal Dialysis United States Prognosis Creatinine Acute Kidney Injury L-Lactate Dehydrogenase Phenotype Male Databases, Factual Electronic Health Records Middle Aged Female Comorbidity Aged
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DDxNet: a deep learning model for automatic interpretation of electronic health records, electrocardiograms and electroencephalograms.

Scientific reports Oct 2, 2020
Effective patient care mandates rapid, yet accurate, diagnosis. With the abundance of non-invasive diagnostic measurements and electronic health records (EHR), manual interpretation for differential diagnosis has become time-consuming and challenging...
Electroencephalography Humans Patient Care Algorithms Deep Learning Electronic Health Records Machine Learning Models, Theoretical Software Electrocardiography
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Prediction of incident myocardial infarction using machine learning applied to harmonized electronic health record data.

BMC medical informatics and decision making Oct 2, 2020
BACKGROUND: With cardiovascular disease increasing, substantial research has focused on the development of prediction tools. We compare deep learning and machine learning models to a baseline logistic regression using only 'known' risk factors in pre...
Humans Case-Control Studies Incidence Predictive Value of Tests Myocardial Infarction Machine Learning Electronic Health Records Female
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The Utility of Clinical Notes for Sexual Minority Health Research.

American journal of preventive medicine Oct 1, 2020
INTRODUCTION: Despite improvements in electronic medical record capability to collect data on sexual orientation, not all healthcare systems have adopted this practice. This can limit the usability of systemwide electronic medical record data for sex...
Electronic Health Records Female Male Natural Language Processing Humans Sexual Behavior Documentation Minority Health
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A deep learning-based, unsupervised method to impute missing values in electronic health records for improved patient management.

Journal of biomedical informatics Oct 1, 2020
Electronic health records (EHRs) often suffer missing values, for which recent advances in deep learning offer a promising remedy. We develop a deep learning-based, unsupervised method to impute missing values in patient records, then examine its imp...
Deep Learning Data Accuracy Humans Research Design Electronic Health Records
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Korean clinical entity recognition from diagnosis text using BERT.

BMC medical informatics and decision making Sep 30, 2020
BACKGROUND: While clinical entity recognition mostly aims at electronic health records (EHRs), there are also the demands of dealing with the other type of text data. Automatic medical diagnosis is an example of new applications using a different dat...
Republic of Korea Humans Natural Language Processing Information Storage and Retrieval Machine Learning Neural Networks, Computer Electronic Health Records Language
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Marrying Medical Domain Knowledge With Deep Learning on Electronic Health Records: A Deep Visual Analytics Approach.

Journal of medical Internet research Sep 28, 2020
BACKGROUND: Deep learning models have attracted significant interest from health care researchers during the last few decades. There have been many studies that apply deep learning to medical applications and achieve promising results. However, there...
Humans Neural Networks, Computer Electronic Health Records Deep Learning Knowledge Bases
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