AIMC Topic: Electronic Health Records

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Deep learning predicts extreme preterm birth from electronic health records.

Journal of biomedical informatics Oct 31, 2019
OBJECTIVE: Models for predicting preterm birth generally have focused on very preterm (28-32 weeks) and moderate to late preterm (32-37 weeks) settings. However, extreme preterm birth (EPB), before the 28th week of gestational age, accounts for the m...
Datasets as Topic International Classification of Diseases Electronic Health Records Infant, Newborn Infant, Extremely Premature Deep Learning Humans Algorithms
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Analysis of risk factor domains in psychosis patient health records.

Journal of biomedical semantics Oct 31, 2019
BACKGROUND: Readmission after discharge from a hospital is disruptive and costly, regardless of the reason. However, it can be particularly problematic for psychiatric patients, so predicting which patients may be readmitted is critically important b...
Humans Neural Networks, Computer Biological Ontologies Male Natural Language Processing Psychotic Disorders Data Mining Electronic Health Records Female Risk Factors Young Adult
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Using machine learning to selectively highlight patient information.

Journal of biomedical informatics Oct 29, 2019
BACKGROUND: Electronic medical record (EMR) systems need functionality that decreases cognitive overload by drawing the clinician's attention to the right data, at the right time. We developed a Learning EMR (LEMR) system that learns statistical mode...
Humans Machine Learning Information Seeking Behavior Physicians Electronic Health Records
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ECG AI-Guided Screening for Low Ejection Fraction (EAGLE): Rationale and design of a pragmatic cluster randomized trial.

American heart journal Oct 25, 2019
BACKGROUND: A deep learning algorithm to detect low ejection fraction (EF) using routine 12-lead electrocardiogram (ECG) has recently been developed and validated. The algorithm was incorporated into the electronic health record (EHR) to automaticall...
Cardiac Output, Low Asymptomatic Diseases Humans Artificial Intelligence Electrocardiography Prospective Studies Informed Consent Sample Size Electronic Health Records Heart Failure Echocardiography Cost-Benefit Analysis Deep Learning
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Ensembles of natural language processing systems for portable phenotyping solutions.

Journal of biomedical informatics Oct 23, 2019
BACKGROUND: Manually curating standardized phenotypic concepts such as Human Phenotype Ontology (HPO) terms from narrative text in electronic health records (EHRs) is time consuming and error prone. Natural language processing (NLP) techniques can fa...
Humans Phenotype Datasets as Topic Electronic Health Records Reproducibility of Results Natural Language Processing
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Machine learning for prediction of septic shock at initial triage in emergency department.

Journal of critical care Oct 22, 2019
BACKGROUND: We hypothesized utilizing machine learning (ML) algorithms for screening septic shock in ED would provide better accuracy than qSOFA or MEWS.
Multivariate Analysis Vital Signs Aged Support Vector Machine Young Adult Prognosis Triage Adult Humans Electronic Health Records Area Under Curve Middle Aged Male Machine Learning Republic of Korea Sepsis Software Female Hospital Mortality Emergency Service, Hospital Algorithms Shock, Septic
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An automated machine learning-based model predicts postoperative mortality using readily-extractable preoperative electronic health record data.

British journal of anaesthesia Oct 15, 2019
BACKGROUND: Rapid, preoperative identification of patients with the highest risk for medical complications is necessary to ensure that limited infrastructure and human resources are directed towards those most likely to benefit. Existing risk scores ...
Preoperative Period Middle Aged Risk Assessment Postoperative Complications Comorbidity Adolescent Hospital Mortality Health Status California Aged, 80 and over Young Adult Adult Male Humans Machine Learning Electronic Health Records Female Risk Factors Aged
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Associative attention networks for temporal relation extraction from electronic health records.

Journal of biomedical informatics Oct 15, 2019
Temporal relations are crucial in constructing a timeline over the course of clinical care, which can help medical practitioners and researchers track the progression of diseases, treatments and adverse reactions over time. Due to the rapid adoption ...
Natural Language Processing Data Mining Humans Neural Networks, Computer Electronic Health Records
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Recurrent Neural Networks for Early Detection of Heart Failure From Longitudinal Electronic Health Record Data: Implications for Temporal Modeling With Respect to Time Before Diagnosis, Data Density, Data Quantity, and Data Type.

Circulation. Cardiovascular quality and outcomes Oct 15, 2019
BACKGROUND: We determined the impact of data volume and diversity and training conditions on recurrent neural network methods compared with traditional machine learning methods.
Humans Neural Networks, Computer Risk Factors Male Diagnosis, Computer-Assisted Incidence Time Factors Machine Learning Electronic Health Records Female Reproducibility of Results Vital Signs Early Diagnosis Primary Health Care Smoking Longitudinal Studies Alcohol Drinking Predictive Value of Tests California Heart Failure
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Atrial Fibrillation Burden Signature and Near-Term Prediction of Stroke: A Machine Learning Analysis.

Circulation. Cardiovascular quality and outcomes Oct 15, 2019
BACKGROUND: Atrial fibrillation (AF) increases the risk of stroke 5-fold and there is rising interest to determine if AF severity or burden can further risk stratify these patients, particularly for near-term events. Using continuous remote monitorin...
Risk Assessment Diagnosis, Computer-Assisted Time Factors Logistic Models Aged Aged, 80 and over Male Middle Aged Humans Neural Networks, Computer Atrial Fibrillation Machine Learning Proof of Concept Study Electronic Health Records Administrative Claims, Healthcare Female Prognosis Telemetry Risk Factors Veterans Health Services United States Retrospective Studies Predictive Value of Tests Signal Processing, Computer-Assisted Stroke
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