AIMC Topic: Machine Learning

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Training a Convolutional Neural Network with Terminology Summarization Data Improves SNOMED CT Enrichment.

AMIA ... Annual Symposium proceedings. AMIA Symposium Mar 4, 2020
As a step toward learning to automatically insert new concepts into a large biomedical ontology, we are studying the easier problem of automatically verifying that an IS-A link should exist between a new child concept and an existing parent concept. ...
Deep Learning Systematized Nomenclature of Medicine Biological Ontologies Neural Networks, Computer Machine Learning
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Identifying Cancer Patients at Risk for Heart Failure Using Machine Learning Methods.

AMIA ... Annual Symposium proceedings. AMIA Symposium Mar 4, 2020
Cardiotoxicity related to cancer therapies has become a serious issue, diminishing cancer treatment outcomes and quality of life. Early detection of cancer patients at risk for cardiotoxicity before cardiotoxic treatments and providing preventive mea...
Male Middle Aged Neoplasms Aged Humans Aged, 80 and over Electronic Health Records Machine Learning Adult Female Algorithms Risk Factors Quality of Life Antineoplastic Agents Heart Failure
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Predicting Nocturnal Hypoglycemia from Continuous Glucose Monitoring Data with Extended Prediction Horizon.

AMIA ... Annual Symposium proceedings. AMIA Symposium Mar 4, 2020
Nocturnal hypoglycemia is a serious complication of insulin-treated diabetes, which commonly goes undetected. Continuous glucose monitoring (CGM) devices have enabled prediction of impending nocturnal hypoglycemia, however, prior efforts have been li...
Models, Biological Hypoglycemia Blood Glucose Self-Monitoring Monitoring, Ambulatory Diabetes Mellitus, Type 1 Hypoglycemic Agents Humans Insulin Area Under Curve Machine Learning ROC Curve Blood Glucose
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Assessing Contribution of Higher Order Clinical Risk Factors to Prediction of Outcome in Aneurysmal Subarachnoid Hemorrhage Patients.

AMIA ... Annual Symposium proceedings. AMIA Symposium Mar 4, 2020
The goal of this study was to investigate the application of machine learning models capable of capturing multiplica tive and temporal clinical risk factors for outcome prediction inpatients with aneurysmal subarachnoid hemorrhage (aSAH). We examined...
Humans Neural Networks, Computer Treatment Outcome Machine Learning Cohort Studies Area Under Curve ROC Curve Subarachnoid Hemorrhage Prognosis Logistic Models Risk Factors
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Using Natural Language Processing to improve EHR Structured Data-based Surgical Site Infection Surveillance.

AMIA ... Annual Symposium proceedings. AMIA Symposium Mar 4, 2020
Surgical Site Infection surveillance in healthcare systems is labor intensive and plagued by underreporting as current methodology relies heavily on manual chart review. The rapid adoption of electronic health records (EHRs) has the potential to allo...
Natural Language Processing Surgical Wound Infection Information Storage and Retrieval Logistic Models Support Vector Machine Decision Trees Electronic Health Records Sensitivity and Specificity Humans Machine Learning Algorithms
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A High Recall Classifier for Selecting Articles for MEDLINE Indexing.

AMIA ... Annual Symposium proceedings. AMIA Symposium Mar 4, 2020
MEDLINE is the National Library of Medicine's premier bibliographic database for biomedical literature. A highly valuable feature of the database is that each record is manually indexed with a controlled vocabulary called MeSH. Most MEDLINE journals ...
National Library of Medicine (U.S.) Abstracting and Indexing MEDLINE Medical Subject Headings Neural Networks, Computer Machine Learning United States
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Bootstrapping Adversarial Learning of Biomedical Ontology Alignments.

AMIA ... Annual Symposium proceedings. AMIA Symposium Mar 4, 2020
Learning how to automatically align biomedical ontologies has been a long-standing goal, given their ever-growing content and the many applications that rely on them. Because the knowledge graphs underlying biomedical ontologies enable neural learnin...
Semantics Biological Ontologies Vocabulary, Controlled Neural Networks, Computer Machine Learning Models, Theoretical
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Interpretation of machine learning predictions for patient outcomes in electronic health records.

AMIA ... Annual Symposium proceedings. AMIA Symposium Mar 4, 2020
Electronic health records are an increasingly important resource for understanding the interactions between patient health, environment, and clinical decisions. In this paper we report an empirical study of predictive modeling of seven patient outcom...
Humans Machine Learning Logistic Models Models, Theoretical Models, Statistical Electronic Health Records Female Male Patient Outcome Assessment
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Machine Learned Mapping of Local EHR Flowsheet Data to Standard Information Models using Topic Model Filtering.

AMIA ... Annual Symposium proceedings. AMIA Symposium Mar 4, 2020
Electronic health record (EHR) data must be mapped to standard information models for interoperability and to support research across organizations. New information models are being developed and validated for data important to nursing, but a signifi...
Health Information Interoperability Machine Learning Health Information Management Electronic Health Records Support Vector Machine Humans
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Machine Learning Based Opioid Overdose Prediction Using Electronic Health Records.

AMIA ... Annual Symposium proceedings. AMIA Symposium Mar 4, 2020
Opioid addiction in the United States has come to national attention as opioid overdose (OD) related deaths have risen at alarming rates. Combating opioid epidemic becomes a high priority for not only governments but also healthcare providers. This d...
Opioid-Related Disorders Drug Overdose New York Humans United States Machine Learning Models, Statistical Databases, Factual Analgesics, Opioid Electronic Health Records
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