AIMC Topic: Community Health Planning

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Natural Language Processing Improves Detection of Nonsevere Hypoglycemia in Medical Records Versus Coding Alone in Patients With Type 2 Diabetes but Does Not Improve Prediction of Severe Hypoglycemia Events: An Analysis Using the Electronic Medical Record in a Large Health System.

Diabetes care May 15, 2020
OBJECTIVE: To determine if natural language processing (NLP) improves detection of nonsevere hypoglycemia (NSH) in patients with type 2 diabetes and no NSH documentation by diagnosis codes and to measure if NLP detection improves the prediction of fu...
Humans Algorithms Electronic Health Records Female Aged Aged, 80 and over Young Adult Adult Male Middle Aged Severity of Illness Index Diabetes Mellitus, Type 2 United States Predictive Value of Tests Natural Language Processing Hypoglycemia Information Storage and Retrieval International Classification of Diseases Community Health Planning Clinical Decision Rules
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Development of a Late-Life Dementia Prediction Index with Supervised Machine Learning in the Population-Based CAIDE Study.

Journal of Alzheimer's disease : JAD Jan 1, 2017
BACKGROUND AND OBJECTIVE: This study aimed to develop a late-life dementia prediction model using a novel validated supervised machine learning method, the Disease State Index (DSI), in the Finnish population-based CAIDE study.
Humans Retrospective Studies Female Reproducibility of Results Risk Factors Aged Male Severity of Illness Index Dementia ROC Curve Cognition Predictive Value of Tests Supervised Machine Learning Cerebrovascular Disorders Neuropsychological Tests Finland Apolipoproteins E Community Health Planning
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