AIMC Journal:
Journal of the American Medical Informatics Association : JAMIA

Showing 371 to 380 of 493 articles

A combined strategy of feature selection and machine learning to identify predictors of prediabetes.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: To identify predictors of prediabetes using feature selection and machine learning on a nationally representative sample of the US population.

Temporal convolutional networks allow early prediction of events in critical care.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Clinical interventions and death in the intensive care unit (ICU) depend on complex patterns in patients' longitudinal data. We aim to anticipate these events earlier and more consistently so that staff can consider preemptive action.

Deep learning in clinical natural language processing: a methodical review.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: This article methodically reviews the literature on deep learning (DL) for natural language processing (NLP) in the clinical domain, providing quantitative analysis to answer 3 research questions concerning methods, scope, and context of c...

A governance model for the application of AI in health care.

Journal of the American Medical Informatics Association : JAMIA
As the efficacy of artificial intelligence (AI) in improving aspects of healthcare delivery is increasingly becoming evident, it becomes likely that AI will be incorporated in routine clinical care in the near future. This promise has led to growing ...

Adverse drug event rates in pediatric pulmonary hypertension: a comparison of real-world data sources.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Real-world data (RWD) are increasingly used for pharmacoepidemiology and regulatory innovation. Our objective was to compare adverse drug event (ADE) rates determined from two RWD sources, electronic health records and administrative claim...

Consumer health information and question answering: helping consumers find answers to their health-related information needs.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Consumers increasingly turn to the internet in search of health-related information; and they want their questions answered with short and precise passages, rather than needing to analyze lists of relevant documents returned by search engi...

Automatic extraction of cancer registry reportable information from free-text pathology reports using multitask convolutional neural networks.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: We implement 2 different multitask learning (MTL) techniques, hard parameter sharing and cross-stitch, to train a word-level convolutional neural network (CNN) specifically designed for automatic extraction of cancer data from unstructured...

Imputation and characterization of uncoded self-harm in major mental illness using machine learning.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: We aimed to impute uncoded self-harm in administrative claims data of individuals with major mental illness (MMI), characterize self-harm incidence, and identify factors associated with coding bias.

Supporting the use of standardized nursing terminologies with automatic subject heading prediction: a comparison of sentence-level text classification methods.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: This study focuses on the task of automatically assigning standardized (topical) subject headings to free-text sentences in clinical nursing notes. The underlying motivation is to support nurses when they document patient care by developin...