AIMC Journal:
Journal of the American Medical Informatics Association : JAMIA
Showing 421 to 430 of 493 articles
Hybrid bag of approaches to characterize selection criteria for cohort identification.
Journal of the American Medical Informatics Association : JAMIA
Nov 1, 2019
OBJECTIVE: The 2018 National NLP Clinical Challenge (2018 n2c2) focused on the task of cohort selection for clinical trials, where participating systems were tasked with analyzing longitudinal patient records to determine if the patients met or did n...
Optimizing clinical trials recruitment via deep learning.
Journal of the American Medical Informatics Association : JAMIA
Nov 1, 2019
OBJECTIVE: Clinical trials, prospective research studies on human participants carried out by a distributed team of clinical investigators, play a crucial role in the development of new treatments in health care. This is a complex and expensive proce...
Development and application of a high throughput natural language processing architecture to convert all clinical documents in a clinical data warehouse into standardized medical vocabularies.
Journal of the American Medical Informatics Association : JAMIA
Nov 1, 2019
OBJECTIVE: Natural language processing (NLP) engines such as the clinical Text Analysis and Knowledge Extraction System are a solution for processing notes for research, but optimizing their performance for a clinical data warehouse remains a challen...
A network model of activities in primary care consultations.
Journal of the American Medical Informatics Association : JAMIA
Oct 1, 2019
OBJECTIVE: The objective of this study is to characterize the dynamic structure of primary care consultations by identifying typical activities and their inter-relationships to inform the design of automated approaches to clinical documentation using...
Designing and executing a functional exercise to test a novel informatics tool for mass casualty triage.
Journal of the American Medical Informatics Association : JAMIA
Oct 1, 2019
OBJECTIVE: The testing of informatics tools designed for use during mass casualty incidents presents a unique problem as there is no readily available population of victims or identical exposure setting. The purpose of this article is to describe the...
Applying machine learning to predict real-world individual treatment effects: insights from a virtual patient cohort.
Journal of the American Medical Informatics Association : JAMIA
Oct 1, 2019
OBJECTIVE: We aimed to investigate bias in applying machine learning to predict real-world individual treatment effects.
Learning to detect and understand drug discontinuation events from clinical narratives.
Journal of the American Medical Informatics Association : JAMIA
Oct 1, 2019
OBJECTIVE: Identifying drug discontinuation (DDC) events and understanding their reasons are important for medication management and drug safety surveillance. Structured data resources are often incomplete and lack reason information. In this article...
A new approach and gold standard toward author disambiguation in MEDLINE.
Journal of the American Medical Informatics Association : JAMIA
Oct 1, 2019
OBJECTIVE: Author-centric analyses of fast-growing biomedical reference databases are challenging due to author ambiguity. This problem has been mainly addressed through author disambiguation using supervised machine-learning algorithms. Such algorit...
Satellite images and machine learning can identify remote communities to facilitate access to health services.
Journal of the American Medical Informatics Association : JAMIA
Aug 1, 2019
OBJECTIVE: Community health systems operating in remote areas require accurate information about where people live to efficiently provide services across large regions. We sought to determine whether a machine learning analyses of satellite imagery c...
Underserved populations with missing race ethnicity data differ significantly from those with structured race/ethnicity documentation.
Journal of the American Medical Informatics Association : JAMIA
Aug 1, 2019
OBJECTIVE: We aimed to address deficiencies in structured electronic health record (EHR) data for race and ethnicity by identifying black and Hispanic patients from unstructured clinical notes and assessing differences between patients with or withou...
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