AIMC Topic: Natural Language Processing

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Do Neural Information Extraction Algorithms Generalize Across Institutions?

JCO clinical cancer informatics
PURPOSE: Natural language processing (NLP) techniques have been adopted to reduce the curation costs of electronic health records. However, studies have questioned whether such techniques can be applied to data from previously unseen institutions. We...

[Corpus Analysis of Psychiatric Disorders Utilizing Natural Language Processing and Neurolinguistics].

Brain and nerve = Shinkei kenkyu no shinpo
Natural language processing (NLP) is a technology in which a computer processes human "natural language" directly. Along with the development of technologies such as automatic morphological analysis and transition words or sentences to vectors, more ...

In Reply to Spadafore and Monrad.

Academic medicine : journal of the Association of American Medical Colleges

An investigation of single-domain and multidomain medication and adverse drug event relation extraction from electronic health record notes using advanced deep learning models.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: We aim to evaluate the effectiveness of advanced deep learning models (eg, capsule network [CapNet], adversarial training [ADV]) for single-domain and multidomain relation extraction from electronic health record (EHR) notes.

Medical Knowledge Extraction and Analysis from Electronic Medical Records Using Deep Learning.

Chinese medical sciences journal = Chung-kuo i hsueh k'o hsueh tsa chih
Objectives Medical knowledge extraction (MKE) plays a key role in natural language processing (NLP) research in electronic medical records (EMR), which are the important digital carriers for recording medical activities of patients. Named entity reco...

Measuring Exposure to Incarceration Using the Electronic Health Record.

Medical care
BACKGROUND: Electronic health records (EHRs) are a rich source of health information; however social determinants of health, including incarceration, and how they impact health and health care disparities can be hard to extract.

Quantifying risk factors in medical reports with a context-aware linear model.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: We seek to quantify the mortality risk associated with mentions of medical concepts in textual electronic health records (EHRs). Recognizing mentions of named entities of relevant types (eg, conditions, symptoms, laboratory tests or behavi...