AIMC Topic: Electronic Health Records
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Artificial intelligence in sepsis early prediction and diagnosis using unstructured data in healthcare.
Nature communications
Jan 29, 2021
Sepsis is a leading cause of death in hospitals. Early prediction and diagnosis of sepsis, which is critical in reducing mortality, is challenging as many of its signs and symptoms are similar to other less critical conditions. We develop an artifici...
Disease Concept-Embedding Based on the Self-Supervised Method for Medical Information Extraction from Electronic Health Records and Disease Retrieval: Algorithm Development and Validation Study.
Journal of medical Internet research
Jan 27, 2021
BACKGROUND: The electronic health record (EHR) contains a wealth of medical information. An organized EHR can greatly help doctors treat patients. In some cases, only limited patient information is collected to help doctors make treatment decisions. ...
Early identification of patients with acute gastrointestinal bleeding using natural language processing and decision rules.
Journal of gastroenterology and hepatology
Jan 25, 2021
BACKGROUND AND AIM: Guidelines recommend risk stratification scores in patients presenting with gastrointestinal bleeding (GIB), but such scores are uncommonly employed in practice. Automation and deployment of risk stratification scores in real time...
A Preliminary Characterization of Canonicalized and Non-Canonicalized Section Headers Across Variable Clinical Note Types.
AMIA ... Annual Symposium proceedings. AMIA Symposium
Jan 25, 2021
In the electronic health record, the majority of clinically relevant information is stored within clinical notes. Most clinical notes follow a set organizational structure composed of canonicalized section headers that facilitate clinical review and ...
AllergyMap: An Open Source Corpus of Allergy Mention Normalizations.
AMIA ... Annual Symposium proceedings. AMIA Symposium
Jan 25, 2021
Allergy mention normalization is challenging because of the wide range of possible allergens including medications, foods, plants, animals, and consumer products. This paper describes the process of mapping free-text allergy information from an elect...
Timely and Efficient AI Insights on EHR: System Design.
AMIA ... Annual Symposium proceedings. AMIA Symposium
Jan 25, 2021
A patient's electronic health record (EHR) contains extensive documentation of the patient's medical history but is difficult for clinicians to review and find what they are looking for under the time constraints of the clinical setting. Although rec...
Patient Cohort Retrieval using Transformer Language Models.
AMIA ... Annual Symposium proceedings. AMIA Symposium
Jan 25, 2021
We apply deep learning-based language models to the task of patient cohort retrieval (CR) with the aim to assess their efficacy. The task ofCR requires the extraction of relevant documents from the electronic health records (EHRs) on the basis of a g...
EffiCare: Better Prognostic Models via Resource-Efficient Health Embeddings.
AMIA ... Annual Symposium proceedings. AMIA Symposium
Jan 25, 2021
Recent medical prognostic models adapted from high data-resource fields like language processing have quickly grown in complexity and size. However, since medical data typically constitute low data-resource settings, performances on tasks like clinic...
User-Centered Design of a Machine Learning Intervention for Suicide Risk Prediction in a Military Setting.
AMIA ... Annual Symposium proceedings. AMIA Symposium
Jan 25, 2021
Primary care represents a major opportunity for suicide prevention in the military. Significant advances have been made in using electronic health record data to predict suicide attempts in patient populations. With a user-centered design approach, w...
Multi-task Learning via Adaptation to Similar Tasks for Mortality Prediction of Diverse Rare Diseases.
AMIA ... Annual Symposium proceedings. AMIA Symposium
Jan 25, 2021
The mortality prediction of diverse rare diseases using electronic health record (EHR) data is a crucial task for intelligent healthcare. However, data insufficiency and the clinical diversity of rare diseases make it hard for deep learning models to...
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