AI Medical Compendium Topic:
Electronic Health Records

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Deep learning-based methods for natural hazard named entity recognition.

Scientific reports
Natural hazard named entity recognition is a technique used to recognize natural hazard entities from a large number of texts. The method of natural hazard named entity recognition can facilitate acquisition of natural hazards information and provide...

Temporal information extraction with the scalable cross-sentence context for electronic health records.

Journal of biomedical informatics
Temporal information is essential for accurate understanding of medical information hidden in electronic health record texts. In the absence of temporal information, it is even impossible to distinguish whether the mentioned symptom is a current cond...

Design and Evaluation of a Postpartum Depression Ontology.

Applied clinical informatics
OBJECTIVE: Postpartum depression (PPD) remains an understudied research area despite its high prevalence. The goal of this study is to develop an ontology to aid in the identification of patients with PPD and to enable future analyses with electronic...

Clinical Text Data Categorization and Feature Extraction Using Medical-Fissure Algorithm and Neg-Seq Algorithm.

Computational intelligence and neuroscience
A large amount of patient information has been gathered in Electronic Health Records (EHRs) concerning their conditions. An EHR, as an unstructured text document, serves to maintain health by identifying, treating, and curing illnesses. In this resea...

Capturing Surgical Data: Comparing a Quality Improvement Registry to Natural Language Processing and Manual Chart Review.

Journal of gastrointestinal surgery : official journal of the Society for Surgery of the Alimentary Tract
INTRODUCTION: Collecting accurate operative details remains a limitation of surgical research. Surgeon-entered data in clinical registries offers one solution, but natural language processing (NLP) has emerged as a modality for automating manual char...

Effective of Smart Mathematical Model by Machine Learning Classifier on Big Data in Healthcare Fast Response.

Computational and mathematical methods in medicine
In the past few years, big data related to healthcare has become more important, due to the abundance of data, the increasing cost of healthcare, and the privacy of healthcare. Create, analyze, and process large and complex data that cannot be proces...

Deep Learning-based detection of psychiatric attributes from German mental health records.

International journal of medical informatics
BACKGROUND: Health care records provide large amounts of data with real-world and longitudinal aspects, which is advantageous for predictive analyses and improvements in personalized medicine. Text-based records are a main source of information in me...

A Study of Social and Behavioral Determinants of Health in Lung Cancer Patients Using Transformers-based Natural Language Processing Models.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Social and behavioral determinants of health (SBDoH) have important roles in shaping people's health. In clinical research studies, especially comparative effectiveness studies, failure to adjust for SBDoH factors will potentially cause confounding i...

Integrating Multimodal Electronic Health Records for Diagnosis Prediction.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Diagnosis prediction aims to predict the patient's future diagnosis based on their Electronic Health Records (EHRs). Most existing works adopt recurrent neural networks (RNNs) to model the sequential EHR data. However, they mainly utilize medical cod...

A Machine Learning Pipeline for Accurate COVID-19 Health Outcome Prediction using Longitudinal Electronic Health Records.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Current COVID-19 predictive models primarily focus on predicting the risk of mortality, and rely on COVID-19 specific medical data such as chest imaging after COVID-19 diagnosis. In this project, we developed an innovative supervised machine learning...