AIMC Topic: Electronic Health Records
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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
Feb 28, 2022
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
Feb 23, 2022
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
Feb 22, 2022
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
Feb 21, 2022
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
Feb 21, 2022
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
Feb 21, 2022
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...
Automated Mapping of Real-world Oncology Laboratory Data to LOINC.
AMIA ... Annual Symposium proceedings. AMIA Symposium
Feb 21, 2022
In this study we seek to determine the efficacy of using automated mapping methods to reduce the manual mapping burden of laboratory data to LOINC(r) on a nationwide electronic health record derived oncology specific dataset. We developed novel encod...
Towards more patient friendly clinical notes through language models and ontologies.
AMIA ... Annual Symposium proceedings. AMIA Symposium
Feb 21, 2022
Clinical notes are an efficient way to record patient information but are notoriously hard to decipher for non-experts. Automatically simplifying medical text can empower patients with valuable information about their health, while saving clinicians ...
Understanding Heart Failure Patients EHR Clinical Features via SHAP Interpretation of Tree-Based Machine Learning Model Predictions.
AMIA ... Annual Symposium proceedings. AMIA Symposium
Feb 21, 2022
Heart failure (HF) is a major cause of mortality. Accurately monitoring HF progress and adjusting therapies are critical for improving patient outcomes. An experienced cardiologist can make accurate HF stage diagnoses based on combination of symptoms...
Extraction of Active Medications and Adherence Using Natural Language Processing for Glaucoma Patients.
AMIA ... Annual Symposium proceedings. AMIA Symposium
Feb 21, 2022
Accuracy of medication data in electronic health records (EHRs) is crucial for patient care and research, but many studies have shown that medication lists frequently contain errors. In contrast, physicians often pay more attention to the clinical no...
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