Alle Papers – Machine Learning im Gesundheitswesen

An Integrated Two-Layered Voting (TLV) Framework for Coronary Artery Disease Prediction Using Machine Learning Classifiers

Predicting Heart Disease Based on Influential Features with Machine Learning

Weighing the odds: Assessing underdiagnosis of adult obesity via electronic medical record problem list omissions

Promoting Secondary Analysis of Electronic Medical Records in China: Summary of the PLAGH-MIT Critical Data Conference and Health Datathon

Concurrent imputation and prediction on EHR data using bi-directional GANs

Symptom-based network classification identifies distinct clinical subgroups of liver diseases with common molecular pathways

Comparison of Predictive Models for the Early Diagnosis of Diabetes

Clinical Data Miner: An Electronic Case Report Form System With Integrated Data Preprocessing and Machine-Learning Libraries Supporting Clinical Diagnostic Model Research

A Comparative Analysis of Explainable AI Techniques for Enhanced Model Interpretability

Converting to a Common Data Model: What is Lost in Translation?

The AI Will See You Now: Feasibility and Acceptability of a Conversational AI Medical Interviewing System

Large language models could make natural language again the universal interface of healthcare

Improved de-identification of physician notes through integrative modeling of both public and private medical text

Utilizing Large Language Models for Enhanced Clinical Trial Matching: A Study on Automation in Patient Screening

Estimating Counterfactual Treatment Outcomes over Time Through\n Adversarially Balanced Representations

Using Machine Learning Models to Predict In-Hospital Mortality for ST-Elevation Myocardial Infarction Patients

Exploring Language Markers of Mental Health in Psychiatric Stories

Research on a traditional Chinese medicine case-based question-answering system integrating large language models and knowledge graphs

Customization of a Severity of Illness Score Using Local Electronic Medical Record Data

miniTUBA: medical inference by network integration of temporal data using Bayesian analysis

EVALUATING OBSTETRIC RISK SCORES BY RECEIVER OPERATING CHARACTERISTIC CURVES

A method for machine learning generation of realistic synthetic datasets for validating healthcare applications

Multi-task deep autoencoder to predict Alzheimer’s disease progression using temporal DNA methylation data in peripheral blood

Chemical-induced disease extraction via recurrent piecewise convolutional neural networks

Algorithm-Agnostic Explainability for Unsupervised Clustering