Alle Papers – Machine Learning im Gesundheitswesen

Validation of Psoriatic Arthritis Diagnoses in Electronic Medical Records Using Natural Language Processing

Severe sepsis mortality prediction with relevance vector machines

Follow-up study of refractory obesity treated by fasting.

REBAR: Low-variance, unbiased gradient estimates for discrete latent\n variable models

Personalized lab test models to quantify disease potentials in healthy individuals

Considering clustering: a methodological review of clinical decision support system studies.

Graph neural network modelling as a potentially effective method for predicting and analyzing procedures based on patients' diagnoses

Improving Deep Reinforcement Learning With Transitional Variational Autoencoders: A Healthcare Application

Weight-elimination neural networks applied to coronary surgery mortality prediction

Deep Learning for Natural Language Processing in Urology: State-of-the-Art Automated Extraction of Detailed Pathologic Prostate Cancer Data From Narratively Written Electronic Health Records

A data mining approach for grouping and analyzing trajectories of care using claim data: the example of breast cancer

Transforming French Electronic Health Records into the Observational Medical Outcome Partnership's Common Data Model: A Feasibility Study

Exploring Machine Learning Techniques for Coronary Heart Disease Prediction

Automated Recognition of Alzheimer’s Dementia Using Bag-of-Deep-Features and Model Ensembling

Survey on RNN and CRF models for de-identification of medical free text

Opportunities, Pitfalls, and Alternatives in Adapting Electronic Health Records for Health Services Research

RSDD-Time: Temporal Annotation of Self-Reported Mental Health Diagnoses

Data preparation framework for preprocessing clinical data in data mining.

GRASP: Generic Framework for Health Status Representation Learning Based on Incorporating Knowledge from Similar Patients

Predicting the Risk of Incident Type 2 Diabetes Mellitus in Chinese Elderly Using Machine Learning Techniques

BioInstruct: instruction tuning of large language models for biomedical natural language processing

Predictive Modeling and Analytics for Diabetes using Hyperparameter tuned Machine Learning Techniques

BiomedRAG: A retrieval augmented large language model for biomedicine

Bridging the gap between the randomised clinical trial world and the real world by combination of population-based registry and electronic health record data: A case study in haemato-oncology

Process Knowledge-Infused AI: Toward User-Level Explainability, Interpretability, and Safety