Alle Papers – Machine Learning im Gesundheitswesen

Prediction of Depression among Women Using Random Oversampling and Random Forest

Deep multimodal predictome for studying mental disorders

Inclusion of social determinants of health improves sepsis readmission prediction models

Patient Similarity via Joint Embeddings of Medical Knowledge Graph and Medical Entity Descriptions

Discharge summary hospital course summarisation of in patient Electronic Health Record text with clinical concept guided deep pre-trained Transformer models

Advanced Ensemble Machine Learning Techniques for Optimizing Diabetes Mellitus Prognostication: A Detailed Examination of Hospital Data

On the effectiveness of compact biomedical transformers

Exploration of symptom clusters during hemodialysis and symptom network analysis of older maintenance hemodialysis patients: a cross-sectional study

Risk Prediction of Kidney Disease Using Machine Learning Strategies

Investigating Topic Modeling Techniques to Extract Meaningful Insights in Italian Long COVID Narration

Identification of Preanesthetic History Elements by a Natural Language Processing Engine

Can Cluster-Boosted Regression Improve Prediction of Death and Length of Stay in the ICU?

Deep Learning and Explainable AI in Healthcare Using EHR

Towards Interpretable Deep Learning Models for Knowledge Tracing

Intelligent Patient Management

Analysis of treatment pathways for three chronic diseases using OMOP CDM

Decoupled Feature-Temporal CNN: Explaining Deep Learning-Based Machine Health Monitoring

Evaluation of a clinical decision support system for rare diseases: a qualitative study

The Data-Driven Future of Healthcare: A Review

Exploring Transformer Text Generation for Medical Dataset Augmentation.

SSF-DDI: a deep learning method utilizing drug sequence and substructure features for drug–drug interaction prediction

Predictive risk modelling for early hospital readmission of patients with diabetes in India

Application and Potential of Artificial Intelligence in Heart Failure: Past, Present, and Future

Mental Health Risk Adjustment with Clinical Categories and Machine Learning

Comparing medical history data derived from electronic health records and survey answers in the <i>All of Us</i> Research Program