Alle Papers – Machine Learning im Gesundheitswesen

Multi-channel, convolutional attention based neural model for automated diagnostic coding of unstructured patient discharge summaries

Chest X-rays Classification: A Multi-Label and Fine-Grained Problem

MedDialog: A Large-scale Medical Dialogue Dataset

AD-CovNet: An exploratory analysis using a hybrid deep learning model to handle data imbalance, predict fatality, and risk factors in Alzheimer's patients with COVID-19

Deteriorated functional and structural brain networks and normally appearing functional–structural coupling in diabetic kidney disease: a graph theory-based magnetic resonance imaging study

Dynamic prediction of cardiovascular disease using improved LSTM

Using Deep Learning to Identify High-Risk Patients with Heart Failure withReduced Ejection Fraction

Real-world prediction of preclinical Alzheimer’s disease with a deep generative model

Machine learning models for identifying preterm infants at risk of cerebral hemorrhage

Evaluation of Doc’EDS: a French semantic search tool to query health documents from a clinical data warehouse

Ensembles of natural language processing systems for portable phenotyping solutions

Synthetic data generation in healthcare: A scoping review of reviews on domains, motivations, and future applications

Correction: Computational Phenotype Discovery Using Unsupervised Feature Learning over Noisy, Sparse, and Irregular Clinical Data

Lightweight transformers for clinical natural language processing

Machine Learning Enhanced Virtual Autopsy

Data-driven decision-making in healthcare: Improving patient outcomes through predictive modeling

Computerized migraine diagnostic tools: a systematic review

Latent topic ensemble learning for hospital readmission cost optimization

Effective Treatment Recommendations for Type 2 Diabetes Management Using Reinforcement Learning: Treatment Recommendation Model Development and Validation

Xplainer: From X-Ray Observations to Explainable Zero-Shot Diagnosis

Identifying the Predictive Capability of Machine Learning Classifiers for Designing Heart Disease Detection System

Deepr: A Convolutional Net for Medical Records

Interpretable Question Answering on Knowledge Bases and Text

Toward understanding COVID-19 pneumonia: a deep-learning-based approach for severity analysis and monitoring the disease

Secondary use of electronic medical records for clinical research: challenges and opportunities