Alle Papers – Machine Learning im Gesundheitswesen

An explainable attention network for fraud detection in claims management

Medical Time-Series Data Generation Using Generative Adversarial Networks

Toward Inclusive Trial Protocols in Heterogeneous Neurological Disorders

Chinese named entity recognition method for the finance domain based on enhanced features and pretrained language models

Generative AI in Medicine — Evaluating Progress and Challenges

Learning temporal probabilistic causal models from longitudinal data

FedSepsis: A Federated Multi-Modal Deep Learning-Based Internet of Medical Things Application for Early Detection of Sepsis from Electronic Health Records Using Raspberry Pi and Jetson Nano Devices

Predicting diabetes-related hospitalizations based on electronic health records

Modified Scoring Criteria for the RBANS Figures

Development of Electronic Health Record–Based Prediction Models for 30-Day Readmission Risk Among Patients Hospitalized for Acute Myocardial Infarction

Data Driven Analytics for Personalized Healthcare

Attention Is Not the Only Choice: Counterfactual Reasoning for Path-Based Explainable Recommendation

Effectiveness of non-interruptive nudge interventions in electronic health records to improve the delivery of care in hospitals: a systematic review

Combining joint models for biomedical event extraction

Generating synthetic clinical data that capture class imbalanced distributions with generative adversarial networks: Example using antiretroviral therapy for HIV

Adaptive Gated Graph Convolutional Network for Explainable Diagnosis of Alzheimer’s Disease Using EEG Data

Deep learning on time series laboratory test results from electronic health records for early detection of pancreatic cancer

Artificial intelligence in virtual standardized patients: Combining natural language understanding and rule based dialogue management to improve conversational fidelity

Eventcharts: Visualizing Survival and other Timed-Events Data

AI-based diabetes care: risk prediction models and implementation concerns

TAdaNet: Task-Adaptive Network for Graph-Enriched Meta-Learning

Can We Edit Factual Knowledge by In-Context Learning?

HPO2Vec+: Leveraging heterogeneous knowledge resources to enrich node embeddings for the Human Phenotype Ontology

Prognosis of Clinical Outcomes with Temporal Patterns and Experiences with One Class Feature Selection

Risk prediction of diabetic nephropathy using machine learning techniques: A pilot study with secondary data