Alle Papers – Machine Learning im Gesundheitswesen

Data Work: Meaning-Making in the Era of Data-Rich Medicine

INSPIRE, a publicly available research dataset for perioperative medicine

NIH Precision Medicine Initiative: Implications for Diabetes Research

An overview on deep clustering

A framework for healthcare support in the rural and low income areas of the developing world

Explaining Explanations: Axiomatic Feature Interactions for Deep Networks

Evaluation of the Unified Medical Language System as a Medical Knowledge Source

Policy Evaluation and Optimization with Continuous Treatments

ClinicalGPT: Large Language Models Finetuned with Diverse Medical Data and Comprehensive Evaluation

A Review of Deep Learning Methods for Irregularly Sampled Medical Time Series Data

Learning the Joint Representation of Heterogeneous Temporal Events for Clinical Endpoint Prediction

Scoping review of the current landscape of AI-based applications in clinical trials

Using model checking for critiquing based on clinical guidelines

Machine Learning for Pulmonary and Critical Care Medicine: A Narrative Review

Positive-Unlabeled Learning for inferring drug interactions based on heterogeneous attributes

A Novel Insight Into the Challenges of Diagnosing Degenerative Cervical Myelopathy Using Web-Based Symptom Checkers

Ensemble Methods for Heart Disease Prediction

Using Electronic Health Records to Address Overweight and Obesity

Learning consistent representations with temporal and causal enhancement for knowledge tracing

Recording of diabetes on death certificates

Privacy-Preserving Record Linkage of Deidentified Records Within a Public Health Surveillance System: Evaluation Study

Robust Heart Disease Prediction: A Novel Approach based on Significant Feature and Ensemble learning Model

Multilayer dynamic ensemble model for intensive care unit mortality prediction of neonate patients

Data-driven ICU management: Using Big Data and algorithms to improve outcomes

Significant variation in the performance of DNA methylation predictors across data preprocessing and normalization strategies