Alle Papers – Machine Learning im Gesundheitswesen

Patient-level temporal aggregation for text-based asthma status ascertainment

Predicting clinical progression trajectories of early Alzheimer's disease patients

Alzheimer's Disease Prediction Using Convolutional Neural Network Models Leveraging Pre-existing Architecture and Transfer Learning

Predicting and Improving Memory Retention: Psychological Theory Matters in the Big Data Era

Robust-ODAL: Learning from heterogeneous health systems without sharing patient-level data

Feasibility of AsthmaCritic, a decision-support system for asthma and COPD which generates patient-specific feedback on routinely recorded data in general practice

Health equity assessment of machine learning performance (HEAL): a framework and dermatology AI model case study

Composite distance metric integration by leveraging multiple experts' inputs and its application in patient similarity assessment

Toward explainable AI-empowered cognitive health assessment

Predicting hospital length of stay using machine learning on a large open health dataset

Extracting Family History of Patients From Clinical Narratives: Exploring an End-to-End Solution With Deep Learning Models

TyG-er: An ensemble Regression Forest approach for identification of clinical factors related to insulin resistance condition using Electronic Health Records

The application of unsupervised deep learning in predictive models using electronic health records

Retention weighted recall improves discrimination of Alzheimer's disease

Ranking Medical Terms to Support Expansion of Lay Language Resources for Patient Comprehension of Electronic Health Record Notes: Adapted Distant Supervision Approach

Improving Clinical Translation of Machine Learning Approaches Through Clinician-Tailored Visual Displays of Black Box Algorithms: Development and Validation

Problems and Barriers Related to the Use of AI-Based Clinical Decision Support Systems: Interview Study

Graph Artificial Intelligence in Medicine

Integrating Knowledge Graphs with Symbolic AI: The Path to Interpretable Hybrid AI Systems in Medicine

Construction of a knowledge graph for diabetes complications from expert-reviewed clinical evidences

Characterizing shared and distinct symptom clusters in common chronic conditions through natural language processing of nursing notes

Machine Learning for Predicting Micro- and Macrovascular Complications in Individuals With Prediabetes or Diabetes: Retrospective Cohort Study

Multi-View Deep Learning Framework for Predicting Patient Expenditure in Healthcare

Mapping the Bibliometrics Landscape of AI in Medicine: Methodological Study

A General Framework for Diagnosis Prediction via Incorporating Medical Code Descriptions