Machine Learning im Gesundheitswesen
Anwendungen von maschinellem Lernen in der klinischen Praxis, Prognose und Versorgungsforschung.
Machine Learning verändert das Gesundheitswesen grundlegend – von der Vorhersage von Krankheitsverläufen über die Optimierung von Behandlungspfaden bis hin zur Identifikation von Risikogruppen. Klinische Daten, Laborwerte und Bildgebungsdaten werden mit ML-Modellen ausgewertet, um Entscheidungen schneller und fundierter zu treffen. Diese Seite bündelt die relevantesten Studien und ihre Ergebnisse.
Top 10 – Meistzitierte Papers
Top 2026von 44.161 Papers
"Why Should I Trust You?"
2016 · 14.150 Zit.
A Comprehensive Survey on Graph Neural Networks
2020 · 8.543 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.047 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 7.410 Zit.
Analysis of Survival Data.
1985 · 4.379 Zit.
Artificial intelligence in healthcare: past, present and future
2017 · 4.376 Zit.
A guide to deep learning in healthcare
2018 · 4.222 Zit.
Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement
2015 · 3.769 Zit.
Machine Learning in Medicine
2019 · 3.584 Zit.
The potential for artificial intelligence in healthcare
2019 · 3.446 Zit.
Top 10 – Neueste Papers
zuletzt veröffentlicht
Data Augmentation Application in Deep Learning Drug Discovery by Utilizing Relationships Between Biological and Medical Entities
2029-01-01 · 0 Zit.
ACCELERATING PATHOLOGY REPORT DIGITIZATION: A MULTI-ENGINE OCR AND LLM FRAMEWORK FOR HEALTHCARE APPLICATIONS
2026-09-15 · 0 Zit.
ACCELERATING PATHOLOGY REPORT DIGITIZATION: A MULTI-ENGINE OCR AND LLM FRAMEWORK FOR HEALTHCARE APPLICATIONS
2026-09-15 · 0 Zit.
Applied Machine Learning for CNS Clinical Trial Risk Assessment: An Interpretable Framework with an ALS Case Study
2026-09-01 · 0 Zit.
Applied Machine Learning for CNS Clinical Trial Risk Assessment: An Interpretable Framework with an ALS Case Study
2026-09-01 · 0 Zit.
PROPHET: A Multi-Modal Ensemble Framework for Calibrated Probability Forecasting in Decentralized Prediction Markets
2026-04-03 · 0 Zit.
PROPHET: A Multi-Modal Ensemble Framework for Calibrated Probability Forecasting in Decentralized Prediction Markets
2026-04-03 · 0 Zit.
The Atrial Fibrillation In Critically Ill patients (AFICILL) studies: validation and implemetation of topological data analysis and machine learning techniques in the prediction of atrial-fibrillation related outcomes in patients admitted to medical sub-intensive care units
2026-03-24 · 0 Zit.
Evaluating population health and healthcare access through secondary health data: from chronic conditionsto health emergencies
2026-03-24 · 0 Zit.
Interpretable machine learning prediction models for 28-day mortality in critically ill patients with atrial fibrillation and acute kidney injury.
2026-03-09 · 0 Zit.
Top 8 Autoren
von 59.564 Autoren insgesamt
www.rasitdinc.com
Design Intelligence (United States)
Mihaela van der Schaar
Nan Liu
Qingdao University
Girish N. Nadkarni
Icahn School of Medicine at Mount Sinai
Richard Dobson
King's College London
Jiang Bian
Raşit Dinç
United States Agency for International Development
Gary S. Collins
University Hospitals Birmingham NHS Foundation Trust
Top 8 Institutionen
von 267 Institutionen insgesamt
Chandigarh University
IN
Galgotias University
IN
The Alan Turing Institute
GB
Mass General Brigham
US
Artificial Intelligence in Medicine (Canada)
CA
CMR University
IN
IQVIA (United States)
US
Bennett University
IN