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 49.076 Papers
"Why Should I Trust You?"
2016 · 14.732 Zit.
Coding Algorithms for Defining Comorbidities in ICD-9-CM and ICD-10 Administrative Data
2005 · 10.547 Zit.
A Comprehensive Survey on Graph Neural Networks
2020 · 8.949 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.550 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 8.061 Zit.
Artificial intelligence in healthcare: past, present and future
2017 · 4.519 Zit.
A guide to deep learning in healthcare
2018 · 4.482 Zit.
Analysis of Survival Data.
1985 · 4.382 Zit.
Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement
2015 · 3.925 Zit.
Machine Learning in Medicine
2019 · 3.794 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.
AI decision support tool to prepare clinicians AI-driven mental health decision support linked to clinician resilience and preparedness
2026-12-31 · 0 Zit.
Towards Early and Accurate Disease Detection Through Multimodal Predictive Modeling: Fusion of Electronic Health Records, Medical Imaging, And Omics Data Using Interpretable Machine Learning.
2026-11-03 · 0 Zit.
Towards Early and Accurate Disease Detection Through Multimodal Predictive Modeling: Fusion of Electronic Health Records, Medical Imaging, And Omics Data Using Interpretable Machine Learning.
2026-11-03 · 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.
Exploring Necessary Conditions for High and Low Patient Ratings in Online Healthcare Consultations: An LLM-Based Weak Supervision Approach
2026-07-01 · 0 Zit.
A Novel Machine Learning and Deep Learning Insight for Alzheimer's Diseases Using Neuroimaging Dataset Analysis
2026-06-01 · 0 Zit.
Top 8 Autoren
von 76.175 Autoren insgesamt
www.rasitdinc.com
Design Intelligence (United States)
Mihaela van der Schaar
Nan Liu
Southwest Medical University
Girish N. Nadkarni
Mount Sinai Health System
Oleh Ivchenko
Odessa National Polytechnic University
Jiang Bian
Regenstrief Institute
Raşit Dinç
California Medical Innovations Institute
Richard Dobson
King's College London
Top 8 Institutionen
von 340 Institutionen insgesamt
Chandigarh University
IN
Galgotias University
IN
Daffodil International University
BD
Mass General Brigham
US
Vels University
IN
The Alan Turing Institute
GB
Artificial Intelligence in Medicine (Canada)
CA
CMR University
IN