Top Papers: Machine Learning im Gesundheitswesen (2020)

Die 50 meistzitierten Arbeiten zu Machine Learning im Gesundheitswesen aus dem Jahr 2020 (von 4.083 insgesamt).

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.

2018 2019 2020 2021

#	Paper	Zitationen
1	A Comprehensive Survey on Graph Neural Networks Zonghan Wu, Shirui Pan, Fengwen Chen et al. IEEE Transactions on Neural Networks and Learning Systems	8.977
2	An overview of clinical decision support systems: benefits, risks, and strategies for success Reed T. Sutton, David Pincock, Daniel C. Baumgart et al. npj Digital Medicine	2.738
3	The future of digital health with federated learning Nicola Rieke, Jonny Hancox, Wenqi Li et al. npj Digital Medicine	2.404
4	A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI Erico Tjoa, Cuntai Guan IEEE Transactions on Neural Networks and Learning Systems	2.100
5	Can AI Help in Screening Viral and COVID-19 Pneumonia? Muhammad E. H. Chowdhury, Tawsifur Rahman, Amith Khandakar et al. IEEE Access	1.888
6	Precision Medicine, AI, and the Future of Personalized Health Care Kevin B. Johnson, Wei‐Qi Wei, Dilhan Weeraratne et al. Clinical and Translational Science	1.673
7	InceptionTime: Finding AlexNet for time series classification Hassan Ismail Fawaz, Benjamin Lucas, Germain Forestier et al. Data Mining and Knowledge Discovery	1.402
8	The rise of artificial intelligence in healthcare applications Adam Bohr, Kaveh Memarzadeh Elsevier eBooks	1.380
9	Federated Learning for Healthcare Informatics Jie Xu, Benjamin S. Glicksberg, Chang Su et al. Journal of Healthcare Informatics Research	1.371
10	Score-Based Generative Modeling through Stochastic Differential Equations Yang Song, Jascha Sohl‐Dickstein, Diederik P. Kingma et al. arXiv (Cornell University)	1.270
11	Explaining machine learning classifiers through diverse counterfactual explanations Ramaravind Kommiya Mothilal, Amit Sharma, Chenhao Tan —	1.032
12	Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine Zeeshan Ahmed, Khalid Gaffer Mohamed, Saman Zeeshan et al. Database	971
13	The ethics of AI in health care: A mapping review Jessica Morley, Caio C. Vieira Machado, Christopher Burr et al. Social Science & Medicine	866
14	ROCKET: exceptionally fast and accurate time series classification using random convolutional kernels Angus Dempster, François Petitjean, Geoffrey I. Webb Data Mining and Knowledge Discovery	862
15	Automated machine learning: Review of the state-of-the-art and opportunities for healthcare Jonathan Waring, Charlotta Lindvall, Renato Umeton Artificial Intelligence in Medicine	827
16	Supervised Machine Learning: A Brief Primer Tammy Jiang, Jaimie L. Gradus, Anthony J. Rosellini Behavior Therapy	788
17	An Introduction to Machine Learning Solveig Badillo, Balázs Bánfai, Fabian Birzele et al. Clinical Pharmacology & Therapeutics	784
18	The ‘Digital Twin’ to enable the vision of precision cardiology Jorge Corral Acero, Francesca Margara, M Marciniak et al. European Heart Journal	781
19	2020 International Joint Conference on Neural Networks (IJCNN) —	774
20	Fusion of medical imaging and electronic health records using deep learning: a systematic review and implementation guidelines Shih-Cheng Huang, Anuj Pareek, Saeed Seyyedi et al. npj Digital Medicine	766
21	The role of explainability in creating trustworthy artificial intelligence for health care: A comprehensive survey of the terminology, design choices, and evaluation strategies Aniek F. Markus, Jan A. Kors, Peter R. Rijnbeek Journal of Biomedical Informatics	734
22	Machine Learning and Artificial Intelligence: Definitions, Applications, and Future Directions J. Matthew Helm, Andrew Swiergosz, Heather S. Haeberle et al. Current Reviews in Musculoskeletal Medicine	676
23	PadChest: A large chest x-ray image dataset with multi-label annotated reports Aurelia Bustos, Antonio Pertusa, José María Salinas et al. Medical Image Analysis	652
24	Improving the accuracy of medical diagnosis with causal machine learning Jonathan G. Richens, Ciarán M. Lee, Saurabh Johri Nature Communications	594
25	Brief introduction of medical database and data mining technology in big data era Jin Yang, Yuanjie Li, Qingqing Liu et al. Journal of Evidence-Based Medicine	592
26	Predicting 30-days mortality for MIMIC-III patients with sepsis-3: a machine learning approach using XGboost Nianzong Hou, Mingzhe Li, Lu He et al. Journal of Translational Medicine	588
27	CORD-19: The COVID-19 Open Research Dataset Lucy Lu Wang, Kyle Lo, Yoganand Chandrasekhar et al. PubMed	587
28	How Machine Learning Will Transform Biomedicine Jeremy Goecks, Vahid Jalili, Laura M. Heiser et al. Cell	580
29	Survey on categorical data for neural networks John Hancock, Taghi M. Khoshgoftaar Journal Of Big Data	580
30	Applications of machine learning to diagnosis and treatment of neurodegenerative diseases Monika A. Myszczynska, Poojitha N. Ojamies, Alix M.B. Lacoste et al. Nature Reviews Neurology	579
31	Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone Davide Chicco, Giuseppe Jurman BMC Medical Informatics and Decision Making	578
32	Minimum information about clinical artificial intelligence modeling: the MI-CLAIM checklist Beau Norgeot, Giorgio Quer, Brett K. Beaulieu‐Jones et al. Nature Medicine	552
33	Artificial Intelligence in Healthcare: Review and Prediction Case Studies Guoguang Rong, Arnaldo Mendez, Elie Bou Assi et al. Engineering	541
34	Machine Learning and Natural Language Processing in Mental Health: Systematic Review Aziliz Le Glaz, Yannis Haralambous, Deok-Hee Kim-Dufor et al. Journal of Medical Internet Research	540
35	Logistic regression was as good as machine learning for predicting major chronic diseases Simon Nusinovici, Yih Chung Tham, Marco Yu Chak Yan et al. Journal of Clinical Epidemiology	517
36	BEHRT: Transformer for Electronic Health Records Yikuan Li, Shishir Rao, José Roberto Ayala Solares et al. Scientific Reports	512
37	Accurate brain age prediction with lightweight deep neural networks Han Peng, Weikang Gong, Christian F. Beckmann et al. Medical Image Analysis	502
38	Opportunities and Challenges in Explainable Artificial Intelligence (XAI): A Survey Arun Das, Paul Rad arXiv (Cornell University)	496
39	Open Graph Benchmark: Datasets for Machine Learning on Graphs Weihua Hu, Matthias Fey, Marinka Žitnik et al. arXiv (Cornell University)	491
40	Machine learning and artificial intelligence research for patient benefit: 20 critical questions on transparency, replicability, ethics, and effectiveness Sebastian J. Vollmer, Bilal A. Mateen, Gergő Bohner et al. BMJ	465
41	Development and validation of an interpretable deep learning framework for Alzheimer’s disease classification Shangran Qiu, Prajakta Joshi, Matthew I. Miller et al. Brain	465
42	Towards an Artificial Intelligence Framework for Data-Driven Prediction of Coronavirus Clinical Severity Xiangao Jiang, Megan Coffee, Anasse Bari et al. Computers, materials & continua/Computers, materials & continua (Print)	455
43	Learning to Learn Single Domain Generalization Fengchun Qiao, L. Zhao, Xi Peng —	447
44	Machine learning for precision medicine Sarah J. MacEachern, Nils D. Forkert Genome	446
45	CNN-RNN Based Intelligent Recommendation for Online Medical Pre-Diagnosis Support Xiaokang Zhou, Yue Li, Wei Liang IEEE/ACM Transactions on Computational Biology and Bioinformatics	446
46	Artificial Intelligence in Health Care: Bibliometric Analysis Yuqi Guo, Zhichao Hao, Shichong Zhao et al. Journal of Medical Internet Research	438
47	Early prediction of circulatory failure in the intensive care unit using machine learning Stephanie L. Hyland, Martin Faltys, Matthias Hüser et al. Nature Medicine	437
48	Explainable artificial intelligence model to predict acute critical illness from electronic health records Simon Meyer Lauritsen, Mads Ruben Burgdorff Kristensen, Mathias Vassard Olsen et al. Nature Communications	436
49	A tutorial on calibration measurements and calibration models for clinical prediction models Yingxiang Huang, Wentao Li, Fima Macheret et al. Journal of the American Medical Informatics Association	432
50	Underspecification Presents Challenges for Credibility in Modern Machine Learning Alexander D’Amour, Katherine Heller, Dan Moldovan et al. arXiv (Cornell University)	431

Top Papers: Machine Learning im Gesundheitswesen (2020)

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