Top Papers: Machine Learning im Gesundheitswesen (2019)

Die 50 meistzitierten Arbeiten zu Machine Learning im Gesundheitswesen aus dem Jahr 2019 (von 3.032 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.

2017 2018 2019 2020

#	Paper	Zitationen
1	Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead Cynthia Rudin Nature Machine Intelligence	8.444
2	Machine Learning in Medicine Alvin Rajkomar, Jay B. Dean, Isaac S. Kohane New England Journal of Medicine	3.752
3	The potential for artificial intelligence in healthcare Thomas H. Davenport, Ravi Kalakota Future Healthcare Journal	3.531
4	Key challenges for delivering clinical impact with artificial intelligence Christopher Kelly, Alan Karthikesalingam, Mustafa Suleyman et al. BMC Medicine	2.331
5	The “All of Us” Research Program The All of Us Research Program Investigators New England Journal of Medicine	2.256
6	BERT4Rec Fei Sun, Jun Liu, Jian Wu et al. —	2.201
7	A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models Evangelia Christodoulou, Jie Ma, Gary S. Collins et al. Journal of Clinical Epidemiology	1.879
8	XAI—Explainable artificial intelligence David Gunning, Mark Stefik, Jaesik Choi et al. Science Robotics	1.838
9	Causability and explainability of artificial intelligence in medicine Andreas Holzinger, Georg Langs, Helmut Denk et al. Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery	1.656
10	Comparing different supervised machine learning algorithms for disease prediction Shahadat Uddin, Arif Khan, Md Ekramul Hossain et al. BMC Medical Informatics and Decision Making	1.601
11	Publicly Available Clinical Emily Alsentzer, John R. Murphy, William Boag et al. —	1.574
12	MIMIC-CXR, a de-identified publicly available database of chest radiographs with free-text reports Alistair E. W. Johnson, Tom Pollard, Seth J. Berkowitz et al. Scientific Data	1.544
13	Session-Based Recommendation with Graph Neural Networks Shu Wu, Yuyuan Tang, Yanqiao Zhu et al. Proceedings of the AAAI Conference on Artificial Intelligence	1.433
14	Explainable AI: Interpreting, Explaining and Visualizing Deep Learning NIPS 2017 Long Beach, Calif., Wojciech Samek, Grégoire Montavon et al. Lecture notes in computer science	1.316
15	<p>Taiwan’s National Health Insurance Research Database: past and future</p> Cheng‐Yang Hsieh, Chien‐Chou Su, Shih‐Chieh Shao et al. Clinical Epidemiology	1.188
16	Explainable AI: from black box to glass box Arun Rai Journal of the Academy of Marketing Science	1.101
17	A clinically applicable approach to continuous prediction of future acute kidney injury Nenad Tomašev, Xavier Glorot, Jack W. Rae et al. Nature	1.074
18	Overview of artificial intelligence in medicine Fnu Amisha, Paras Malik, Monika Pathania et al. Journal of Family Medicine and Primary Care	1.070
19	Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again Eric J. Topol, Graham Winton —	996
20	Do no harm: a roadmap for responsible machine learning for health care Jenna Wiens, Suchi Saria, Mark Sendak et al. Nature Medicine	938
21	Artificial Intelligence for Mental Health and Mental Illnesses: an Overview Sarah Graham, Colin A. Depp, Ellen Lee et al. Current Psychiatry Reports	880
22	The “All of Us” Research Program Frank Sullivan, Brian McKinstry, Shobna Vasishta New England Journal of Medicine	874
23	Transfer Learning in Biomedical Natural Language Processing: An Evaluation of BERT and ELMo on Ten Benchmarking Datasets Yifan Peng, Shankai Yan, Zhiyong Lu —	837
24	Designing Theory-Driven User-Centric Explainable AI Danding Wang, Qian Yang, Ashraf Abdul et al. —	830
25	XGBoost Model for Chronic Kidney Disease Diagnosis Adeola Ogunleye, Qing‐Guo Wang IEEE/ACM Transactions on Computational Biology and Bioinformatics	755
26	Evaluation and accurate diagnoses of pediatric diseases using artificial intelligence Huiying Liang, Brian Tsui, Hao Ni et al. Nature Medicine	706
27	Deep Learning in Alzheimer's Disease: Diagnostic Classification and Prognostic Prediction Using Neuroimaging Data Taeho Jo, Kwangsik Nho, Andrew J. Saykin Frontiers in Aging Neuroscience	684
28	Explainable AI: A Brief Survey on History, Research Areas, Approaches and Challenges Feiyu Xu, Hans Uszkoreit, Yangzhou Du et al. Lecture notes in computer science	662
29	2019 International Joint Conference on Neural Networks (IJCNN) —	642
30	The importance of interpretability and visualization in machine learning for applications in medicine and health care Alfredo Vellido Neural Computing and Applications	630
31	The seven tools of causal inference, with reflections on machine learning Judea Pearl Communications of the ACM	628
32	Toward systematic review automation: a practical guide to using machine learning tools in research synthesis Iain Marshall, Byron Wallace Systematic Reviews	624
33	Improving the accuracy of prediction of heart disease risk based on ensemble classification techniques C. Beulah Christalin Latha, S. Carolin Jeeva Informatics in Medicine Unlocked	616
34	ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission Kexin Huang, Jaan Altosaar, Rajesh Ranganath arXiv (Cornell University)	614
35	Artificial Intelligence Transforms the Future of Health Care Nariman Noorbakhsh-Sabet, Ramin Zand, Yanfei Zhang et al. The American Journal of Medicine	607
36	Multitask learning and benchmarking with clinical time series data Hrayr Harutyunyan, Hrant Khachatrian, David C. Kale et al. Scientific Data	603
37	Cardiovascular disease risk prediction using automated machine learning: A prospective study of 423,604 UK Biobank participants Ahmed M. Alaa, Thomas Bolton, Emanuele Di Angelantonio et al. PLoS ONE	594
38	Introducing Artificial Intelligence Training in Medical Education Ketan Paranjape, Michiel Schinkel, Rishi Panday et al. JMIR Medical Education	587
39	Natural Language Processing of Clinical Notes on Chronic Diseases: Systematic Review Seyedmostafa Sheikhalishahi, Riccardo Miotto, Joel T. Dudley et al. JMIR Medical Informatics	545
40	Machine learning for clinical decision support in infectious diseases: a narrative review of current applications Nathan Peiffer‐Smadja, Timothy M. Rawson, Raheelah Ahmad et al. Clinical Microbiology and Infection	527
41	Diabetes Prediction using Machine Learning Algorithms Aishwarya Madhav Mujumdar, V. Vaidehi Procedia Computer Science	524
42	Applications of artificial neural networks in health care organizational decision-making: A scoping review Nida Shahid, Tim Rappon, Whitney Berta PLoS ONE	523
43	Application of Artificial Intelligence to Gastroenterology and Hepatology Catherine Le Berre, William J. Sandborn, Sabeur Aridhi et al. Gastroenterology	517
44	How to Read Articles That Use Machine Learning Yun Liu, Po-Hsuan Cameron Chen, Jonathan Krause et al. JAMA	511
45	Prediction Models for Suicide Attempts and Deaths Bradley E. Belsher, Derek J. Smolenski, Larry D. Pruitt et al. JAMA Psychiatry	509
46	What is Machine Learning? A Primer for the Epidemiologist Qifang Bi, Katherine E Goodman, Joshua Kaminsky et al. American Journal of Epidemiology	506
47	Artificial intelligence and machine learning in clinical development: a translational perspective Pratik Shah, Francis Kendall, Sean Khozin et al. npj Digital Medicine	498
48	Developing prediction models for clinical use using logistic regression: an overview Maren E. Shipe, Stephen A. Deppen, Farhood Farjah et al. Journal of Thoracic Disease	488
49	Deep learning in clinical natural language processing: a methodical review Stephen Wu, Kirk Roberts, Surabhi Datta et al. Journal of the American Medical Informatics Association	474
50	The “inconvenient truth” about AI in healthcare Trishan Panch, Heather Mattie, Leo Anthony Celi npj Digital Medicine	466

Top Papers: Machine Learning im Gesundheitswesen (2019)

Verwandte Seiten