Top Papers: Machine Learning im Gesundheitswesen (2021)

Die 50 meistzitierten Arbeiten zu Machine Learning im Gesundheitswesen aus dem Jahr 2021 (von 4.939 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.

2019 2020 2021 2022

#	Paper	Zitationen
1	Text Data Augmentation for Deep Learning Connor Shorten, Taghi M. Khoshgoftaar, Borko Furht Journal Of Big Data	1.660
2	The false hope of current approaches to explainable artificial intelligence in health care Marzyeh Ghassemi, Luke Oakden‐Rayner, Andrew L. Beam The Lancet Digital Health	1.276
3	The role of artificial intelligence in healthcare: a structured literature review Silvana Secinaro, Davide Calandra, Aurelio Secinaro et al. BMC Medical Informatics and Decision Making	1.004
4	A Survey on the Explainability of Supervised Machine Learning Nadia Burkart, Marco F. Huber Journal of Artificial Intelligence Research	940
5	Explanation of machine learning models using shapley additive explanation and application for real data in hospital Yasunobu Nohara, Koutarou Matsumoto, Hidehisa Soejima et al. Computer Methods and Programs in Biomedicine	849
6	Med-BERT: pretrained contextualized embeddings on large-scale structured electronic health records for disease prediction Laila Rasmy, Yang Xiang, Ziqian Xie et al. npj Digital Medicine	799
7	Addressing bias in big data and AI for health care: A call for open science Natalia Norori, Qiyang Hu, Florence M. Aellen et al. Patterns	748
8	Unbox the black-box for the medical explainable AI via multi-modal and multi-centre data fusion: A mini-review, two showcases and beyond Guang Yang, Qinghao Ye, Jun Xia Information Fusion	686
9	Multimodal deep learning models for early detection of Alzheimer’s disease stage Janani Venugopalan, Tong Li, Hamid Reza Hassanzadeh et al. Scientific Reports	680
10	Synthetic data in machine learning for medicine and healthcare Richard J. Chen, Ming Y. Lu, Tiffany Chen et al. Nature Biomedical Engineering	676
11	Deep Generative Modelling: A Comparative Review of VAEs, GANs, Normalizing Flows, Energy-Based and Autoregressive Models Leach, Adam Durham Research Online (Durham University)	597
12	Efficient Prediction of Cardiovascular Disease Using Machine Learning Algorithms With Relief and LASSO Feature Selection Techniques Pronab Ghosh, Sami Azam, Mirjam Jonkman et al. IEEE Access	565
13	Current Challenges and Future Opportunities for XAI in Machine Learning-Based Clinical Decision Support Systems: A Systematic Review Anna Markella Antoniadi, Yuhan Du, Yasmine Guendouz et al. Applied Sciences	557
14	Opening the Black Box: The Promise and Limitations of Explainable Machine Learning in Cardiology Jeremy Petch, Shuang Di, Walter Nelson Canadian Journal of Cardiology	528
15	Machine learning-based prediction of COVID-19 diagnosis based on symptoms Yazeed Zoabi, Shira Deri-Rozov, Noam Shomron npj Digital Medicine	519
16	ICD-11: an international classification of diseases for the twenty-first century James Harrison, Stefanie Weber, Robert Jakob et al. BMC Medical Informatics and Decision Making	510
17	Chatbot for Health Care and Oncology Applications Using Artificial Intelligence and Machine Learning: Systematic Review Lu Xu, Leslie Sanders, Kay Li et al. JMIR Cancer	509
18	Data mining in clinical big data: the frequently used databases, steps, and methodological models Wentao Wu, Yuan-Jie Li, Aozi Feng et al. Military Medical Research	504
19	FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Space Quande Liu, Cheng Chen, Jing Qin et al. —	493
20	Reinforcement Learning in Healthcare: A Survey Chao Yu, Jiming Liu, Shamim Nemati et al. ACM Computing Surveys	481
21	Machine Learning in Healthcare Hafsa Habehh, Suril Gohel Current Genomics	471
22	Heart disease prediction using supervised machine learning algorithms: Performance analysis and comparison Md. Mamun Ali, Bikash Kumar Paul, Kawsar Ahmed et al. Computers in Biology and Medicine	470
23	The promise of artificial intelligence: a review of the opportunities and challenges of artificial intelligence in healthcare Yuri Yin‐Moe Aung, David Wong, Daniel Shu Wei Ting British Medical Bulletin	462
24	Ethical Machine Learning in Healthcare Irene Y. Chen, Emma Pierson, Sherri Rose et al. Annual Review of Biomedical Data Science	454
25	How medical AI devices are evaluated: limitations and recommendations from an analysis of FDA approvals Eric Q. Wu, Kevin Wu, Roxana Daneshjou et al. Nature Medicine	452
26	Comparison of feature importance measures as explanations for classification models Mirka Saarela, Susanne Jauhiainen SN Applied Sciences	433
27	A review of irregular time series data handling with gated recurrent neural networks Philip B. Weerakody, Kok Wai Wong, Guanjin Wang et al. Neurocomputing	407
28	Role of Artificial Intelligence Applications in Real-Life Clinical Practice: Systematic Review Jiamin Yin, Kee Yuan Ngiam, Hock‐Hai Teo Journal of Medical Internet Research	403
29	Artificial Intelligence for Mental Health Care: Clinical Applications, Barriers, Facilitators, and Artificial Wisdom Ellen Lee, John Torous, Munmun De Choudhury et al. Biological Psychiatry Cognitive Neuroscience and Neuroimaging	397
30	Artificial intelligence in sepsis early prediction and diagnosis using unstructured data in healthcare Kim Huat Goh, Le Wang, Adrian Yeow et al. Nature Communications	386
31	Heart disease prediction using machine learning algorithms Harshit Jindal, Sarthak Agrawal, Rishabh Khera et al. IOP Conference Series Materials Science and Engineering	368
32	DEMNET: A Deep Learning Model for Early Diagnosis of Alzheimer Diseases and Dementia From MR Images Suriya Murugan, Chandran Venkatesan, M. G. Sumithra et al. IEEE Access	358
33	Deep Learning Approach for Early Detection of Alzheimer’s Disease Hadeer A. Helaly, Mahmoud Badawy, Amira Y. Haikal Cognitive Computation	353
34	Deep Learning applications for COVID-19 Connor Shorten, Taghi M. Khoshgoftaar, Borko Furht Journal Of Big Data	346
35	Risk of bias in studies on prediction models developed using supervised machine learning techniques: systematic review Constanza L. Andaur Navarro, Johanna AAG Damen, Toshihiko Takada et al. BMJ	346
36	Interpretable prediction of 3-year all-cause mortality in patients with heart failure caused by coronary heart disease based on machine learning and SHAP Ke Wang, Jing Tian, Chu Zheng et al. Computers in Biology and Medicine	338
37	Use of Machine Learning to Develop and Evaluate Models Using Preoperative and Intraoperative Data to Identify Risks of Postoperative Complications Bing Xue, Dingwen Li, Chenyang Lu et al. JAMA Network Open	338
38	Deterministic Local Interpretable Model-Agnostic Explanations for Stable Explainability Muhammad Rehman Zafar, Naimul Khan Machine Learning and Knowledge Extraction	335
39	A multilayer multimodal detection and prediction model based on explainable artificial intelligence for Alzheimer’s disease Shaker El–Sappagh, José M. Alonso, S. M. Riazul Islam et al. Scientific Reports	333
40	Machine learning-based prediction of adverse events following an acute coronary syndrome (PRAISE): a modelling study of pooled datasets Fabrizio D’Ascenzo, Ovidio De Filippo, Guglielmo Gallone et al. The Lancet	333
41	Digital medicine and the curse of dimensionality Visar Berisha, Chelsea Krantsevich, P. Richard Hahn et al. npj Digital Medicine	331
42	Prediction of Chronic Kidney Disease - A Machine Learning Perspective Pankaj Chittora, Sandeep Chaurasia, Prąsun Chakrabarti et al. IEEE Access	329
43	Machine Learning and the Future of Cardiovascular Care Giorgio Quer, Ramy Arnaout, Ramy Arnaout et al. Journal of the American College of Cardiology	324
44	Disentangling User Interest and Conformity for Recommendation with Causal Embedding Yu Zheng, Chen Gao, Xiang Li et al. —	324
45	Accessing Artificial Intelligence for Clinical Decision-Making Chris Giordano, Meghan Brennan, Basma Mohamed et al. Frontiers in Digital Health	294
46	A Systematic Review of Human–Computer Interaction and Explainable Artificial Intelligence in Healthcare With Artificial Intelligence Techniques Mobeen Nazar, Muhammad Mansoor Alam, Eiad Yafi et al. IEEE Access	283
47	Machine learning methods for predicting progression from mild cognitive impairment to Alzheimer’s disease dementia: a systematic review Sergio Grueso, Raquel Viejo-Sobera Alzheimer s Research & Therapy	277
48	The role of machine learning in clinical research: transforming the future of evidence generation E. Hope Weissler, Tristan Naumann, Tomas Andersson et al. Trials	274
49	Challenges and opportunities beyond structured data in analysis of electronic health records Maryam Tayefi, Phuong Dinh Ngo, Taridzo Chomutare et al. Wiley Interdisciplinary Reviews Computational Statistics	270
50	Machine learning in clinical decision making Lorenz Adlung, Yotam Cohen, Uria Mor et al. Med	268

Top Papers: Machine Learning im Gesundheitswesen (2021)

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