Top Papers: Machine Learning im Gesundheitswesen (2017)

Die 50 meistzitierten Arbeiten zu Machine Learning im Gesundheitswesen aus dem Jahr 2017 (von 1.355 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.

2015 2016 2017 2018

#	Paper	Zitationen
1	Artificial intelligence in healthcare: past, present and future Fei Jiang, Yong Jiang, Hui Zhi et al. Stroke and Vascular Neurology	4.499
2	Deep learning for healthcare: review, opportunities and challenges Riccardo Miotto, Fei Wang, Shuang Wang et al. Briefings in Bioinformatics	2.919
3	Learning Important Features Through Propagating Activation Differences Avanti Shrikumar, Peyton Greenside, Anshul Kundaje arXiv (Cornell University)	2.367
4	Artificial intelligence in medicine Pavel Hamet, Johanne Tremblay Metabolism	2.228
5	Deep EHR: A Survey of Recent Advances in Deep Learning Techniques for Electronic Health Record (EHR) Analysis Benjamin Shickel, Patrick Tighe, Azra Bihorac et al. IEEE Journal of Biomedical and Health Informatics	1.531
6	Machine Learning and Data Mining Methods in Diabetes Research Ioannis Kavakiotis, O. Tsave, Athanasios Salifoglou et al. Computational and Structural Biotechnology Journal	1.393
7	Can machine-learning improve cardiovascular risk prediction using routine clinical data? Stephen Weng, Jenna Reps, Joe Kai et al. PLoS ONE	1.316
8	Disease Prediction by Machine Learning Over Big Data From Healthcare Communities Min Chen, Yixue Hao, Kai Hwang et al. IEEE Access	1.267
9	Artificial Intelligence in Precision Cardiovascular Medicine Chayakrit Krittanawong, HongJu Zhang, Zhen Wang et al. Journal of the American College of Cardiology	1.060
10	Clinical information extraction applications: A literature review Yanshan Wang, Liwei Wang, Majid Rastegar-Mojarad et al. Journal of Biomedical Informatics	892
11	An Interpretable Machine Learning Model for Accurate Prediction of Sepsis in the ICU Shamim Nemati, Andre L. Holder, Fereshteh Razmi et al. Critical Care Medicine	796
12	Machine Learning for Precision Psychiatry: Opportunities and Challenges Danilo Bzdok, Andreas Meyer‐Lindenberg Biological Psychiatry Cognitive Neuroscience and Neuroimaging	793
13	Artificial Intelligence in Medical Practice: The Question to the Answer? D. Douglas Miller, Eric W. Brown The American Journal of Medicine	768
14	GRAM Edward Choi, Mohammad Taha Bahadori, Le Song et al. —	647
15	What do we need to build explainable AI systems for the medical domain? Andreas Holzinger, Chris Biemann, Constantinos S. Pattichis et al. arXiv (Cornell University)	636
16	Patient Subtyping via Time-Aware LSTM Networks İnci M. Baytaş, Cao Xiao, Xi Zhang et al. —	625
17	Medical big data: promise and challenges Choong Ho Lee, Hyung‐Jin Yoon Kidney Research and Clinical Practice	591
18	What is precision medicine? Inke R. König, Oliver Fuchs, Gesine Hansen et al. European Respiratory Journal	565
19	Personalizing Session-based Recommendations with Hierarchical Recurrent Neural Networks Massimo Quadrana, Alexandros Karatzoglou, Balázs Hidasi et al. —	564
20	Machine Learning for Healthcare: On the Verge of a Major Shift in Healthcare Epidemiology Jenna Wiens, Erica S. Shenoy Clinical Infectious Diseases	553
21	Google DeepMind and healthcare in an age of algorithms Julia Powles, Hal Hodson Health and Technology	489
22	Dipole Fenglong Ma, Radha Chitta, Jing Zhou et al. —	488
23	Converging blockchain and next-generation artificial intelligence technologies to decentralize and accelerate biomedical research and healthcare Polina Mamoshina, Lucy O. Ojomoko, Yury Yanovich et al. Oncotarget	468
24	Leveraging uncertainty information from deep neural networks for disease detection Christian Leibig, Vaneeda Allken, Murat Seçkin Ayhan et al. Scientific Reports	461
25	Machine Learning and Neurosurgical Outcome Prediction: A Systematic Review Joeky T. Senders, Patrick Staples, Aditya V. Karhade et al. World Neurosurgery	458
26	Predicting healthcare trajectories from medical records: A deep learning approach Trang Pham, Truyen Tran, Dinh Phung et al. Journal of Biomedical Informatics	455
27	Multimodal Neuroimaging Feature Learning With Multimodal Stacked Deep Polynomial Networks for Diagnosis of Alzheimer's Disease Jun Shi, Zheng Xiao, Yan Li et al. IEEE Journal of Biomedical and Health Informatics	442
28	Machine Learning Methods to Predict Diabetes Complications Arianna Dagliati, Simone Marini, Lucia Sacchi et al. Journal of Diabetes Science and Technology	422
29	Learning a Health Knowledge Graph from Electronic Medical Records Maya Rotmensch, Yoni Halpern, Abdulhakim Tlimat et al. Scientific Reports	421
30	Synthea: An approach, method, and software mechanism for generating synthetic patients and the synthetic electronic health care record Jason Walonoski, M Krámer, Joseph C. Nichols et al. Journal of the American Medical Informatics Association	411
31	Real-valued (Medical) Time Series Generation with Recurrent Conditional\n GANs Cristóbal Esteban, Stephanie L. Hyland, Gunnar Rätsch arXiv (Cornell University)	383
32	Effect of a machine learning-based severe sepsis prediction algorithm on patient survival and hospital length of stay: a randomised clinical trial David Shimabukuro, Christopher W. Barton, Mitchell D. Feldman et al. BMJ Open Respiratory Research	382
33	Personal Health Records: A Systematic Literature Review Alex Roehrs, Cristiano André da Costa, Rodrigo da Rosa Righi et al. Journal of Medical Internet Research	362
34	Generating Multi-label Discrete Patient Records using Generative Adversarial Networks Edward Choi, Siddharth Biswal, Bradley Malin et al. arXiv (Cornell University)	342
35	Deep ensemble learning of sparse regression models for brain disease diagnosis Heung‐Il Suk, Seong‐Whan Lee, Dinggang Shen Medical Image Analysis	331
36	Creating an automated trigger for sepsis clinical decision support at emergency department triage using machine learning Steven Horng, David Sontag, Yoni Halpern et al. PLoS ONE	313
37	SV-RCNet: Workflow Recognition From Surgical Videos Using Recurrent Convolutional Network Yueming Jin, Qi Dou, Hao Chen et al. IEEE Transactions on Medical Imaging	312
38	Opportunities and obstacles for deep learning in biology and medicine Travers Ching, Daniel Himmelstein, Brett K. Beaulieu‐Jones et al. bioRxiv (Cold Spring Harbor Laboratory)	309
39	Causal Effect Inference with Deep Latent-Variable Models Christos Louizos, Uri Shalit, Joris M. Mooij et al. arXiv (Cornell University)	294
40	The need to approximate the use-case in clinical machine learning Sohrab Saeb, Luca Lonini, Arun Jayaraman et al. GigaScience	273
41	FearNet: Brain-Inspired Model for Incremental Learning Ronald Kemker, Christopher Kanan arXiv (Cornell University)	272
42	Real-valued (Medical) Time Series Generation with Recurrent Conditional GANs Cristóbal Esteban, Stephanie L. Hyland, Gunnar Rätsch arXiv (Cornell University)	269
43	Comparative approaches for classification of diabetes mellitus data: Machine learning paradigm Md. Maniruzzaman, Nishith Kumar, Md. Menhazul Abedin et al. Computer Methods and Programs in Biomedicine	265
44	Lost in Thought — The Limits of the Human Mind and the Future of Medicine Ziad Obermeyer, Thomas H. Lee New England Journal of Medicine	252
45	A neural joint model for entity and relation extraction from biomedical text Fei Li, Meishan Zhang, Guohong Fu et al. BMC Bioinformatics	251
46	A neural network multi-task learning approach to biomedical named entity recognition Gamal Crichton, Sampo Pyysalo, Billy Chiu et al. BMC Bioinformatics	242
47	Natural language processing to extract symptoms of severe mental illness from clinical text: the Clinical Record Interactive Search Comprehensive Data Extraction (CRIS-CODE) project Richard Jackson, Rashmi Patel, Nishamali Jayatilleke et al. BMJ Open	235
48	MMD GAN: Towards Deeper Understanding of Moment Matching Network Chunliang Li, Wei-Cheng Chang, Yu Cheng et al. arXiv (Cornell University)	231
49	Predictive analytics in health care using machine learning tools and techniques B. Nithya, V. Ilango —	221
50	Learning representations for the early detection of sepsis with deep neural networks Hye Jin Kam, Ha Young Kim Computers in Biology and Medicine	221

Top Papers: Machine Learning im Gesundheitswesen (2017)

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