Deep patient: An unsupervised representation to predict the future of patients from the electronic health records

Miotto, Riccardo, Li, Li, Kidd, Brian A., Dudley, Joel T.·2016·Scientific Reports

Cited by (1,005)

Items 101–200 of 1,005

  1. MISSING DATA IMPUTATION IN THE ELECTRONIC HEALTH RECORD USING DEEPLY LEARNED AUTOENCODERS
    Brett K. Beaulieu‐Jones, Jason H. Moore · 2016 · Pacific Symposium on Biocomputing (PSB)
  2. Transparency in Complex Computational Systems
    Kathleen Creel · 2020 · Philosophy of Science
  3. Deep Learning Intervention for Health Care Challenges: Some Biomedical Domain Considerations
    Tobore, Igbe, Li, Jingzhen, Yuhang, Liu, Al-Handarish, Yousef, Kandwal, Abhishek, Nie, Zedong, Wang, Lei · 2019 · JMIR mHealth and uHealth
  4. Digital diabetes: Perspectives for diabetes prevention, management and research
    Guy Fagherazzi, Philippe Ravaud · 2018 · Diabetes & Metabolism
  5. Deep Learning for Natural Language Processing in Radiology—Fundamentals and a Systematic Review
    Vera Sorin, Yiftach Barash, Eli Konen, Eyal Klang · 2020 · Journal of the American College of Radiology
  6. Autoencoders
    Walter Hugo Lopez Pinaya, Sandra Vieira, Rafael Garcia‐Dias, Andrea Mechelli · 2019 · Elsevier eBooks
  7. Deep learning in pharmacogenomics: from gene regulation to patient stratification
    Kalinin, Alexandr A, Higgins, Gerald A, Reamaroon, Narathip, Soroushmehr, Sayedmohammadreza, Allyn-Feuer, Ari, Dinov, Ivo D, Najarian, Kayvan, Athey, Brian D · 2018 · Pharmacogenomics
  8. MiME: Multilevel Medical Embedding of Electronic Health Records for Predictive Healthcare
    Edward Choi, Cao Xiao, Walter F. Stewart, Jimeng Sun · 2018 · arXiv (Cornell University)
  9. Deep learning in omics: a survey and guideline
    Zhiqiang Zhang, Yi Chen Zhao, Xiangke Liao, Wenqiang Shi, Kenli Li, Quan Zou, Shaoliang Peng · 2018 · Briefings in Functional Genomics
  10. PREDICTIVE MODELING OF HOSPITAL READMISSION RATES USING ELECTRONIC MEDICAL RECORD-WIDE MACHINE LEARNING: A CASE-STUDY USING MOUNT SINAI HEART FAILURE COHORT
    Khader Shameer, Kipp W. Johnson, Alexandre Yahi, Riccardo Miotto, Li Li, Doran Ricks, Jebakumar Jebakaran, Patricia Kovatch, Partho P. Sengupta, SENGUPTA GELIJNS et al. · 2016 · Pacific Symposium on Biocomputing (PSB)
  11. State‐of‐the‐Art Machine Learning Techniques Aiming to Improve Patient Outcomes Pertaining to the Cardiovascular System
    Rahul K. Sevakula, Wan-Tai Michael Au-Yeung, Jagmeet P. Singh, Edwin Kevin Heist, Eric M. Isselbacher, Antonis A. Armoundas · 2020 · Journal of the American Heart Association
  12. Cardiovascular care with digital twin technology in the era of generative artificial intelligence
    Phyllis Thangaraj, S. Benson, Evangelos K. Oikonomou, Folkert Wouter Asselbergs, Rohan Khera · 2024 · European Heart Journal
  13. Connecting Systems, Data, and People: A Multidisciplinary Research Roadmap for Chronic Disease Management
    Indranil R. Bardhan, Hsinchun Chen, Elena Karahanna · 2020 · MIS Quarterly
  14. Intelligent Health Care: Applications of Deep Learning in Computational Medicine
    Sijie Yang, Fei Zhu, Xinghong Ling, Quan Liu, Peiyao Zhao · 2021 · Frontiers in Genetics
  15. Predicting the early risk of chronic kidney disease in patients with diabetes using real-world data
    Ravizza, Stefan, Huschto, Tony, Adamov, Anja, Böhm, Lars, Büsser, Alexander, Flöther, Frederik F., Hinzmann, Rolf, König, Helena, McAhren, Scott M., Robertson, Daniel H. et al. · 2019 · Nature Medicine
  16. A Survey on an Emerging Area: Deep Learning for Smart City Data
    Chen, Qi, Wang, Wei, Wu, Fangyu, De, Suparna, Wang, Ruili, Zhang, Bailing, Huang, Xin · 2019 · IEEE Transactions on Emerging Topics in Computational Intelligence
  17. Precision Psychiatry Applications with Pharmacogenomics: Artificial Intelligence and Machine Learning Approaches
    Eugene Lin, Chieh‐Hsin Lin, Hsien‐Yuan Lane · 2020 · International Journal of Molecular Sciences
  18. Multimodal data integration for oncology in the era of deep neural networks: a review
    Waqas, Asim, Tripathi, Aakash, Ramachandran, Ravi P., Stewart, Paul A., Rasool, Ghulam · 2024 · Frontiers in Artificial Intelligence
  19. Machine learning and deep learning methods that use omics data for metastasis prediction
    Somayah Albaradei, Maha A. Thafar, Asim Alsaedi, Christophe Van Neste, Takashi Gojobori, Magbubah Essack, Xin Gao · 2021 · Computational and Structural Biotechnology Journal
  20. Doctor XAI
    Cecilia Panigutti, Alan Perotti, Dino Pedreschi · 2020 · ACM Conference on Fairness, Accountability, and Transparency (FAccT)
  21. AI applications in robotics, diagnostic image analysis and precision medicine: Current limitations, future trends, guidelines on CAD systems for medicine
    Tetiana Habuza, Alramzana Nujum Navaz, Faiza Hashim, Fady Alnajjar, Nazar Zaki, Mohamed Adel Serhani, Yauhen A. Statsenko · 2021 · Informatics in Medicine Unlocked
  22. 2017 Roadmap for Innovation—ACC Health Policy Statement on Healthcare Transformation in the Era of Digital Health, Big Data, and Precision Health
    Sanjeev P. Bhavnani, Kapil Parakh, Ashish Atreja, Regina S. Druz, Garth Nigel Graham, Salim S. Hayek, Harlan M. Krumholz, Thomas M. Maddox, Maulik D. Majmudar, John S. Rumsfeld et al. · 2017 · Journal of the American College of Cardiology
  23. TransformEHR: transformer-based encoder-decoder generative model to enhance prediction of disease outcomes using electronic health records
    Yang, Zhichao, Mitra, Avijit, Liu, Weisong, Berlowitz, Dan, Yu, Hong · 2023 · Nature Communications
  24. Deep learning for temporal data representation in electronic health records: A systematic review of challenges and methodologies
    Feng Xie, Han Yuan, Yilin Ning, Marcus Eng Hock Ong, Mengling Feng, Wynne Hsu, Bibhas Chakraborty, Nan Liu · 2021 · Journal of Biomedical Informatics
  25. Boosting Deep Learning Risk Prediction with Generative Adversarial Networks for Electronic Health Records
    Che, Zhengping, Cheng, Yu, Zhai, Shuangfei, Sun, Zhaonan, Liu, Yan · 2017 · 2017 IEEE International Conference on Data Mining (ICDM)
  26. Patient similarity for precision medicine: A systematic review
    Enea Parimbelli, Simone Marini, Lucia Sacchi, Riccardo Bellazzi · 2018 · Journal of Biomedical Informatics
  27. Variational autoencoders for cancer data integration: design principles and computational practice
    Simidjievski, Nikola, Bodnar, Cristian, Tariq, Ifrah, Scherer, Paul, Andres Terre, Helena, Shams, Zohreh, Jamnik, Mateja, Liò, Pietro · 2019 · Frontiers in Genetics
  28. Unsupervised machine learning for the discovery of latent disease clusters and patient subgroups using electronic health records
    Yanshan Wang, Yiqing Zhao, Terry M. Therneau, Elizabeth J. Atkinson, Ahmad Pahlavan Tafti, Nan Zhang, Shreyasee Amin, ANDREW HAR LIMPER, Sundeep Khosla, Hongfang Liu · 2019 · Journal of Biomedical Informatics
  29. Data-driven subtyping of Parkinson's disease using longitudinal clinical records: a cohort study
    Zhang, Xi, Chou, Jingyuan, Liang, Jian, Xiao, Cao, Zhao, Yize, Sarva, Harini, Henchcliffe, Claire, Wang, Fei · 2019 · Scientific Reports
  30. Big data and medical research in China
    Zhang, Luxia, Wang, Haibo, Li, Quanzheng, Zhao, Ming-Hui, Zhan, Qi-Min · 2018 · BMJ
  31. Clinical big data and deep learning: Applications, challenges, and future outlooks
    Ying Yu, Min Li, Liangliang Liu, Yaohang Li, Jianxin Wang · 2019 · Big Data Mining and Analytics
  32. RAIM: Recurrent Attentive and Intensive Model of Multimodal Patient Monitoring Data
    Xu, Yanbo, Biswal, Siddharth, Deshpande, Shriprasad R., Maher, Kevin O., Sun, Jimeng · 2018 · Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
  33. GRAM: Graph-based Attention Model for Healthcare Representation Learning
    Edward Choi, Mohammad Taha Bahadori, Le Song, Walter F. Stewart, Jimeng Sun · 2016 · arXiv (Cornell University)
  34. Machine Learning and Smart Devices for Diabetes Management: Systematic Review
    Makroum, Mohammed Amine, Adda, Mehdi, Bouzouane, Abdenour, Ibrahim, Hussein · 2022 · Sensors
  35. 3D deep convolutional neural networks for amino acid environment similarity analysis
    Torng, Wen, Altman, Russ B. · 2017 · BMC Bioinformatics
  36. Artificial intelligence and primary care research: a scoping review
    Kueper, Jacqueline K., Terry, Amanda L., Zwarenstein, Merrick, Lizotte, Daniel J. · 2020 · The Annals of Family Medicine
  37. Predicting the onset of type 2 diabetes using wide and deep learning with electronic health records
    Nguyen, Binh P., Pham, Hung N., Tran, Hop, Nghiem, Nhung, Nguyen, Quang H., Do, Trang T.T., Tran, Cao Truong, Simpson, Colin R. · 2019 · Computer Methods and Programs in Biomedicine
  38. Multi-disease prediction using LSTM recurrent neural networks
    Lu Men, Noyan Ilk, Xinlin Tang, Yuan Liu · 2021 · Expert Systems with Applications
  39. Role of artificial intelligence in revolutionizing drug discovery
    Ashfaq Ur Rehman, Mingyu Li, Binjian Wu, Yasir Ali, Salman Rasheed, Sana Shaheen, Xinyi Liu, Ray Luo, Jian Zhang · 2024 · Fundamental Research
  40. Predictive Modeling of the Progression of Alzheimer’s Disease with Recurrent Neural Networks
    Tingyan Wang, Robin G. Qiu, Ming Yang Yu · 2018 · Scientific Reports
  41. Multimodal autoencoder: A deep learning approach to filling in missing sensor data and enabling better mood prediction
    Natasha Jaques, Sara Taylor, Akane Sano, Rosalind W. Picard · 2017 · International Conference on Affective Computing and Intelligent Interaction and workshops
  42. Multi-Label Classification of Patient Notes a Case Study on ICD Code Assignment
    Tal Baumel, Jumana Nassour-Kassis, Cohen, Raphael, Elhadad, Michael, Elhadad, No`emie · 2017 · arXiv (Cornell University)
  43. Language models are an effective representation learning technique for electronic health record data
    Steinberg, Ethan, Jung, Ken, Fries, Jason A., Corbin, Conor K., Pfohl, Stephen R., Shah, Nigam H. · 2021 · Journal of Biomedical Informatics
  44. Liver fibrosis classification based on transfer learning and FCNet for ultrasound images
    Dan Meng, Libo Zhang, Guitao Cao, Wenming Cao, Guixu Zhang, Bing Hu · 2017 · IEEE Access
  45. An Overview of Artificial Intelligence in Oncology
    Eduardo Moreno Júdice de Mattos Farina, Jacqueline Justino Nabhen, Maria Inez Dacoregio, Felipe Batalini, Fábio Ynoe de Moraes · 2022 · Future Science OA
  46. A comprehensive review of machine learning algorithms and their application in geriatric medicine: present and future
    Richard John Woodman, Arduino Aleksander Mangoni · 2023 · Aging Clinical and Experimental Research
  47. Automated Machine Learning for Healthcare and Clinical Notes Analysis
    Akram Mustafa, Mostafa Rahimi Azghadi · 2021 · Computers
  48. Interpretable filter based convolutional neural network (IF-CNN) for glucose prediction and classification using PD-SS algorithm
    R. Kamalraj, Subramani Neelakandan, Ranjith Kumar Manoharan, V. Chandra Shekhar Rao, Rohit Anand, Harinder Singh · 2021 · Measurement
  49. The Secondary Use of Electronic Health Records for Data Mining: Data Characteristics and Challenges
    Tabinda Sarwar, Sattar Seifollahi, Jeffrey Chan, Xiuzhen Zhang, Vural Aksakallı, Irene L. Hudson, Karin M. Verspoor, Lawrence Cavedon · 2022 · ACM Computing Surveys
  50. AI in patient flow: applications of artificial intelligence to improve patient flow in NHS acute mental health inpatient units
    Fatema Mustansir Dawoodbhoy, Jack Delaney, Paulina Cecula, Jiakun Yu, Iain Peacock, Joseph S. Tan, Benita M. Cox · 2021 · Heliyon
  51. Learning for Personalized Medicine: A Comprehensive Review From a Deep Learning Perspective
    Zhang, Sushen, Bamakan, Seyed Mojtaba Hosseini, Qu, Qiang, Li, Sha · 2019 · IEEE Reviews in Biomedical Engineering
  52. An RNN Architecture with Dynamic Temporal Matching for Personalized Predictions of Parkinson's Disease
    Chao Che, Cao Xiao, Jian Liang, Bo Jin, Jiayu Zho, Fei Wang · 2017 · Society for Industrial and Applied Mathematics eBooks
  53. The virtual doctor: An interactive clinical-decision-support system based on deep learning for non-invasive prediction of diabetes
    Sebastian Spänig, Agnes Emberger‐Klein, Jan‐Peter Sowa, Ali E. Canbay, Klaus Menrad, Dominik Heider · 2019 · Artificial Intelligence in Medicine
  54. Machine and deep learning for longitudinal biomedical data: a review of methods and applications
    Anna Cascarano, Jordi Mur-Petit, Jerónimo Hernández-González, Marina Camacho, Nina de Toro Eadie, Polyxeni Gkontra, Marc Chadeau‐Hyam, Jordi M. Vitria, Karim Lekadir · 2023 · Artificial Intelligence Review
  55. Predicting post-stroke pneumonia using deep neural network approaches
    Yanqiu Ge, Qinghua Wang, Li Wang, Honghu Wu, Peng Chen, Jiajing Wang, Yuan Xu, Gang Xiong, Yaoyun Zhang, Yingping Yi · 2019 · International Journal of Medical Informatics
  56. The use of electronic health records for psychiatric phenotyping and genomics
    Jordan W. Smoller · 2017 · American Journal of Medical Genetics Part B Neuropsychiatric Genetics
  57. A Tour of Unsupervised Deep Learning for Medical Image Analysis
    Khalid Raza, Nripendra Kumar Singh · 2021 · Current Medical Imaging Formerly Current Medical Imaging Reviews
  58. Deep learning in biomedical data science
    Baldi, Pierre · 2018 · Annual Review of Biomedical Data Science
  59. CNN-Based Health Model for Regular Health Factors Analysis in Internet-of-Medical Things Environment
    Walaa Nagy Ismail, Mohammad Mehedi Hassan, Hessah A. Alsalamah, Giancarlo Fortino · 2020 · IEEE Access
  60. Promises and perils of artificial intelligence in dentistry
    Farhana Pethani · 2020 · Australian Dental Journal
  61. Progress in non-invasive detection of liver fibrosis
    Li Chengxi, Rentao Li, Wei Zhang · 2018 · Cancer Biology and Medicine
  62. Comparison between logistic regression and machine learning algorithms on survival prediction of traumatic brain injuries
    Jinzhou Feng, Yu Wang, Jin Peng, Mingwei Sun, Jun Zeng, Hua Jiang · 2019 · Journal of Critical Care
  63. CODER: Knowledge-infused cross-lingual medical term embedding for term normalization
    Zheng Peng Yuan, Zhengyun Zhao, Haixia Sun, Jiao Li, Fei Wang, Sheng Yu · 2022 · Journal of Biomedical Informatics
  64. Assessing the utility of deep neural networks in predicting postoperative surgical complications: a retrospective study
    Alexander Bonde, Kartik M. Varadarajan, Nicholas Bonde, Anders Troelsen, Orhun Kamil Muratoglu, Henrik Malchau, Anthony D. Yang, Hasan Badre Alam, Martin Sillesen · 2021 · The Lancet Digital Health
  65. Machine Learning Techniques for Biomedical Natural Language Processing: A Comprehensive Review
    Essam Halim Houssein, Rehab E. Mohamed, Abdelmgeid Amin Ali · 2021 · IEEE Access
  66. Use of deep learning to develop continuous-risk models for adverse event prediction from electronic health records
    Nenad Tomašev, Natalie Harris, Sebastien Baur, Anne Mottram, Xavier Glorot, Jack W. Rae, Michal Zielinski, Harry Askham, Andre Saraiva, Valerio Magliulo et al. · 2021 · Nature Protocols
  67. Application of Artificial Intelligence on Psychological Interventions and Diagnosis: An Overview
    Sijia Zhou, Jingping Zhao, Lulu Zhang · 2022 · Frontiers in Psychiatry
  68. Two-stage Federated Phenotyping and Patient Representation Learning
    Dianbo Liu, Dmitriy Dligach, Timothy A. Miller · 2019 · BioNLP Workshop and Shared Task (BioNLP)
  69. Architectures and accuracy of artificial neural network for disease classification from omics data
    Hui Yu, David C. Samuels, Ying‐Yong Zhao, Yan Ping Guo · 2019 · BMC Genomics
  70. Ethics of AI in Pathology
    Chhavi Chauhan, Rama R. Gullapalli · 2021 · American Journal Of Pathology
  71. Computational biology: deep learning
    William Jones, Kaur Alasoo, Dmytro Fishman, Leopold Parts · 2017 · Emerging Topics in Life Sciences
  72. Towards electronic health record-based medical knowledge graph construction, completion, and applications: A literature study
    Murali, Lino, Gopakumar, G., Viswanathan, Daleesha M., Nedungadi, Prema · 2023 · Journal of Biomedical Informatics
  73. Interpretable Predictions of Clinical Outcomes with An Attention-based Recurrent Neural Network
    Ying Sha, May Dongmei Wang · 2017 · ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB)
  74. AI and Big Data in Healthcare: Towards a More Comprehensive Research Framework for Multimorbidity
    Ljiljana Trtica Majnarić, František Babič, Shane O’Sullivan, Andreas Holzinger · 2021 · Journal of Clinical Medicine
  75. Precision Medicine for Heart Failure with Preserved Ejection Fraction: An Overview
    Sanjiv Jayendra Shah · 2017 · Journal of Cardiovascular Translational Research
  76. Artificial Intelligence to Decode Cancer Mechanism: Beyond Patient Stratification for Precision Oncology
    Sandip Kumar Patel, Bhawana George, Vineeta Rai · 2020 · Frontiers in Pharmacology
  77. Preserving differential privacy in convolutional deep belief networks
    NhatHai Phan, Xintao Wu, Dejing Dou · 2017 · Machine Learning
  78. Public attitudes value interpretability but prioritize accuracy in Artificial Intelligence
    Anne-Marie Nußberger, Lan Luo, L. Elisa Celis, Molly J. Crockett · 2022 · Nature Communications
  79. The Revival of the Notes Field: Leveraging the Unstructured Content in Electronic Health Records
    Michela Assale, Linda Greta Dui, Andrea Cina, Andrea Seveso, Federico Cabitza · 2019 · Frontiers in Medicine
  80. Current applications of big data and machine learning in cardiology.
    Renato Cuocolo, Teresa Perillo, Eliana De Rosa, Lorenzo Ugga, Mario Petretta · 2019 · PubMed Central
  81. Deep Learning Application Pros And Cons Over Algorithm
    Ata Jahangir Moshayedi, Atanu Shuvam Roy, Amin Kolahdooz, Yang Shuxin · 2022 · EAI Endorsed Transactions on AI and Robotics
  82. Reviewing methods of deep learning for intelligent healthcare systems in genomics and biomedicine
    Imran Zafar, Shakila Anwar, Faheem kanwal, Waqas Yousaf, Fakhar Un Nisa, Tanzeela Kausar, Qurat ul Ain, Ahsanullah Unar, Mohammad Amjad Kamal, Summya Rashid et al. · 2023 · Biomedical Signal Processing and Control
  83. Predicting opioid dependence from electronic health records with machine learning
    Randall J. Ellis, Zichen Wang, Nicholas G. Genes, Avi Ma’ayan · 2019 · BioData Mining
  84. Non-obstructive azoospermia: current and future perspectives
    Tharu Tharakan, Rong Luo, Channa Nalin Jayasena, Suks Minhas · 2021 · Faculty Reviews
  85. CLOCS: Contrastive Learning of Cardiac Signals Across Space, Time, and Patients
    Dani Kiyasseh, Tingting Zhu, David A. Clifton · 2020 · arXiv (Cornell University)
  86. Synthetic data in biomedicine via generative artificial intelligence
    Boris van Breugel, Tennison Liu, Dino Oglić, Mihaela van der Schaar · 2024 · Nature Reviews Bioengineering
  87. Unsupervised Extraction of Stable Expression Signatures from Public Compendia with an Ensemble of Neural Networks
    Jie Tan, Georgia Doing, Kimberley A. Lewis, Courtney E. Price, Kathleen Chen, Kyle C. Cady, Barret Perchuk, Michael T. Laub, Deborah A. Hogan, Casey S. Greene · 2017 · Cell Systems
  88. Going deep into schizophrenia with artificial intelligence
    Jose Cortes-Briones, Nicolas I. Tapia, Deepak Cyril D’Souza, P. A. Estévez · 2021 · Schizophrenia Research
  89. Scale-up of Digital Innovations in Health Care: Expert Commentary on Enablers and Barriers
    Hannes Schlieter, Lisa A. Marsch, Diane Whitehouse, Lena Otto, Ana Rita M. Londral, Gisbert Wilhelm Teepe, Martin Benedict, Joseph Ollier, Tom Ulmer, Nathalie Gasser et al. · 2021 · Journal of Medical Internet Research
  90. Democratizing EHR analyses with FIDDLE: a flexible data-driven preprocessing pipeline for structured clinical data
    Shengpu Tang, Parmida Davarmanesh, Yanmeng Song, Danai Koutra, Michael W. Sjoding, Jenna Wiens · 2020 · Journal of the American Medical Informatics Association
  91. Ensemble Multifeatured Deep Learning Models and Applications: A Survey
    Satheesh Abimannan, El-Sayed M. El-Alfy, Yue‐Shan Chang, Shahid Hussain, Saurabh Shukla, Dhivyadharsini Satheesh · 2023 · IEEE Access
  92. Benchmarking Deep Learning Architectures for Predicting Readmission to the ICU and Describing Patients-at-Risk
    Barbieri, Sebastiano, Kemp, James, Perez-Concha, Oscar, Kotwal, Sradha, Gallagher, Martin, Ritchie, Angus, Jorm, Louisa · 2020 · Scientific Reports
  93. Predicting dementia with routine care EMR data
    Zina Ben Miled, Kyle Haas, Christopher Matthew Black, Rezaul Karim Khandker, Vasu Chandrasekaran, Richard B. Lipton, Malaz A. Boustani · 2019 · Artificial Intelligence in Medicine
  94. Decoding disease: from genomes to networks to phenotypes
    Aaron K. Wong, Rachel S. G. Sealfon, Chandra L. Theesfeld, Olga G. Troyanskaya · 2021 · Nature Reviews Genetics
  95. Machine learning to support social media empowered patients in cancer care and cancer treatment decisions
    De Silva, Daswin, Ranasinghe, Weranja, Bandaragoda, Tharindu, Adikari, Achini, Mills, Nishan, Iddamalgoda, Lahiru, Alahakoon, Damminda, Lawrentschuk, Nathan, Persad, Raj, Osipov, Evgeny et al. · 2018 · PLOS ONE
  96. Digital Biomarkers for the Early Detection of Mild Cognitive Impairment: Artificial Intelligence Meets Virtual Reality
    Silvia Cavedoni, Alice Chirico, Elisa Pedroli, Pietro Cipresso, Giuseppe Riva · 2020 · Frontiers in Human Neuroscience
  97. Predicting Inpatient Flow at a Major Hospital Using Interpretable Analytics
    Bertsimas, Dimitris, Pauphilet, Jean, Stevens, Jennifer, Tandon, Manu · 2022 · Manufacturing & Service Operations Management
  98. Machine Learning Methods in Health Economics and Outcomes Research—The PALISADE Checklist: A Good Practices Report of an ISPOR Task Force
    William V. Padula, Noémi Kreif, David J. Vanness, Blythe J. S. Adamson, J.D. Rueda, Federico Felizzi, Pall F. Jonsson, Maarten J. IJzerman, Atul Janardhan Butte, William H. Crown · 2022 · Value in Health
  99. The importance of resource awareness in artificial intelligence for healthcare
    Zhenge Jia, Jianxu Chen, Xiaowei Xu, John N. Kheir, Jingtong Hu, Han Xiao, Sui Peng, Xiaobo Sharon Hu, Danny Chen, Yiyu Shi · 2023 · Nature Machine Intelligence
  100. Distributed machine learning cloud teleophthalmology IoT for predicting AMD disease progression
    Arun Das, Paul Rad, Kim‐Kwang Raymond Choo, Babak Nouhi, Jonathan Lish, Joseph R. Martel · 2018 · Future Generation Computer Systems