Predicting the Future - Big Data, Machine Learning, and Clinical Medicine.
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TLDR
The algorithms of machine learning, which can sift through vast numbers of variables looking for combinations that reliably predict outcomes, will improve prognosis, displace much of the work of radiologists and anatomical pathologists, and improve diagnostic accuracy.Abstract:
The algorithms of machine learning, which can sift through vast numbers of variables looking for combinations that reliably predict outcomes, will improve prognosis, displace much of the work of radiologists and anatomical pathologists, and improve diagnostic accuracy.read more
Citations
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Scalable and accurate deep learning with electronic health records
Alvin Rajkomar,Alvin Rajkomar,Eyal Oren,Kai Chen,Andrew M. Dai,Nissan Hajaj,Michaela Hardt,Peter J. Liu,Xiaobing Liu,Jake Marcus,Mimi Sun,Patrik Sundberg,Hector Yee,Kun Zhang,Yi Zhang,Gerardo Flores,Gavin E. Duggan,Jamie Irvine,Quoc V. Le,Kurt Litsch,Alexander Mossin,Justin Tansuwan,De Wang,James Wexler,Jimbo Wilson,Dana Ludwig,Samuel L. Volchenboum,Katherine Chou,Michael Pearson,Srinivasan Madabushi,Nigam H. Shah,Atul J. Butte,Michael D. Howell,Claire Cui,Greg S. Corrado,Jeffrey Dean +35 more
TL;DR: A representation of patients’ entire raw EHR records based on the Fast Healthcare Interoperability Resources (FHIR) format is proposed, and it is demonstrated that deep learning methods using this representation are capable of accurately predicting multiple medical events from multiple centers without site-specific data harmonization.
Journal ArticleDOI
Artificial Intelligence in Healthcare
TL;DR: Recent breakthroughs in AI technologies and their biomedical applications are outlined, the challenges for further progress in medical AI systems are identified, and the economic, legal and social implications of AI in healthcare are summarized.
Journal ArticleDOI
Scalable and accurate deep learning for electronic health records
Alvin Rajkomar,Eyal Oren,Kai Chen,Andrew M. Dai,Nissan Hajaj,Peter J. Liu,Xiaobing Liu,Mimi Sun,Patrik Sundberg,Hector Yee,Kun Zhang,Gavin E. Duggan,Gerardo Flores,Michaela Hardt,Jamie Irvine,Quoc V. Le,Kurt Litsch,Jake Marcus,Alexander Mossin,Justin Tansuwan,De Wang,James Wexler,Jimbo Wilson,Dana Ludwig,Samuel L. Volchenboum,Katherine Chou,Michael Pearson,Srinivasan Madabushi,Nigam H. Shah,Atul J. Butte,Michael D. Howell,Claire Cui,Greg S. Corrado,Jeffrey Dean +33 more
TL;DR: In this paper, the authors proposed a representation of patients' entire, raw EHR records based on the Fast Healthcare Interoperability Resources (FHIR) format and demonstrated that deep learning methods using this representation are capable of accurately predicting multiple medical events from multiple centers without site-specific data harmonization.
Journal ArticleDOI
AACR project genie: Powering precision medicine through an international consortium
Shawn M. Sweeney,Ethan Cerami,Alexander S. Baras,Trevor J. Pugh,Nikolaus Schultz,Thomas Stricker,James Lindsay,C. Del Vecchio Fitz,Priti Kumari,Christine M. Micheel,K. Shaw,Jianjiong Gao,Nicola Moore,Cyriac Kandoth,Brendan Reardon,Eva M Lepisto,Stuart Gardos,Kristen K. Dang,Justin Guinney,Larsson Omberg,Thomas Yu,Benjamin Gross,Zachary J. Heins,David M. Hyman,Barrett J. Rollins,Charles L. Sawyers,David B. Solit,Deborah Schrag,Victor E. Velculescu,Fabrice Andre,Philippe L. Bedard,Mia Levy,Gerrit A. Meijer +32 more
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Can machine-learning improve cardiovascular risk prediction using routine clinical data?
TL;DR: In this article, the authors assessed whether machine-learning can improve cardiovascular risk prediction and found that machine learning offers an opportunity to improve accuracy by exploiting complex interactions between risk factors, which can increase the number of patients who could benefit from preventive treatment, while avoiding unnecessary treatment of others.
References
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