Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records
- Journal
- Scientific Reports
In The Last Decade
doi.org/10.1038/srep26094 →Countries where authors are citing Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records
This map shows the geographic impact of Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records more than expected).
Fields of papers citing Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records
This network shows the impact of Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records.
About Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records
This paper, published in 2016, received 988 indexed citations . Written by Riccardo Miotto, Li Li, Brian Kidd and Joel T. Dudley covering the research area of Molecular Biology, Artificial Intelligence and Health Information Management. It is primarily cited by scholars working on Artificial Intelligence (718 citations), Health Information Management (367 citations) and Molecular Biology (187 citations). Published in Scientific Reports.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.
This paper is also available at doi.org/10.1038/srep26094.