Melissa Jay
Impact in
- Health Informatics top 1%
- Artificial Intelligence in Healthcare and Education
- Family Practice top 2%
- Clinical Reasoning and Diagnostic Skills
Papers in ⓘ
-
- Sepsis Diagnosis and Treatment 7
-
- Machine Learning in Healthcare 6
- Co-authors
- Jacob Calvert (8 shared papers)Ritankar Das (8 shared papers)Uli K. Chettipally (7 shared papers)Jana Hoffman (7 shared papers)Yaniv Kerem (4 shared papers)Mitchell D. Feldman (2 shared papers)David Shimabukuro (3 shared papers)Lisa Shieh (2 shared papers)
- Journals
- Computers in Biology and Medicine (2 papers)Statistics in Medicine (2 papers)BMJ Open (1 paper)Journal of Medical Economics (1 paper)Annals of Medicine and Surgery (2 papers)
- Partner nations
- United StatesUnited Kingdom
In The Last Decade
Melissa Jay
10 papers receiving 879 citations
Hit Papers
Peers
Comparison fields: 5 of 83
- Health Informatics 101
- Family Practice 133
- Health Information Management 123
- Artificial Intelligence 597
- Epidemiology 625
Countries citing papers authored by Melissa Jay
This map shows the geographic impact of Melissa Jay's research. 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 Melissa Jay with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Melissa Jay more than expected).
Fields of papers citing papers by Melissa Jay
This network shows the impact of papers produced by Melissa Jay. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Melissa Jay. The network helps show where Melissa Jay may publish in the future.
Co-authors
The 25 scholars most cited alongside Melissa Jay, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | Prediction of Sepsis in the Intensive Care Unit With Minimal Electronic Health Record Data: A Machine Learning Approach Hit paper breakdown → | 2016 | 339 |
| 2 | 2018 | 229 | |
| 3 | 2016 | 184 | |
| 4 | 2016 | 50 | |
| 5 | 2016 | 42 | |
| 6 | 2017 | 40 | |
| 7 | 2017 | 20 | |
| 8 | 2016 | 15 | |
| 9 | 2021 | 5 | |
| 10 | 2021 | 1 | |
| 11 | 2020 | 0 |
About Melissa Jay
Melissa Jay is a scholar working on Epidemiology, Artificial Intelligence, Surgery, Statistics and Probability and Cellular and Molecular Neuroscience, having authored 11 papers that have together received 925 indexed citations. Recurring topics across this work include Sepsis Diagnosis and Treatment (7 papers), Machine Learning in Healthcare (6 papers), Hemodynamic Monitoring and Therapy (3 papers), Statistical Methods in Epidemiology (2 papers), Statistical Methods and Bayesian Inference (1 paper), Blood Pressure and Hypertension Studies (1 paper), Respiratory Support and Mechanisms (1 paper) and demographic modeling and climate adaptation (1 paper). The work is most often cited by research in Health Informatics (101 citations), Family Practice (133 citations), Health Information Management (123 citations), Artificial Intelligence (597 citations) and Epidemiology (625 citations). Melissa Jay has collaborated with scholars based in United States and United Kingdom. Frequent co-authors include Jacob Calvert, Ritankar Das, Uli K. Chettipally, Jana Hoffman, Yaniv Kerem, Mitchell D. Feldman, David Shimabukuro, Lisa Shieh, Thomas Desautels and Qingqing Mao. Their work appears in journals such as Computers in Biology and Medicine, Statistics in Medicine, BMJ Open, Journal of Medical Economics and Annals of Medicine and Surgery.
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.