Melissa Jay
Impact in
- Health Informatics top 2%
- Artificial Intelligence in Healthcare and Education
- Family Practice top 2%
- Clinical Reasoning and Diagnostic Skills
Papers in
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- Sepsis Diagnosis and Treatment 6
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- Statistical Methods and Bayesian Inference 1
- Advanced Causal Inference Techniques 1
- Statistical Methods and Inference 1
- Co-authors
- Ritankar Das (8 shared papers)Jacob Calvert (8 shared papers)Jana Hoffman (7 shared papers)Uli K. Chettipally (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)Journal of Medical Economics (1 paper)BMJ Open (1 paper)Annals of Medicine and Surgery (2 papers)
- Partner nations
- United StatesUnited Kingdom
In The Last Decade
Melissa Jay
10 papers receiving 929 citations
Melissa Jay's Hit Papers
Peers
Comparison fields: 5 of 79
- Health Informatics 80
- Family Practice 81
- Epidemiology 596
- Health Information Management 87
- Artificial Intelligence 411
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 | 362 |
| 2 | 2018 | 247 | |
| 3 | 2016 | 193 | |
| 4 | 2016 | 54 | |
| 5 | 2016 | 43 | |
| 6 | 2017 | 41 | |
| 7 | 2017 | 23 | |
| 8 | 2016 | 15 | |
| 9 | 2021 | 5 | |
| 10 | 2021 | 1 | |
| 11 | 2020 | 0 |
About Melissa Jay
Melissa Jay is a scholar working on Epidemiology, Statistics and Probability, Cellular and Molecular Neuroscience, Neurology and Artificial Intelligence, having authored 11 papers that have together received 984 indexed citations. Recurring topics across this work include Sepsis Diagnosis and Treatment (6 papers), demographic modeling and climate adaptation (1 paper), Statistical Methods and Bayesian Inference (1 paper), Advanced Causal Inference Techniques (1 paper), Healthcare Policy and Management (1 paper), Fibromyalgia and Chronic Fatigue Syndrome Research (1 paper), Insurance, Mortality, Demography, Risk Management (1 paper) and Statistical Methods and Inference (1 paper). The work is most often cited by research in Health Informatics (80 citations), Family Practice (81 citations), Epidemiology (596 citations), Health Information Management (87 citations) and Artificial Intelligence (411 citations). Melissa Jay has collaborated with scholars based in United States and United Kingdom. Frequent co-authors include Ritankar Das, Jacob Calvert, Jana Hoffman, Uli K. Chettipally, 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, Journal of Medical Economics, BMJ Open 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.