Michele E. Day
Impact in
- Health Information Management top 10%
- Electronic Health Records Systems
Papers in
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- Signaling Pathways in Disease 1
- ATP Synthase and ATPases Research 1
- Protein Kinase Regulation and GTPase Signaling 1
- Biomedical Text Mining and Ontologies 1
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- Advanced Text Analysis Techniques 1
- Privacy-Preserving Technologies in Data 1
- Co-authors
- Lucila Ohno‐Machado (3 shared papers)Claudiu Farcas (3 shared papers)Xiaoqian Jiang (2 shared papers)Hyeoneui Kim (2 shared papers)Aziz A. Boxwala (2 shared papers)Michael E. Matheny (2 shared papers)Frederic S. Resnic (2 shared papers)Staal A. Vinterbo (1 shared paper)
- Journals
- Journal of the American Medical Informatics Association (2 papers)The Journal of Cell Biology (1 paper)Proceedings of the National Academy of Sciences (1 paper)PubMed (1 paper)
- Partner nations
- United States
In The Last Decade
Michele E. Day
5 papers receiving 190 citations
Peers
Comparison fields: 5 of 60
- Health Information Management 22
- Health Informatics 4
- Information Systems and Management 15
- Artificial Intelligence 63
- Molecular Biology 103
Countries citing papers authored by Michele E. Day
This map shows the geographic impact of Michele E. Day'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 Michele E. Day with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michele E. Day more than expected).
Fields of papers citing papers by Michele E. Day
This network shows the impact of papers produced by Michele E. Day. 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 Michele E. Day. The network helps show where Michele E. Day may publish in the future.
Co-authors
The 25 scholars most cited alongside Michele E. Day, 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 | 2011 | 95 | |
| 2 | 2011 | 36 | |
| 3 | 2013 | 30 | |
| 4 | Ensembles of NLP Tools for Data Element Extraction from Clinical Notes. | 2016 | 19 |
| 5 | 2015 | 15 |
About Michele E. Day
Michele E. Day is a scholar working on Molecular Biology, Artificial Intelligence, Cardiology and Cardiovascular Medicine, Health, Toxicology and Mutagenesis and Public Health, Environmental and Occupational Health, having authored 5 papers that have together received 195 indexed citations. Recurring topics across this work include Advanced Text Analysis Techniques (1 paper), Signaling Pathways in Disease (1 paper), Cardiac electrophysiology and arrhythmias (1 paper), Health Systems, Economic Evaluations, Quality of Life (1 paper), ATP Synthase and ATPases Research (1 paper), Protein Kinase Regulation and GTPase Signaling (1 paper), Biomedical Text Mining and Ontologies (1 paper) and Privacy-Preserving Technologies in Data (1 paper). The work is most often cited by research in Health Information Management (22 citations), Health Informatics (4 citations), Information Systems and Management (15 citations), Artificial Intelligence (63 citations) and Molecular Biology (103 citations). Michele E. Day has collaborated with scholars based in United States. Frequent co-authors include Lucila Ohno‐Machado, Claudiu Farcas, Xiaoqian Jiang, Hyeoneui Kim, Aziz A. Boxwala, Michael E. Matheny, Frederic S. Resnic, Staal A. Vinterbo, Brian E. Chapman and Susan S. Taylor. Their work appears in journals such as Journal of the American Medical Informatics Association, The Journal of Cell Biology, Proceedings of the National Academy of Sciences and PubMed.
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.