Michael Shwe
Impact in
-
- Electronic Health Records Systems
- Artificial Intelligence top 5%
- Bayesian Modeling and Causal Inference
- AI-based Problem Solving and Planning
- Machine Learning in Healthcare
- Machine Learning and Algorithms
Papers in
-
- Machine Learning in Healthcare 5
- Bayesian Modeling and Causal Inference 4
- Speech Recognition and Synthesis 1
- Semantic Web and Ontologies 1
-
- Biomedical Text Mining and Ontologies 4
- Co-authors
- Gregory F. Cooper (3 shared papers)Blackford Middleton (3 shared papers)David Heckerman (3 shared papers)Eric Horvitz (3 shared papers)Harold P. Lehmann (2 shared papers)Max Henrion (2 shared papers)Lawrence M. Fagan (1 shared paper)Samson W. Tu (1 shared paper)
- Journals
- PubMed (3 papers)Computers and Biomedical Research (1 paper)PubMed Central (2 papers)Uncertainty in Artificial Intelligence (1 paper)
- Partner nations
- United StatesNetherlands
In The Last Decade
Michael Shwe
7 papers receiving 298 citations
Peers
Comparison fields: 5 of 70
- Health Information Management 59
- Artificial Intelligence 252
- Family Practice 11
- Software 21
- Management Science and Operations Research 47
Countries citing papers authored by Michael Shwe
This map shows the geographic impact of Michael Shwe'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 Michael Shwe with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michael Shwe more than expected).
Fields of papers citing papers by Michael Shwe
This network shows the impact of papers produced by Michael Shwe. 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 Michael Shwe. The network helps show where Michael Shwe may publish in the future.
Co-authors
The 9 scholars most cited alongside Michael Shwe, 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 | Probabilistic diagnosis using a reformulation of the INTERNIST-1/QMR knowledge base. I. The probabilistic model and inference algorithms. | 1991 | 235 |
| 2 | 1991 | 45 | |
| 3 | Validating the knowledge base of a therapy planning system. | 1989 | 24 |
| 4 | Reuse of knowledge represented in the Arden syntax. | 1992 | 19 |
| 5 | A Probabilistic Reformulation of the Quick Medical Reference System | 1990 | 10 |
| 6 | 1993 | 6 | |
| 7 | Handsfree Decision Support: Toward a Non-invasive Human-Computer Interface*. | 1995 | 4 |
About Michael Shwe
Michael Shwe is a scholar working on Artificial Intelligence, Molecular Biology, Health Information Management, Statistics and Probability and Infectious Diseases, having authored 7 papers that have together received 343 indexed citations. Recurring topics across this work include Machine Learning in Healthcare (5 papers), Biomedical Text Mining and Ontologies (4 papers), Bayesian Modeling and Causal Inference (4 papers), Speech Recognition and Synthesis (1 paper), Electronic Health Records Systems (1 paper), Medical Coding and Health Information (1 paper), Statistical Methods and Inference (1 paper) and Semantic Web and Ontologies (1 paper). The work is most often cited by research in Health Information Management (59 citations), Artificial Intelligence (252 citations), Family Practice (11 citations), Software (21 citations) and Management Science and Operations Research (47 citations). Michael Shwe has collaborated with scholars based in United States and Netherlands. Frequent co-authors include Gregory F. Cooper, Blackford Middleton, David Heckerman, Eric Horvitz, Harold P. Lehmann, Max Henrion, Lawrence M. Fagan, Samson W. Tu and Walter Sujansky. Their work appears in journals such as PubMed, Computers and Biomedical Research, PubMed Central and Uncertainty in Artificial Intelligence.
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.