Thomas A. Lasko
Impact in
- Health Informatics top 0.5%
- Artificial Intelligence in Healthcare and Education
- Health Information Management top 0.2%
- Artificial Intelligence in Healthcare
- Electronic Health Records Systems
Papers in
-
- Electronic Health Records Systems 4
- Co-authors
- Lucila Ohno‐MachadoJui G. BhagwatKelly H. ZouJoshua C. DennyMichael E. MathenySharon E. DavisMia LevyGuanhua Chen
- Journals
- Journal of the American Medical Informatics Association (11 papers)Journal of Biomedical Informatics (3 papers)PLoS ONE (3 papers)Applied Clinical Informatics (2 papers)Computers in Biology and Medicine (2 papers)
- Partner nations
- United StatesGermanyIreland
In The Last Decade
Thomas A. Lasko
53 papers receiving 2.3k citations
Hit Papers
Peers
Comparison fields: 5 of 179
- Health Informatics 232
- Health Information Management 347
- Artificial Intelligence 857
- Family Practice 32
- Computational Mathematics 8
Countries citing papers authored by Thomas A. Lasko
This map shows the geographic impact of Thomas A. Lasko'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 Thomas A. Lasko with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Thomas A. Lasko more than expected).
Fields of papers citing papers by Thomas A. Lasko
This network shows the impact of papers produced by Thomas A. Lasko. 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 Thomas A. Lasko. The network helps show where Thomas A. Lasko may publish in the future.
Co-authors
The 25 scholars most cited alongside Thomas A. Lasko, 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 | 2026 | 0 | |
| 2 | 2025 | 0 | |
| 3 | 2024 | 15 | |
| 4 | 2024 | 3 | |
| 5 | 2024 | 0 | |
| 6 | 2024 | 2 | |
| 7 | 2023 | 7 | |
| 8 | 2023 | 33 | |
| 9 | 2023 | 1 | |
| 10 | 2023 | 8 | |
| 11 | 2023 | 9 | |
| 12 | 2022 | 12 | |
| 13 | 2020 | 74 | |
| 14 | Predicting Medications from Diagnostic Codes with Recurrent Neural Networks | 2017 | 25 |
| 15 | 2017 | 2 | |
| 16 | 2013 | 21 | |
| 17 | 2013 | 55 | |
| 18 | 2013 | 79 | |
| 19 | 2006 | 47 | |
| 20 | The use of receiver operating characteristic curves in biomedical informatics Hit paper breakdown → | 2005 | 660 |
About Thomas A. Lasko
Thomas A. Lasko is a scholar working on Health Informatics, Health Information Management, Toxicology, Artificial Intelligence and Geriatrics and Gerontology, having authored 57 papers that have together received 2.4k indexed citations. Recurring topics across this work include Machine Learning in Healthcare (18 papers), Biomedical Text Mining and Ontologies (8 papers), Lung Cancer Diagnosis and Treatment (7 papers), Radiomics and Machine Learning in Medical Imaging (5 papers), Statistical Methods in Clinical Trials (4 papers), Electronic Health Records Systems (4 papers), Sepsis Diagnosis and Treatment (4 papers) and Metabolomics and Mass Spectrometry Studies (3 papers). The work is most often cited by research in Health Informatics (232 citations), Health Information Management (347 citations), Artificial Intelligence (857 citations), Family Practice (32 citations) and Computational Mathematics (8 citations). Thomas A. Lasko has collaborated with scholars based in United States, Germany and Ireland. Frequent co-authors include Lucila Ohno‐Machado, Jui G. Bhagwat, Kelly H. Zou, Joshua C. Denny, Michael E. Matheny, Sharon E. Davis, Mia Levy, Guanhua Chen, Hua Xu and Colin G. Walsh. Their work appears in journals such as Journal of the American Medical Informatics Association, Journal of Biomedical Informatics, PLoS ONE, Applied Clinical Informatics and Computers in Biology and Medicine.
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.