David C. Kale
Impact in
- Health Informatics top 0.5%
- Artificial Intelligence in Healthcare and Education
- Health Information Management top 0.5%
- Artificial Intelligence in Healthcare
Papers in
-
- Machine Learning in Healthcare 5
- Machine Learning and Algorithms 2
- Data Stream Mining Techniques 2
- Advanced Clustering Algorithms Research 1
-
- Time Series Analysis and Forecasting 5
- Co-authors
- Yan Liu (6 shared papers)Kenneth Jung (2 shared papers)Pilar N. Ossorio (1 shared paper)Finale Doshi‐Velez (1 shared paper)Katherine Heller (1 shared paper)Sonoo Thadaney-Israni (1 shared paper)Jenna Wiens (1 shared paper)Marzyeh Ghassemi (1 shared paper)
- Journals
- Nature Medicine (1 paper)Journal of Community Health (1 paper)Veterinary Surgery (1 paper)PubMed (2 papers)International Conference on Machine Learning (1 paper)
- Partner nations
- United StatesCanada
In The Last Decade
David C. Kale
16 papers receiving 948 citations
David C. Kale's Hit Papers
Peers
Comparison fields: 5 of 124
- Health Informatics 241
- Health Information Management 147
- Artificial Intelligence 420
- Family Practice 15
- Signal Processing 80
Countries citing papers authored by David C. Kale
This map shows the geographic impact of David C. Kale'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 David C. Kale with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David C. Kale more than expected).
Fields of papers citing papers by David C. Kale
This network shows the impact of papers produced by David C. Kale. 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 David C. Kale. The network helps show where David C. Kale may publish in the future.
Co-authors
The 25 scholars most cited alongside David C. Kale, 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 | Do no harm: a roadmap for responsible machine learning for health care Hit paper breakdown → | 2019 | 543 |
| 2 | 2015 | 162 | |
| 3 | 2012 | 85 | |
| 4 | 2014 | 60 | |
| 5 | 2014 | 22 | |
| 6 | 2013 | 21 | |
| 7 | 2013 | 19 | |
| 8 | Functional Subspace Clustering with Application to Time Series | 2015 | 15 |
| 9 | Causal Phenotype Discovery via Deep Networks. | 2015 | 15 |
| 10 | 2015 | 11 | |
| 11 | 2017 | 10 | |
| 12 | Sim•TwentyFive: an interactive visualization system for data-driven decision support. | 2012 | 10 |
| 13 | The Effectiveness of Transfer Learning in Electronic Health Records Data | 2017 | 5 |
| 14 | 2011 | 4 | |
| 15 | 2009 | 3 | |
| 16 | 2012 | 1 |
About David C. Kale
David C. Kale is a scholar working on Artificial Intelligence, Signal Processing, Computer Vision and Pattern Recognition, Molecular Biology and General Health Professions, having authored 16 papers that have together received 986 indexed citations. Recurring topics across this work include Machine Learning in Healthcare (5 papers), Time Series Analysis and Forecasting (5 papers), Machine Learning and Algorithms (2 papers), Electronic Health Records Systems (2 papers), Data Stream Mining Techniques (2 papers), Biomedical Text Mining and Ontologies (2 papers), Advanced Clustering Algorithms Research (1 paper) and Respiratory Support and Mechanisms (1 paper). The work is most often cited by research in Health Informatics (241 citations), Health Information Management (147 citations), Artificial Intelligence (420 citations), Family Practice (15 citations) and Signal Processing (80 citations). David C. Kale has collaborated with scholars based in United States and Canada. Frequent co-authors include Yan Liu, Kenneth Jung, Pilar N. Ossorio, Finale Doshi‐Velez, Katherine Heller, Sonoo Thadaney-Israni, Jenna Wiens, Marzyeh Ghassemi, Suchi Saria and Mark Sendak. Their work appears in journals such as Nature Medicine, Journal of Community Health, Veterinary Surgery, PubMed and International Conference on Machine Learning.
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.