Malcolm Pradhan
Impact in
- Internal Medicine top 5%
- Venous Thromboembolism Diagnosis and Management
- Artificial Intelligence top 10%
- Bayesian Modeling and Causal Inference
- AI-based Problem Solving and Planning
Papers in
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- Bayesian Modeling and Causal Inference 3
- Machine Learning in Healthcare 2
-
- Genetic factors in colorectal cancer 2
- Co-authors
- Max Henrion (2 shared papers)Gregory Provan (2 shared papers)Gelareh Farshid (6 shared papers)Michael JR Edmonds (2 shared papers)Blackford Middleton (1 shared paper)W. B. Runciman (2 shared papers)Sharon W. Weiss (1 shared paper)John R. Goldblum (1 shared paper)
- Journals
- Cancer (2 papers)Pathology (2 papers)Artificial Intelligence (1 paper)Best practice & research. Clinical anaesthesiology (1 paper)The Medical Journal of Australia (1 paper)
- Partner nations
- AustraliaUnited StatesBarbados
In The Last Decade
Malcolm Pradhan
15 papers receiving 507 citations
Peers
Comparison fields: 5 of 100
- Internal Medicine 62
- Artificial Intelligence 188
- Cancer Research 72
- Health Information Management 20
- Medical Laboratory Technology 6
Countries citing papers authored by Malcolm Pradhan
This map shows the geographic impact of Malcolm Pradhan'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 Malcolm Pradhan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Malcolm Pradhan more than expected).
Fields of papers citing papers by Malcolm Pradhan
This network shows the impact of papers produced by Malcolm Pradhan. 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 Malcolm Pradhan. The network helps show where Malcolm Pradhan may publish in the future.
Co-authors
The 23 scholars most cited alongside Malcolm Pradhan, 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 | 1994 | 123 | |
| 2 | 2002 | 109 | |
| 3 | 1996 | 106 | |
| 4 | 2004 | 99 | |
| 5 | 2004 | 42 | |
| 6 | 2000 | 33 | |
| 7 | 2001 | 19 | |
| 8 | 2003 | 17 | |
| 9 | 2000 | 6 | |
| 10 | 2023 | 3 | |
| 11 | 2013 | 3 | |
| 12 | 2024 | 2 | |
| 13 | 2024 | 1 | |
| 14 | 2010 | 1 | |
| 15 | 2000 | 1 | |
| 16 | 2024 | 0 | |
| 17 | 2025 | 0 |
About Malcolm Pradhan
Malcolm Pradhan is a scholar working on Artificial Intelligence, Pathology and Forensic Medicine, Oncology, Health Information Management and Cancer Research, having authored 17 papers that have together received 565 indexed citations. Recurring topics across this work include Breast Cancer Treatment Studies (3 papers), Bayesian Modeling and Causal Inference (3 papers), Machine Learning in Healthcare (2 papers), Cancer Diagnosis and Treatment (2 papers), Electronic Health Records Systems (2 papers), Genetic factors in colorectal cancer (2 papers), PI3K/AKT/mTOR signaling in cancer (2 papers) and Diabetes Management and Research (1 paper). The work is most often cited by research in Internal Medicine (62 citations), Artificial Intelligence (188 citations), Cancer Research (72 citations), Health Information Management (20 citations) and Medical Laboratory Technology (6 citations). Malcolm Pradhan has collaborated with scholars based in Australia, United States and Barbados. Frequent co-authors include Max Henrion, Gregory Provan, Gelareh Farshid, Michael JR Edmonds, Blackford Middleton, W. B. Runciman, Sharon W. Weiss, John R. Goldblum, James Kollias and P. Grantley Gill. Their work appears in journals such as Cancer, Pathology, Artificial Intelligence, Best practice & research. Clinical anaesthesiology and The Medical Journal of Australia.
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.