Modi Liu

631 citations
7 papers · 377 · h-index 6

Impact in

Papers in

Modi Liu

7 papers receiving 369 citations

Peers

Modi Liu
Comparison fields: 5 of 87
  • Health Informatics 57
  • Health Information Management 84
  • Physical Therapy, Sports Therapy and Rehabilitation 29
  • Geriatrics and Gerontology 21
  • Artificial Intelligence 119
Replace Minjie Xia with:
Minjie Xia China
Frank Stearns United States
Shaun T Alfreds United States
Le Zheng China
Jennifer H. Garvin United States
Gina Barnes United States
Oliver Wang United States
Anurag Garikipati United States
Andoni Beristain Spain
Megan E. Salwei United States
Modi Liu relative to Minjie Xia China Minjie Xia's profile →
Citations per field
00.5×1.5×
Minjie Xia · 1×
Citations per year

Countries citing papers authored by Modi Liu

Since Specialization
Citations

This map shows the geographic impact of Modi Liu'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 Modi Liu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Modi Liu more than expected).

Fields of papers citing papers by Modi Liu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Modi Liu. 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 Modi Liu. The network helps show where Modi Liu may publish in the future.

Co-authors

The 25 scholars most cited alongside Modi Liu, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Modi Liu Line = papers co-authored together Modi Liu links everyone, so they are left out of the graph.

All Works

7 of 7 papers shown
#Work
1 2018171
2 202064
3 202058
4 201938
5 201935
6 20217
7
A Deep-Learning Based Prediction of Pancreatic Adenocarcinoma with Electronic Health Records from the State of Maine
20204

About Modi Liu

Modi Liu is a scholar working on Artificial Intelligence, Epidemiology, Cardiology and Cardiovascular Medicine, Emergency Medicine and Social Psychology, having authored 7 papers that have together received 377 indexed citations. Recurring topics across this work include Machine Learning in Healthcare (5 papers), Chronic Disease Management Strategies (2 papers), Cardiovascular Health and Risk Factors (1 paper), Lung Cancer Diagnosis and Treatment (1 paper), Artificial Intelligence in Healthcare (1 paper), Balance, Gait, and Falls Prevention (1 paper), Heart Failure Treatment and Management (1 paper) and Emergency and Acute Care Studies (1 paper). The work is most often cited by research in Health Informatics (57 citations), Health Information Management (84 citations), Physical Therapy, Sports Therapy and Rehabilitation (29 citations), Geriatrics and Gerontology (21 citations) and Artificial Intelligence (119 citations). Modi Liu has collaborated with scholars based in China, United States and Taiwan. Frequent co-authors include Minjie Xia, Karl G. Sylvester, Eric Widen, Shiying Hao, Xuefeng B. Ling, Doff B. McElhinney, Chengyin Ye, Frank Stearns, Bo Jin and Oliver Wang. Their work appears in journals such as Journal of Medical Internet Research, Translational Psychiatry, PLoS ONE, International Journal of Medical Informatics and International journal of medical and health sciences.

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.

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