Modi Liu

631 total citations
7 papers, 377 citations indexed

About

Modi Liu is a scholar working on Artificial Intelligence, Epidemiology and Cardiology and Cardiovascular Medicine. According to data from OpenAlex, Modi Liu has authored 7 papers receiving a total of 377 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Artificial Intelligence, 3 papers in Epidemiology and 2 papers in Cardiology and Cardiovascular Medicine. Recurrent topics in Modi Liu's work include Machine Learning in Healthcare (5 papers), Chronic Disease Management Strategies (2 papers) and Pancreatic and Hepatic Oncology Research (1 paper). Modi Liu is often cited by papers focused on Machine Learning in Healthcare (5 papers), Chronic Disease Management Strategies (2 papers) and Pancreatic and Hepatic Oncology Research (1 paper). Modi Liu collaborates with scholars based in China, United States and Germany. Modi Liu's co-authors include Shiying Hao, Xuefeng B. Ling, Minjie Xia, Doff B. McElhinney, Eric Widen, Karl G. Sylvester, Chengyin Ye, Frank Stearns, Bo Jin and Oliver Wang and has published in prestigious journals such as PLoS ONE, Journal of Medical Internet Research and Translational Psychiatry.

In The Last Decade

Modi Liu

7 papers receiving 369 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Modi Liu China	6	119	84	83	79	57	7	377
Minjie Xia China	7	123 1.0×	84 1.0×	83 1.0×	79 1.0×	57 1.0×	9	392
Frank Stearns United States	11	158 1.3×	117 1.4×	116 1.4×	148 1.9×	58 1.0×	14	579
Le Zheng China	8	77 0.6×	48 0.6×	33 0.4×	74 0.9×	29 0.5×	12	291
Jennifer H. Garvin United States	14	177 1.5×	108 1.3×	73 0.9×	71 0.9×	28 0.5×	58	592
Oliver Wang United States	7	113 0.9×	73 0.9×	78 0.9×	60 0.8×	45 0.8×	13	363
Shaun T Alfreds United States	8	134 1.1×	101 1.2×	103 1.2×	117 1.5×	52 0.9×	12	455
Anthony Lin United States	7	99 0.8×	43 0.5×	38 0.5×	93 1.2×	82 1.4×	16	433
Evan Sholle United States	15	135 1.1×	105 1.3×	53 0.6×	85 1.1×	50 0.9×	40	543
Gina Barnes United States	13	129 1.1×	51 0.6×	44 0.5×	104 1.3×	78 1.4×	31	486
Sara Golas United States	9	100 0.8×	85 1.0×	93 1.1×	71 0.9×	37 0.6×	16	345

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-authorship network of co-authors of Modi Liu

This figure shows the co-authorship network connecting the top 25 collaborators of Modi Liu. A scholar is included among the top collaborators of Modi Liu based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Modi Liu. Modi Liu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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7 of 7 papers shown

1.

Duong, Son Q., Le Zheng, Minjie Xia, et al.. (2021). Identification of patients at risk of new onset heart failure: Utilizing a large statewide health information exchange to train and validate a risk prediction model. PLoS ONE. 16(12). e0260885–e0260885. 7 indexed citations

2.

Li, Xiaodong, Shiying Hao, Xuefeng B. Ling, et al.. (2020). A Deep-Learning Based Prediction of Pancreatic Adenocarcinoma with Electronic Health Records from the State of Maine. International journal of medical and health sciences. 14(11). 358–365. 4 indexed citations

3.

Ye, Chengyin, Jinmei Li, Shiying Hao, et al.. (2020). Identification of elders at higher risk for fall with statewide electronic health records and a machine learning algorithm. International Journal of Medical Informatics. 137. 104105–104105. 64 indexed citations

4.

Zheng, Le, Oliver Wang, Shiying Hao, et al.. (2020). Development of an early-warning system for high-risk patients for suicide attempt using deep learning and electronic health records. Translational Psychiatry. 10(1). 72–72. 58 indexed citations

5.

Zhang, Yan, Shiying Hao, Le Zheng, et al.. (2019). Prediction of the 1-Year Risk of Incident Lung Cancer: Prospective Study Using Electronic Health Records from the State of Maine. Journal of Medical Internet Research. 21(5). e13260–e13260. 35 indexed citations

6.

Ye, Chengyin, Oliver Wang, Modi Liu, et al.. (2019). A Real-Time Early Warning System for Monitoring Inpatient Mortality Risk: Prospective Study Using Electronic Medical Record Data. Journal of Medical Internet Research. 21(7). e13719–e13719. 38 indexed citations

7.

Ye, Chengyin, Tianyun Fu, Shiying Hao, et al.. (2018). Prediction of Incident Hypertension Within the Next Year: Prospective Study Using Statewide Electronic Health Records and Machine Learning. Journal of Medical Internet Research. 20(1). e22–e22. 171 indexed citations

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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