Jennifer J. Liang

463 total citations
14 papers, 250 citations indexed

About

Jennifer J. Liang is a scholar working on Artificial Intelligence, Molecular Biology and Health Information Management. According to data from OpenAlex, Jennifer J. Liang has authored 14 papers receiving a total of 250 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Artificial Intelligence, 8 papers in Molecular Biology and 5 papers in Health Information Management. Recurrent topics in Jennifer J. Liang's work include Topic Modeling (10 papers), Biomedical Text Mining and Ontologies (8 papers) and Machine Learning in Healthcare (7 papers). Jennifer J. Liang is often cited by papers focused on Topic Modeling (10 papers), Biomedical Text Mining and Ontologies (8 papers) and Machine Learning in Healthcare (7 papers). Jennifer J. Liang collaborates with scholars based in United States and Taiwan. Jennifer J. Liang's co-authors include Preethi Raghavan, Anusri Pampari, Jian Peng, Murthy Devarakonda, Neil Mehta, Amy S. Nowacki, John Prager, J. Eric Jelovsek, Özlem Uzuner and Peter Szolovits and has published in prestigious journals such as International Journal of Medical Informatics, Journal of Biomedical Informatics and JMIR Medical Informatics.

In The Last Decade

Jennifer J. Liang

14 papers receiving 240 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Jennifer J. Liang United States	9	190	96	41	39	17	14	250
Stefan Hegselmann Germany	6	132 0.7×	43 0.4×	29 0.7×	53 1.4×	29 1.7×	18	233
Kevin J. Peterson United States	7	132 0.7×	91 0.9×	44 1.1×	28 0.7×	11 0.6×	20	204
Sam Henry United States	6	241 1.3×	181 1.9×	28 0.7×	31 0.8×	8 0.5×	6	320
Karlie R. Sharma United States	4	84 0.4×	64 0.7×	25 0.6×	84 2.2×	36 2.1×	7	268
Laura Stoutenborough United States	8	189 1.0×	148 1.5×	67 1.6×	11 0.3×	10 0.6×	10	336
Guangzhi Xiong United States	4	108 0.6×	41 0.4×	13 0.3×	34 0.9×	6 0.4×	7	161
Isabella C. Wiest Germany	9	125 0.7×	40 0.4×	19 0.5×	135 3.5×	73 4.3×	23	291
Ziyang Xu United States	5	128 0.7×	31 0.3×	20 0.5×	122 3.1×	54 3.2×	11	256
Rebecka Weegar Sweden	8	164 0.9×	82 0.9×	32 0.8×	22 0.6×	19 1.1×	23	259

Countries citing papers authored by Jennifer J. Liang

Since Specialization

Citations

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

Fields of papers citing papers by Jennifer J. Liang

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jennifer J. Liang. 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 Jennifer J. Liang. The network helps show where Jennifer J. Liang may publish in the future.

Co-authorship network of co-authors of Jennifer J. Liang

This figure shows the co-authorship network connecting the top 25 collaborators of Jennifer J. Liang. A scholar is included among the top collaborators of Jennifer J. Liang 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 Jennifer J. Liang. Jennifer J. Liang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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14 of 14 papers shown

Lybarger, Kevin, et al.. (2023). Extracting medication changes in clinical narratives using pre-trained language models. Journal of Biomedical Informatics. 139. 104302–104302. 8 indexed citations

Liang, Jennifer J., et al.. (2023). Overview of the 2022 n2c2 shared task on contextualized medication event extraction in clinical notes. Journal of Biomedical Informatics. 144. 104432–104432. 9 indexed citations

Raghavan, Preethi, et al.. (2021). emrKBQA: A Clinical Knowledge-Base Question Answering Dataset. 64–73. 14 indexed citations

Liang, Jennifer J., et al.. (2021). Reducing Physicians' Cognitive Load During Chart Review: A Problem-Oriented Summary of the Patient Electronic Record.. PubMed. 2021. 763–772. 6 indexed citations

Liang, Jennifer J., et al.. (2021). IBMResearch at MEDIQA 2021: Toward Improving Factual Correctness of Radiology Report Abstractive Summarization. 302–310. 5 indexed citations

Liang, Jennifer J., et al.. (2020). Extraction of Information Related to Drug Safety Surveillance From Electronic Health Record Notes: Joint Modeling of Entities and Relations Using Knowledge-Aware Neural Attentive Models. JMIR Medical Informatics. 8(7). e18417–e18417. 17 indexed citations

Liang, Jennifer J., et al.. (2020). Timely and Efficient AI Insights on EHR: System Design.. PubMed. 2020. 1180–1189. 8 indexed citations

Liang, Jennifer J., et al.. (2020). Identification of Semantically Similar Sentences in Clinical Notes: Iterative Intermediate Training Using Multi-Task Learning. JMIR Medical Informatics. 8(11). e22508–e22508. 18 indexed citations

Liang, Jennifer J., et al.. (2019). A Novel System for Extractive Clinical Note Summarization using. 46–54. 26 indexed citations

10.

Pampari, Anusri, Preethi Raghavan, Jennifer J. Liang, & Jian Peng. (2018). emrQA: A Large Corpus for Question Answering on Electronic Medical Records. 2357–2368. 87 indexed citations

11.

Prager, John, Jennifer J. Liang, & Murthy Devarakonda. (2017). SemanticFind: Locating What You Want in a Patient Record, Not Just What You Ask For.. PubMed. 2017. 249–258. 5 indexed citations

12.

Devarakonda, Murthy, et al.. (2017). Automated problem list generation and physicians perspective from a pilot study. International Journal of Medical Informatics. 105. 121–129. 31 indexed citations

13.

Liang, Jennifer J., et al.. (2017). Ground Truth Creation for Complex Clinical NLP Tasks - an Iterative Vetting Approach and Lessons Learned.. PubMed. 2017. 203–212. 10 indexed citations

14.

Devarakonda, Murthy, et al.. (2015). Toward Generating Domain-Specific / Personalized Problem Lists from Electronic Medical Records.. National Conference on Artificial Intelligence. 66–69. 6 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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