Timothy A. Miller

6.7k total citations
184 papers, 4.2k citations indexed

About

Timothy A. Miller is a scholar working on Artificial Intelligence, Molecular Biology and Surgery. According to data from OpenAlex, Timothy A. Miller has authored 184 papers receiving a total of 4.2k indexed citations (citations by other indexed papers that have themselves been cited), including 64 papers in Artificial Intelligence, 58 papers in Molecular Biology and 43 papers in Surgery. Recurrent topics in Timothy A. Miller's work include Topic Modeling (56 papers), Biomedical Text Mining and Ontologies (40 papers) and Machine Learning in Healthcare (27 papers). Timothy A. Miller is often cited by papers focused on Topic Modeling (56 papers), Biomedical Text Mining and Ontologies (40 papers) and Machine Learning in Healthcare (27 papers). Timothy A. Miller collaborates with scholars based in United States, Italy and Australia. Timothy A. Miller's co-authors include George H. Rudkin, Guergana Savova, Dmitriy Dligach, Dean T. Yamaguchi, Chen Lin, Steven Bethard, Weibiao Huang, Brian T. Carlsen, Ellie J. C. Goldstein and Kenji Ishida and has published in prestigious journals such as JAMA, Gastroenterology and PLoS ONE.

In The Last Decade

Timothy A. Miller

178 papers receiving 3.9k citations

Peers

Timothy A. Miller

SURGE

DERMA

Christian Gabriel Austria

John R. Davis United States

Alexander Ross Kerr United States

Alessio Signori Italy

Josef Smolle Austria

Matthew Cooper United States

Marcel F. Jonkman Netherlands

Paul M. Speight United Kingdom

Stefan Langer Germany

Chun‐Pin Chiang Taiwan

Christian Gabriel Austria

Timothy A. Miller

1.3k ×1.0

SURGE

348 ×0.3

897 ×0.9

349 ×0.6

46 ×0.1

DERMA

Citations per year, relative to Timothy A. Miller Timothy A. Miller (= 1×) peers Christian Gabriel

Countries citing papers authored by Timothy A. Miller

Since Specialization

Citations

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

Fields of papers citing papers by Timothy A. Miller

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Timothy A. Miller. 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 Timothy A. Miller. The network helps show where Timothy A. Miller may publish in the future.

Co-authorship network of co-authors of Timothy A. Miller

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

All Works

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20 of 20 papers shown

Yoon, Wonjin, Boyu Ren, Spencer A. Thomas, et al.. (2025). Aspect-Oriented Summarization for Psychiatric Short-Term Readmission Prediction. PubMed. 2025. 28025–28042.

McMurry, Andrew, Brian E. Dixon, Alon Geva, et al.. (2025). Large Language Model Symptom Identification From Clinical Text: Multicenter Study. Journal of Medical Internet Research. 27. e72984–e72984. 1 indexed citations

Gao, Yanjun, Shan Chen, Dmitriy Dligach, et al.. (2024). When Raw Data Prevails: Are Large Language Model Embeddings Effective in Numerical Data Representation for Medical Machine Learning Applications?. PubMed. 2024. 5414–5428. 1 indexed citations

Miller, Timothy A., Yanjun Gao, Matthew M. Churpek, et al.. (2024). Lessons learned on information retrieval in electronic health records: a comparison of embedding models and pooling strategies. Journal of the American Medical Informatics Association. 32(2). 357–364.

Gao, Yanjun, Ruizhe Li, Brian W. Patterson, et al.. (2024). Leveraging Medical Knowledge Graphs Into Large Language Models for Diagnosis Prediction: Design and Application Study. PubMed. 4. e58670–e58670. 15 indexed citations

Miller, Timothy A., et al.. (2024). Generalizable clinical note section identification with large language models. JAMIA Open. 7(3). ooae075–ooae075. 3 indexed citations

Yoon, Wonjin, Shan Chen, Yanjun Gao, et al.. (2024). LCD benchmark: long clinical document benchmark on mortality prediction for language models. Journal of the American Medical Informatics Association. 32(2). 285–295. 2 indexed citations

Gao, Yanjun, Shan Chen, Dmitriy Dligach, et al.. (2024). Uncertainty estimation in diagnosis generation from large language models: next-word probability is not pre-test probability. JAMIA Open. 8(1). ooae154–ooae154. 3 indexed citations

Gao, Yanjun, Dmitriy Dligach, Timothy A. Miller, & Majid Afshar. (2023). Overview of the Problem List Summarization (ProbSum) 2023 Shared Task on Summarizing Patients’ Active Diagnoses and Problems from Electronic Health Record Progress Notes. PubMed. 2023. 461–467. 8 indexed citations

10.

Guevara-Vega, Marco, Shan Chen, Shalini Moningi, et al.. (2023). Natural Language Processing Methods to Empirically Explore Social Contexts and Needs in Cancer Patient Notes. JCO Clinical Cancer Informatics. 7(7). e2200196–e2200196. 5 indexed citations

11.

Yetişgen, Meliha, et al.. (2023). Improving model transferability for clinical note section classification models using continued pretraining. Journal of the American Medical Informatics Association. 31(1). 89–97. 6 indexed citations

12.

Gao, Yanjun, Dmitriy Dligach, Timothy A. Miller, et al.. (2023). DR.BENCH: Diagnostic Reasoning Benchmark for Clinical Natural Language Processing. Journal of Biomedical Informatics. 138. 104286–104286. 12 indexed citations

13.

Wang, Lijing, et al.. (2023). A computable case definition for patients with SARS-CoV2 testing that occurred outside the hospital. JAMIA Open. 6(3). ooad047–ooad047. 1 indexed citations

14.

Miller, Timothy A., Paul Avillach, & Kenneth D. Mandl. (2020). Experiences implementing scalable, containerized, cloud-based NLP for extracting biobank participant phenotypes at scale. JAMIA Open. 3(2). 185–189. 2 indexed citations

15.

Doshi‐Velez, Finale, et al.. (2019). Unsupervised Learning of PCFGs with Normalizing Flow. 2442–2452. 17 indexed citations

16.

Miller, Timothy A., Carlo Napolitano, Silvia G. Priori, et al.. (2019). Supervised methods to extract clinical events from cardiology reports in Italian. Journal of Biomedical Informatics. 95. 103219–103219. 13 indexed citations

17.

Ren, Xiaoyan, Qi Zhou, David Foulad, et al.. (2019). Osteoprotegerin reduces osteoclast resorption activity without affecting osteogenesis on nanoparticulate mineralized collagen scaffolds. Science Advances. 5(6). eaaw4991–eaaw4991. 50 indexed citations

18.

Savova, Guergana, Eugene Tseytlin, Sean Finan, et al.. (2017). DeepPhe: A Natural Language Processing System for Extracting Cancer Phenotypes from Clinical Records. Cancer Research. 77(21). e115–e118. 65 indexed citations

19.

Dligach, Dmitriy, Timothy A. Miller, Chen Lin, Steven Bethard, & Guergana Savova. (2017). Neural Temporal Relation Extraction. 746–751. 65 indexed citations

20.

Miller, Timothy A., Dmitriy Dligach, Steven Bethard, Chen Lin, & Guergana Savova. (2017). Towards generalizable entity-centric clinical coreference resolution. Journal of Biomedical Informatics. 69. 251–258. 10 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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