About Us
Stable Diffusion generated image
 

The CAIR lab is dedicated to investigating applications of computational linguistics, artificial intelligence and cognitive science methods in healthcare. We work on projects aimed at characterizing and capturing the effects of psychoactive medications and neurodegenerative disease on cognition as well as developing novel computational methods, tools and infrastructure for collecting and analyzing time-series data including speech, language, and physiological signals.  

People

Faculty

Serguei VS Pakhomov, PhD (Professor, College of Pharmacy)

Michael Kotlyar, PharmD (Professor, College of Pharmacy)

Martin Michalowski, PhD, FACMI (Associate Professor, School of Nursing)

Maria Gini, PhD (Professor, Computer Science and Engineering)

Current Doctorate Students

Jacob Solinsky (BICB Program)

Hang Yu (BICB Program)

Zhecheng Sheng (IHI program)

Han Yang (IHI program)

Current Masters Students

Jithendra Kagathi (Data Science program)

Joseph Lisk (CSE Robotics program)

Staff

Angel Sandriepe, Project Manager

Sheena Dufresne, Study Coordinator

 

 

Projects

Current Projects

  • Developing Data-Driven Clinical Signatures for People Who Experience Hallucinations
    • Funding: NIH/NIMH (U01MH135901-01 Ben-Zeev and Cohen PIs)
    • Our team proposes to collect data from a large sample of people who experience hallucinations using smartphone behavioral measurement tools. With the aid of innovative computational modelling strategies, these data will be used to develop “clinical signatures” that indicate which individuals are at heightened risk for severe outcomes such as hospitalization and suicide. University of Minnesota is a subcontractor on this project responsible for developing an informatics platform for remote data collection via smartphones and analysis.
  • Improving safety and access to immune effector cell therapy with artificial intelligence technology
    • Funding: NIH/NCI (R21CA286980)
    • The project aims to determine the feasibility of using an AI based conversational agent system to detect early signs of the cognitive side-effects of immune effector cell therapy (ICANS)
  • Feasibility of Using a Culturally Tailored Conversational Agent for promoting smoking cessation treatment utilization in African Americans who use cigarettes
    • Funding: NIH (P50MD017342-03S1: Allen - PI)
    • The goal of the project is to develop and pilot test a novel Personal Assistant for Smoking Cessation with Artificial Intelligence and Large Language Models (PASCAL) for managing smoking triggers.
  • Survival Since Listing: A New Patient-centered Metric for Transplant Center Report Cards
    • Funding: AHRQ (R01HS028829)
    • This is a subcontract on a project aimed at developing an intelligent chatbot to help patients who are looking for organ transplant centers by gathering search criteria from them using natural language interaction and helping them understand the search results
  • Natural language conversational agent technology for MnCHOICES support services
    • Funding: Minnesota Department of Human Services
    • The long-term objective of the project is to develop a conversational agent system and infrastructure to support training of DHS personnel in conducting the assessment of needs for social services under the MnCHOICES program.
  • DeconDTN: Deconfounding Semantic Coherence for Automated Dementia Diagnosis (Supplement)
    • Funding: National Library of Medicine (R01LM014056-02S1)
    • In our recently awarded R01, we are developing methods to confound deep learning models for natural language processing, to correct for biases emerging from recognition of the source (provenance) of data in multi-institutional datasets. In this supplement, we will use methods developed in the parent award to reduce the bias of models for detection of dementia on the basis of spoken language that occur on account of differential responses to dialectical variants associated with ethnicity.
  • DeconDTN: Deconfounding Deep Transformer Networks for Clinical NLP
    • Funding: National Library of Medicine (R01LM011563)
    • In the proposed research we will develop a systematic approach to Deconfounding Deep Transformer Networks (DeconDTN), embodied in an eponymous and publicly available set of open source tools for (1) identification of data provenance-related biases, (2) mitigation of these biases using a novel set of validated methods, and (3) systematic evaluation of the resulting effects on model performance.

Completed Projects

  • Computerized Assessment of Linguistic Indicators of Lucidity in Alzheimer's Disease Dementia
    • Funding: National Institute on Aging (R21AG069792)
    • The proposed project aims to develop automated linguistic methods for assessing speech of individuals in various stages of dementia in order to enable detection and characterization of lucid episodes in individuals otherwise thought to have lost that ability.
  • Feasibility of Using Wearable Technology for Just-in-Time Prediction of Smoking Lapses
    • Funding: National Institute on Drug Abuse (R21DA049446)
    • The goal of this proposed project proposed project is to develop and validate methods for using wearable devices to predict episodes of smoking.
  • Open Health Natural Language Processing Collaboratory
    • Funding: National Center for Advancing Translational Sciences (U01TR002062)
    • The proposed project aims to broaden the secondary use of electronic health records (EHRs) across the research community by combining innovative privacy-preserving computing techniques and clinical natural language processing.
  • Improving Health and Wellbeing With Personalized, Pervasive Technology
    • Funding: University of Minnesota Grand Challenges Initiative
    • This Grand Challenge research project focuses on improving the health, wellbeing, and independence of individuals with reduced capacity due to illness or advanced age through conversational voice assistants with wearable sensors and smart-textile clothing technology to provide real time, in-home, unobtrusive sensing and actuation solutions.
Resources

Publicly Available Datasets

Semantic Relatedness and Similarity Reference Standards

Clinical Abbreviations Sense Inventory and Data

Packages and Toolkits

UMLS::Similarity and UMLS::Relatedness

TRESTLE: Reproducibility Toolkit

GPT-8 Generative Polypod Transformer

CLAS: Computerized Linguistic Analysis System

NLP-ADAPT: Natural Language Processing Artifact Discovery and Preparation Toolkit

NLP-ADAPT: Kubernetes

ProTK: Prosody Toolkit and Demo at INTERSPEECH 2012

VSC: Virtual Study Coordinator Scripts

HammerNet: Code for manipulating GPT-2 weights

ParadoxASR: Code for examining impact of ASR errors on downstream processing

ANR: Code for Artificial Neural Reserve testing

CREDIT Criteria Worksheet

Note: This is a spreadsheet that contains 14 CREDIT criteria for determining authorship in accordance with ICMJE guidelines. Many journals require authorship contribution statements. This spreadsheet is meant to help a group of authors determine the order of authors on the paper byline and compile a standardized authorship contribution statement.

CREDIT Spreadsheet

Publications

2025

 

  1. Morrell, P.L. and Pakhomov, SVS. (2025). Decoding nature’s grammar with DNA language models, Proc. Natl. Acad. Sci. U.S.A.122 (29) e2512889122, https://doi.org/10.1073/pnas.2512889122.
  2. Yang, H., Yu, H., Kotlyar, M., Dufresne, S., Pakhomov, S. (2025). Relative importance of temporal and location features in predicting smoking events. npj Digit. Med. 8, 409. https://doi.org/10.1038/s41746-025-01799-5
  3. Pakhomov, S, Solinsky, J. Olson, C., Stuckey, R., Michalowski, M., Petersen, A., Bachanova, V. (2025). "ANNA: Automated Neural Nursing Assistant (interim feasibility analysis)". Poster at Pacific Symposium for Biocomputing. January 2025. (pdf)
  4. Ding X, Sheng Z, Hur B, Tauscher J, Ben-Zeev D, Yetişgen M, Pakhomov S, Cohen T. Tailoring task arithmetic to address bias in models trained on multi-institutional datasets. J Biomed Inform. 2025 Aug;168:104858. doi: 10.1016/j.jbi.2025.104858. Epub 2025 Jun 8. PMID: 40494422; PMCID: PMC12282651.
  5. Changye Li, Weizhe Xu, Serguei Pakhomov, Ellen Bradley, Dror Ben-Zeev, and Trevor Cohen. 2025. Bigger But Not Better: Small Neural Language Models Outperform LLMs in Detection of Thought Disorder. In Proceedings of the 10th Workshop on Computational Linguistics and Clinical Psychology (CLPsych 2025), pages 90–105, Albuquerque, New Mexico. Association for Computational Linguistics.
  6. Ma, S., Wang, Y., Wagner, J. et al. Predicting accrual success for better clinical trial resource allocation. Sci Rep 15, 3879 (2025). https://doi.org/10.1038/s41598-025-88400-x
  7. Cohen T, Xu W, Guo Y, Pakhomov S, Leroy G. Coherence and comprehensibility: Large language models predict lay understanding of health-related content. J Biomed Inform. 2025 Jan;161:104758. doi: 10.1016/j.jbi.2024.104758. Epub 2024 Dec 9. PMID: 39662650; PMCID: PMC12243615.

2024

 

  1. Cohen, T., Pakhomov, S., Paullada, A., Yetisgen, M. (2024). Text Classification. In: Xu, H., Demner Fushman, D. (eds) Natural Language Processing in Biomedicine. Cognitive Informatics in Biomedicine and Healthcare. Springer, Cham. https://doi.org/10.1007/978-3-031-55865-8_7
  2. Li., C., Sheng, Z., Cohen, T., Pakhomov, S. (2024). Too Big to Fail: Larger Language Models are Disproportionately Resilient to Induction of Dementia-Related Linguistic Anomalies. Preprint: https://arxiv.org/abs/2406.02830, Accepted to ACL 2024 findings
  3. Yu H, Kotlyar M, Thuras P, Dufresne S, Pakhomov SV. Towards Predicting Smoking Events for Just-in-time Interventions. AMIA Jt Summits Transl Sci Proc. 2024 May 31;2024:468-477. PMID: 38827079; PMCID: PMC11141818.
  4. Li C, Solinsky J, Cohen T, Pakhomov S. A curious case of retrogenesis in language: Automated analysis of language patterns observed in dementia patients and young children. Neurosci Inform. 2024 Mar;4(1):100155. doi: 10.1016/j.neuri.2023.100155. Epub 2023 Dec 21. PMID: 38433986; PMCID: PMC10907010.

 

2023

 

  1. Li, C., Solinsky, J., Cohen, T., Pakhomov, S. (2023). A Curious Case of Retrogenesis in Language: Automated Analysis of Language Patterns Observed in Dementia Patients and Young Children. Neuroscience Informatics. 4(1): 100155
  2. Li, C., Xu, W., Cohen, T., Pakhomov, S. (2023). Useful Blunders: Can Automated Speech Recognition Errors Improve Downstream Dementia Classification? Journal of Biomedical Informatics. 150: 104598
  3. Pakhomov, SVS., Solinsky, J., Michalowski, M., Bachanova, V. (2024). A Conversational Agent for Early Detection of Neurotoxic Effects of Medications through Automated Intensive Observation. In Proc. Pacific Symposium for Biocomputing (PSB-2024).
  4. Ding, X., Sheng, Z. Yetişgen, M., Pakhomov, SVS., Cohen, T. (2023). Backdoor Adjustment of Confounding by Provenance for Robust Text Classification of Multi-institutional Clinical Notes. Accepted in AMIA 2023 Annual Symposium.Preprint: arXiv:2310.02451.
  5. Wang, W., Xu, W., Chander, A., Nepal, S., Buck, B., Pakhomov, SVS., Cohen, T., Ben-Zeev, D. and Campbell, A. (2023). The Power of Speech in the Wild: Discriminative Power of Daily Voice Diaries in Understanding Auditory Verbal Hallucinations Using Deep Learning. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 7, 3, Article 133 (September 2023), 29 pages. https://doi.org/10.1145/3610890.
  6. Sheng, Z. Finzel, R., Lucke, M., Dufresne, S. Gini, M. and Pakhomov, S. (2023). A Dialogue System for Assessing Activities of Daily Living: Improving Consistency with Grounded Knowledge. Accepted to ACL 2023 DialDoc Workshop. Preprint: arXiv:2307.07544.
  7. Li, C., Xu, W., Cohen, T., Michalowski, M., Pakhomov, S. (2023). TRESTLE: Toolkit for Reproducible Execution of Speech, Text and Language Experiments. AMIA Joint Summits Proceedings. 2023 Jun 16;2023:360-369. PMID: 37350929.
  8. Li, C., Cohen, T., Pakhomov, S. (2023). The Far Side of Failure: Investigating the Impact of Speech Recognition Errors on Subsequent Dementia Classification. Accepted as extended abstract for ML4H 2022. Preprint: arXiv:2211.07430.
  9. Yu H, Kotlyar M, Dufresne S, Thuras P, Pakhomov S. Feasibility of Using an Armband Optical Heart Rate Sensor in Naturalistic Environment. Pac Symp Biocomput. 2023;28:43-54. PMID: 36540963.
  10. Zaslavsky O, Cohen T, Wu KC, Jin J, Chien SY, Pakhomov S. PROTOCOLS FOR COLLECTING CONVERSATIONAL LANGUAGE IN PERSONS WITH DEMENTIA. Innov Aging. 2023 Dec 21;7(Suppl 1):746–7. doi: 10.1093/geroni/igad104.2415. PMCID: PMC10737208.

 

2022

 

  1. Li, C., Knopman, D., Xu, W., Cohen, T. and Pakhomov, SVS. (2022). GPT-D: Inducing Dementia-related Linguistic Anomalies by Deliberate Degradation of Artificial Neural Language Models. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1866–1877, Dublin, Ireland. Association for Computational Linguistics.
  2. Singh, E., Bompelli, A., Wan, R. Bian, J., Pakhomov, SVS & Rui Zhang. A conversational agent system for dietary supplements use. BMC Med Inform Decis Mak 22, 153 (2022). https://doi.org/10.1186/s12911-022-01888-5.
  3. Datar, S. et al. (2022). Measuring Physiological Markers of Stress During Conversational Agent Interactions. In: Shaban-Nejad, A., Michalowski, M., Bianco, S. (eds) AI for Disease Surveillance and Pandemic Intelligence. W3PHAI 2021. Studies in Computational Intelligence, vol 1013. Springer, Cham. https://doi.org/10.1007/978-3-030-93080-6_18
  4. Abdelwahab N, Ingraham NE, Nguyen N, Siegel L, Silverman G, Sahoo HS, Pakhomov S, Morse LR, Billings J, Usher MG, Melnik TE, Tignanelli CJ, Ikramuddin F. Predictors of Postacute Sequelae of COVID-19 Development and Rehabilitation: A Retrospective Study. Arch Phys Med Rehabil. 2022 May 13:S0003-9993(22)00397-5. doi: 10.1016/j.apmr.2022.04.009. Epub ahead of print. PMID: 35569640.
  5. Xu W, Wang W, Portanova J, Chander A, Campbell A, Pakhomov S, Ben-Zeev D, Cohen T. Fully automated detection of formal thought disorder with Time-series Augmented Representations for Detection of Incoherent Speech (TARDIS). J Biomed Inform. 2022 Feb;126:103998. doi: 10.1016/j.jbi.2022.103998. Epub 2022 Jan 19. PMID: 35063668.
  6. Changye Li, David Knopman, Weizhe Xu, Trevor Cohen, and Serguei Pakhomov. 2022. GPT-D: Inducing Dementia-related Linguistic Anomalies by Deliberate Degradation of Artificial Neural Language Models. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1866–1877, Dublin, Ireland. Association for Computational Linguistics.

 

2021

 

  1. Silverman, G.M., Sahoo, H.S., Ingraham, N.E., Lupei, M.I., Puskarich, M.A., Usher, M., Dries, J.V., Finzel, R.L., Murray, E., Sartori, J., Simon, G.J., Zhang, R., Melton, G.B., Tignanelli, C.J., & Pakhomov, S.V. (2021). NLP Methods for Extraction of Symptoms from Unstructured Data for Use in Prognostic COVID-19 Analytic Models. J. Artif. Intell. Res., 72, 429-474.
  2. Li, J., Zhou, Y., Jiang, X., Natarajan, K.,  Pakhomov, S.V. S., Liu, H., Xu, H., (2021). Are synthetic clinical notes useful for real natural language processing tasks: A case study on clinical entity recognition, Journal of the American Medical Informatics Association, 28(10):2193–220.
  3. Sahoo, H. S., Silverman, G. M., Ingraham, N. E., Lupei, M., Puskarich, M. E., Finzel, R. L., Sartori, J., Zhang, R., Knoll, B. C., Liu, S., Liu, H., Melton, G. B., Tignanelli, C. J., Pakhomov, S.V.S. (2021). A fast, resource efficient, and reliable rule-based system for COVID-19 symptom identification, JAMIA Open, 4(3):ooab070.
  4. Guo, Y., Li, C., Carol, R., Pakhomov, S.V.S. and Cohen, T. (2021). Crossing the “Cookie Theft” Corpus Chasm: Applying What BERT Learns From Outside Data to the ADReSS Challenge Dementia Detection Task. Frontiers in Computer Science. 3:1-26.
  5. Ferland, L., Sauve, J., Lucke, M., Nie, R., Khadar, M., Pakhomov, S.V.S. and Maria Gini. (2021). “Tell Me About Your Day: Designing a Conversational Agent for Time and Stress Management.” In Explainable AI in Healthcare and Medicine: Building a Culture of Transparency and Accountability, edited by Arash Shaban-Nejad, Martin Michalowski, and David L. Buckeridge, 297–303. Cham: Springer International Publishing, 2021.
  6. Finzel, R., Singh, E., Michalowski, M., Gini, M., Pakhomov, S.V.S. (2021). “Everyday Living Artificial Intelligence Hub.” In Proceedings of the Second Workshop on Data Science with Human in the Loop: Language Advances, pp. 102-104.

 

2020

 

  1. Pakhomov, S.V.S., Thuras, P.D., Finzel, R., Eppel, J., Kotlyar, M. (2020). Using consumer-wearable technology for remote assessment of physiological response to stress in the naturalistic environment. PLOS ONE 15(3): e0229942.
  2. Mashour, G.A., Frank, L.,  Batthyany, A., Kolanowski, A.M., Nahm, M., Schulman-Green, D., Greyson, B., Pakhomov, S.V.S., Karlawish, J., Shah, R.C. (2020). Paradoxical lucidity: A potential paradigm shift for the neurobiology and treatment of severe dementias. Alzheimer’s and Dementia. 18 (8): 1107-1114.
  3. Tignanelli, C., Silverman, G., Lindemann, E., Trembley, A., Gipson, J., Beilman, G., Lyng, J., Finzel, R.,  McEwan, R., Knoll, B., Pakhomov, S.V.S. and Melton, G. (2020). Natural language processing of prehospital emergency medical services trauma records allows for automated characterization of treatment appropriateness. Journal of Trauma and Acute Care Surgery, 88 (5), 607-614.
  4. Loughrey, D.H., Pakhomov, S.V.S., Lawlor, B.A. (2019). Altered verbal fluency processes in older adults with age-related hearing loss. Experimental Gerontology 130, 110794
  5. Swann, J. A., Silvermann, G. M., Lindemann, E. A., Boland, L., Gibson, J. C., Lick, C. J., Knoll, B. C., Pakhomov, S., Melton, G. B., & Tignanelli, C. J. (2020). Artificial Intelligence Facilitates Performance Review and Characterization of Prehospital Emergency Medical Services Treatment. Journal of the American College of Surgeons, 231(4), S305--S306.
  6. Dong, X., Li, J., Soysal, E., Bian, J., DuVall, S. L., Hanchrow, E., Liu, H., Lynch, K. E., Matheny, M., Natarajan, K., Pakhomov, S. & others (2020). COVID-19 TestNorm: A tool to normalize COVID-19 testing names to LOINC codes. Journal of the American Medical Informatics Association, 27(9), 1437--1442.
  7. Singh, E., Bompelli, A., Wan, R., Bian, J., Pakhomov, S.V.S., Zhang, R. (2021). A Conversational Agent System for Dietary Supplements Use. In Proceedings 2020 IEEE International Conference on Healthcare Informatics (ICHI).
  8. Cohen, T. and Pakhomov, S.V.S. (2020). A Tale of Two Perplexities: Sensitivity of Neural Language Models to Lexical Retrieval Deficits in Dementia of the Alzheimer's Type. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL 2020). (pre-print: arXiv:2005.03593).
  9. Bompelli, A., Silverman, G., Finzel, R., Vasilakes, J., Knoll, B., Pakhomov, S., & Zhang, R. (2020). Comparing NLP Systems to Extract Entities of Eligibility Criteria in Dietary Supplements Clinical Trials Using NLP-ADAPT. International Conference on Artificial Intelligence in Medicine (pp. 67--77).
  10. Gaydhani, A., Finzel, R., Dufresne, S., Gini, M., & Pakhomov, S. (2020). Conversational Agent for Daily Living Assessment Coaching. CEUR Workshop Proceedings (vol. 2760, pp. 8--13).
  11. L Ferland, J Sauve, M Lucke, R Nie, M Khadar, S Pakhomov, M Gini. (2020). Tell me about your day: designing a conversational agent for time and stress management. Explainable AI in Healthcare and Medicine, 297-303.

 

2019

 

  1. Wulff, D., Deyne, S., Jones, M., Mata, R., The Aging Lexicon Consortium. (2019). New Perspectives on the Aging Lexicon. Trends in Cognitive Science. 23 (8): 686-698.
  2. Sadat, Md. N., Al Aziz, Md. M., Mohammed, N., Pakhomov, S., Liu, H., Jiang, X. (2019). A privacy-preserving distributed filtering framework for NLP artifacts. BMC Medical Informatics and Decision Making. 19, Article number: 183.
  3. Knoll, B.C.,  Lindemann, E.A., Albert, A.L., Melton, G.B., Pakhomov, S.V.S. (2019) Recurrent Deep Network Models for Clinical NLP Tasks: Use Case with Sentence Boundary Disambiguation. Studies in health technology and informatics 264, 198-202
  4. Hultman, G.M., Marquard, J.L., Kandaswamy, S., Lindemann, E.A., Pakhomov, S.V.S., Melton, G. B. (2019). Electronic Progress Note Reading Patterns: An Eye Tracking Analysis. Studies in health technology and informatics 264, 1684-1685.
  5. Silverman, G.M., Lindemann, E.A., Rajamani, G., Finzel, R.L., McEwan, R., Knoll, B.C., Pakhomov, S.V.S., Melton, G. B.,  Tignanelli, C.J.  (2019). Named Entity Recognition in Prehospital Trauma Care. Studies in health technology and informatics 264, 1586-1587.
  6. Hultman, G.M., Marquard, J.L., Lindemann, E.,  Arsoniadis,  E., Pakhomov, S. ... Melton, G. B. (2019). Challenges and Opportunities to Improve the Clinician Experience Reviewing Electronic Progress Notes. Applied Clinical Informatics 10 (03), 446-453.
  7. Fan, Y., Pakhomov, S., McEwan, R., Zhao, W., Lindemann, E., Zhang, R. (2019). Using word embeddings to expand terminology of dietary supplements on clinical notes. JAMIA Open, 2 (2): 246-253.
  8. Zheng J., Finzel R., Pakhomov S., Gini M. (2020) Spoken Dialogue Systems for Medication Management. In: Shaban-Nejad A., Michalowski M. (eds) Precision Health and Medicine. W3PHAI 2019. Studies in Computational Intelligence, vol 843.
  9. Ferland, L., Sauve, J., Lucke, M., Nie, J., Khadar, M., Pakhomov, SV and Gini, M. (2020). “Tell Me About Your Day: Designing a Conversational Agent for Time and Stress Management”, AAAI Workshop on Health Intelligence

 

2018

 

  1. Pakhomov, S., Eberly, L., Knopman, D. (2018). Recurrent Perseverations on Semantic Verbal Fluency Tasks as an Early Marker of Cognitive Impairment. Journal of Clinical and Experimental Neuropsychology. 40(8):832-840.
  2. Hultman, G., McEwan, R., Pakhomov, S., Lindemann, E., Skube, S., & Melton, G. B. (2018). Usability Evaluation of an Unstructured Clinical Document Query Tool for Researchers. AMIA Summits on Translational Science Proceedings, 2017, 84–93.
  3. Rizvi, R. F., Adam, T. J., Lindemann, E. A., Vasilakes, J., Pakhomov, S. V., Bishop, J. R., … Zhang, R. (2018). Comparing Existing Resources to Represent Dietary Supplements. AMIA Summits on Translational Science Proceedings, 2017, 207–216.
  4. Vasilakes, J., Rizvi, R., Melton, G., Pakhomov, S., Zhang, R. (2018). Evaluating active learning methods for annotating semantic predications, JAMIA Open,  (in press).
  5. Schommer, J.C.; Brown, L.M.; Bortz, R.A.; Cernasev, A.; Gomaa, B.T.; Hager, K.D.; Hillman, L.; Okoro, O.; Pakhomov, S.V.S.; Ranelli, P.L. An Opportunity for Pharmacists to Help Improve Coordination and Continuity of Patient Health Care. Pharmacy 2018, 6, 78.
  6. Rizvi, R., Adam, T., Lindemann, E., Vasilakes, J., Pakhomov, S.V., Bishop, J., Melton, G., Zhang, R. Comparing Existing Resources to Represent Dietary Supplements. Proceedings of the AMIA Informatics Summits, 2018, San Francisco, CA. (selected as Student Paper Competition Finalist)

 

2017

 

  1. Zhang, R., Pakhomov, S.V., Arsoniadis, E.G. Lee, J.T., Wang, Y., Melton, G.B. (2017). Detecting clinically relevant new information in clinical notes across specialties and settings. BMC medical informatics and decision making, 17 (2), 68.
  2. Soysal, E.,Wang, J., Jiang, M., Wu, Y., Pakhomov, S., Liu, H., Xu, H. (2017). CLAMP–a toolkit for efficiently building customized clinical natural language processing pipelines. Journal of the American Medical Informatics Association.  25 (3), 331-336
  3. Hultman, G., McEwan, R., Pakhomov, S., Lindemann, E., Skube, S., Melton, GB. (2017). Usability Evaluation of NLP-PIER: A Clinical Document Search Engine for Researchers. Studies in Health Technology and Informatics 245, 1269-1269
  4. Fan, Y., Adam, TJ., McEwan, R., Pakhomov, SV., Melton, GB., Zhang R., (2017). Detecting Signals of Interactions Between Warfarin and Dietary Supplements in Electronic Health Records. Studies in Health Technology and Informatics 245, 370.
  5. Fan, Y., He, L., Pakhomov, S.V., Melton, G.B., Zhang, R. (2017). Classifying Supplement Use Status in Clinical Notes. In Proceedings of AMIA Summits on Translational Science Proceedings 2017, San Francisco, CA.
  6. Finley, G., Farmer, S., Pakhomov, S.V. (2017). What Analogies Reveal about Word Vectors and their Compositionality. Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (* SEM 2017), Vancouver, Canada.

 

2016

 

  1. Pakhomov, S.V., Finley, G., McEwan, R., Wang, Y., Melton, G. (2016). Corpus domain effects on distributional semantic modeling of medical terms. Bioinformatics.  2016; 32(23):3635-3644.
  2. Pakhomov, S.V., Teeple, W., Mills, A., Kotlyar, M. (2016). Use of an automated mobile application to assess effects of nicotine withdrawal on verbal fluency: a pilot study. Experimental and Clinical Psychopharmacology.  2016; 24(5):341-347.
  3. Pakhomov, S.V., Eberly, L., Knopman, D. (2016). Characterizing cognitive performance in a large longitudinal study of aging with computerized semantic indices of verbal fluency. Neuropsychologia. 2016; 89:42-56.
  4. Rizvi, R.F., Harder, K.A., Hultman, G.M., Adam, T.J., Kim, M., Pakhomov, S.V., Melton, G.B. (2016). A comparative observational study of inpatient clinical note-entry and reading/retrieval styles adopted by physicians. International Journal of Medical Informatics. 2016; 90:1-11.
  5. Finley, G., Pakhomov, S.V., McEwan, R., Melton, G. (2016). “Towards Comprehensive Clinical Abbreviation Disambiguation Using Machine-Labeled Training Data.” In Proceedings of American Medical Informatics Symposium (AMIA). Chicago, IL. [acceptance rate <50%]
  6. Wang, Y., Chen, E., Pakhomov, S.V., Lindemann, E., Melton, G. (2016). “Investigating Longitudinal Tobacco Use Information from Social History and Clinical Notes in the Electronic Health Record.” In Proceedings of American Medical Informatics Symposium (AMIA). Chicago, IL. [acceptance rate <50%]
  7. Knoll, B., Melton, GB, Liu, H., Xu, H., Pakhomov, S.V. (2016). “Using synthetic clinical data to train an HMM-based POS tagger” In Proceedings of the 2016 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI), Las Vegas, NV.

 

2015

 

  1. Pakhomov, S.V., Marino, S., Banks, S., Bernick, C. (2015). Using automatic speech recognition to assess spoken responses to cognitive tests of semantic verbal fluency. Speech Communication; 75:14-26.
  2. Pakhomov, S.V., Jones, D.T., Knopman, D. (2015). Language networks associated with computerized semantic indices. NeuroImage; 104:125-37.
  3. Melton, G.B., Wang, Y., Arsoniadis, E., Pakhomov, S.V., Adam, T.J., Kwaan, M.R., Rothenberger, D.A., Chen, E.S. (2015). Analyzing operative note structure in development of a section header resource. Studies in Health Technology and Informatics. 2015;216:821-6.
  4. Zheng, K., Vydiswaran, V.G., Liu, Y., Wang, Y., Stubbs, A., Uzuner, O., Gururaj, A.E., Bayer, S., Aberdeen, J., Rumshisky, A., Pakhomov, S.V., Liu, H., Xu, H. (2015). Ease of adoption of clinical natural language processing software: An evaluation of five systems. Journal of Biomedical Informatics. S1532-0464(15): 148-3.
  5. Ahmed, G.F., Marino, S.E., Brundage, R.C., Pakhomov, S.V., Leppik, I.E., Cloyd, J.C., Clark, A., Birnbaum, A.K. (2015). Pharmacokinetic-pharmacodynamic modelling of intravenous and oral topiramate and its effect on phonemic fluency in adult healthy volunteers. British Journal of Clinical Pharmacology; 79(5):820-30.
  6. Wang, Y., Pakhomov, S.V., Ryan, J.O., Melton, G.B. (2015). Domain adaption of parsing for operative notes. Journal of Biomedical Informatics; 54:1-9.
  7. Manohar, N., Adam, T.J., Pakhomov, S.V., Melton, G.B., Zhang, R. (2015). Evaluation of Herbal and Dietary Supplement Resource Term Coverage. Studies in Health Technology and Informatics; 216:785-9.
  8. Wang, Y., Chen, E., Pakhomov, S.V., Arsoniadis, E., Carter, E., Lindeman, E., Sarkar, N., Melton, G. (2015). “Automated Extraction of Substance Use Information from Clinical Texts.” In Proceedings of the American Medical Informatics Association Symposium, San Francisco, CA. [acceptance rate <50%]
  9. Zhang, R., Manohar, N., Arsoniadis, E., Wang, Y., Adam, T., Pakhomov, S.V, Melton, G. (2015). “Evaluating Term Coverage of Herbal and Dietary Supplements in Electronic Health Records.” In Proceedings of the American Medical Informatics Association Symposium, San Francisco, CA.
  10. Zhang, R., Adam, T.J., Simon, G., Cairelli, M.J., Rindflesch, T., Pakhomov, S., Melton, G.B. (2015). “Mining Biomedical Literature to Explore Interactions between Cancer Drugs and Dietary Supplements.” In Proceedings of the AMIA Joint Summits on Translational Science. 2015. 

 

2014

 

  1. Moon, S., Pakhomov, S.V, Liu, N., Ryan, J.O., Melton, G.B. (2014). A sense inventory for clinical abbreviations and acronyms created using clinical notes and medical dictionary resources. Journal of American Medical Informatics Association. 2014; 21:299-307.
  2. Zhang, R., Cairelli, M., Fiszman, M., Rosemblat, G., Kilicoglu, H., Rindflesch, T., Pakhomov, S.V, Melton, G. (2014). Using semantic predications to uncover drug-drug interactions in clinical data. Journal of Biomedical Informatics; 49:134-147.
  3. Pakhomov, S.V., Hemmy, L. (2014). A computational linguistic measure of clustering behavior on semantic verbal fluency task predicts risk of future dementia in the Nun Study. Cortex; 55:97-106.
  4. Zhang, R., Cairelli, M., Fiszman, M., Kilicoglu, H., Rindflesch, TC, Pakhomov, S.V., Melton, GB. (2014). Exploiting literature-derived knowledge and semantics to identify potential prostate cancer drugs. Cancer Informatics. 2014: Supp.1:103-11.
  5. Masanz. J., Pakhomov, S.V., Xu, H., Wu, S., Chute, C., Liu, H. (2014). “Open Source Clinical NLP - More than Any Single System.” In Proceedings of the American Medical Informatics Association Joint Summits on Translational Science. 2014.
  6. McInnes, B., Pedersen, T., Liu, Y., Melton, G., Pakhomov, S.V. (2014). “U-path: An Undirected Path-based Measure of Semantic Similarity.” In Proceedings of the American Medical Informatics Association Symposium, Washington, DC.
  7. Bill, R.; Pakhomov, S.V., Chen, E., Winden, T., Carter, E., Melton, G. (2014). “Automated Extraction of Family History Information from Clinical Notes.” In Proceedings of the American Medical Informatics Association Symposium, Washington, DC.
  8. Wang, Y., Pakhomov, S.V, Dale, J., Chen, E., Melton, G. (2014). “Application of HL7/LOINC Document Ontology to a University-affiliated Integrated Health System Research Clinical Data Repository.” In Proceedings of the American Medical Informatics Association Joint Summits on Translational Science. 2014.
  9. Wang, Y., Pakhomov, S.V., Ryan, J., Melton, G. (2014). “Semantic Role Labeling for Modeling Surgical Procedures in Operative Notes.” In Proceedings of the American Medical Informatics Association Symposium, Washington, DC.
  10. Zhang, R., Pakhomov, S.V., Melton, GB. (2014). “Longitudinal Analysis of New Information Types in Clinical Notes.” In Proceedings of the AMIA Joint Summits on Translational Science. 2014. (Manuscript selected as a student paper competition finalist).
  11. Zhang, R., Pakhomov, S.V., Lee, J., Melton, G. (2014). “Using Language Models to Identify Relevant New Information in Inpatient Clinical Notes.” In Proceedings of the American Medical Informatics Association Symposium, Washington, DC. [acceptance rate <50%]

 

2013

 

  1. Wang, Y., Pakhomov, S.V., Melton, G.B. (2013). Predicate argument structure frames for modeling information in operative notes. Studies in Health Technology and Informatics; 192:783-7. (**Manuscript selected as a Student Paper Competition Finalist at MedInfo)
  2. Zhang, R., Pakhomov, S.V., Lee, J.T., Melton, G.B. (2013). Navigating longitudinal clinical notes with an automated method for detecting new information. Studies in Health Technology and Informatics. 2013;192:754-8. (**Manuscript selected as a Student Paper Competition Finalist at MedInfo)
  3. Farri, O., Monsen, K., Pakhomov, S.V., Pieczkiewicz, D., Speedie, S., Melton, G. (2013). Effects of Time Constraints on Clinician-Computer Interaction: A Study on Information Synthesis from EHR Notes. Journal of Biomedical Informatics; 46(6):1136-44.
  4. Farri, O., Rahman, A., Monsen, K.A., Zhang, R., Pakhomov, S.V., Pieckiewicz, D.S., Speedie, S.M., Melton, G.B. (2013). Impact of a prototype visualization tool for new information in EHR clinical documents. Applied Clinical Informatics. 3(4):404-18.
  5. Pakhomov, S.V., Marino, S., Birnbaum, A.K. (2013). Quantification of speech dysfluency as a marker of medication-Induced cognitive impairment: an application of computerized speech analysis in neuropharmacology. Computer Speech and Language. 27(1):116-134.
  6. Ryan, J., Pakhomov, S.V, Marino, S., Bernick, C., Banks, S. (2013). “Computerized Analysis of a Verbal Fluency Test.” In Proceedings of the Association for Computational Linguistics (ACL-2013) Symposium.
  7. Zhang, R., Pakhomov, S.V., Lee, J., Melton, G. (2013). “Navigating Longitudinal Clinical Notes with an Automated Method for Detecting New Information.” In Proceedings of MedInfo Symposium, Copenhagen, Denmark.
  8. Wang, Y., Pakhomov, S., Melton, G. (2013). “Predicate Argument Structure Frames for Modeling Information in Operative Notes.” In Proceedings of MedInfo Symposium, Copenhagen, Denmark. 

 

2012

 

  1. Pakhomov, S.V, Hemmy, L., Lim, K. (2012). Automated semantic indices related to cognitive function and rate of cognitive decline. Neuropsychologia. 50(9):2165-2175.
  2. Pakhomov, S.V., McInnes, B., Lamba, J., Liu, Y., Melton, G., Ghodke, Y., Bhise, N., Lamba, V., Birnbaum, AK. (2012). Using PharmGKB to train text mining approaches for identifying potential gene targets for pharmacogenomic studies. Journal of Biomedical Informatics. 45(5): 862-869.
  3. Marino, S. Pakhomov, S.V., Han, S., Anderson, K, Ding, M., Eberly, L. et al. (2012). The effect of topiramate plasma concentration on linguistic behavior, verbal recall and working memory. Epilepsy and Behavior. 24(3):365-372.
  4. Moon, S., Pakhomov, S.V., Melton, G.B. (2012). “Automated Disambiguation of Acronyms and Abbreviations in Clinical Texts: Window and Training Size Considerations.” In Proceedings of the American Medical Informatics Association Symposium. 2012: 3010-9. (**Manuscript selected as finalist in AMIA Student Paper Competition)
  5. Farri, O., Pieckiewicz, D.S., Rahman, A.S., Adam, T.J., Pakhomov, S.V., Melton, G.B. (2012). “A Qualitative Analysis of EHR Clinical Document Synthesis by Clinicians.” In Proceedings of the American Medical Informatics Association Symposium. 2012:1211-20. (**Manuscript 3rd place in AMIA Student Paper Competition)
  6. Wang, Y., Pakhomov, S.V., Burkart, N.E., Ryan, J.O., Melton, G.B. (2012). “A Study of Actions in Operative Notes.” In Proceedings of the American Medical Informatics Association Symposium. 2012:1431-40.
  7. Zhang, R., Pakhomov, S.V., Gladding, S., Aylward, M., Borman-Shoap, E., Melton, G.B. (2012). “Automated Assessment of Medical Training Evaluation Text.” In Proceedings of the American Medical Informatics Association Symposium. 2012:1459-68.
  8. Liu, Y., Bill, R., Fiszman, M., Rindflesch, T., Pedersen, T., Melton, G.B., Pakhomov, S.V. (2012). “Using SemRep to Label Semantic Relations Extracted from Clinical Text.” In Proceedings of the American Medical Informatics Association Symposium. 2012:587-95. [acceptance rate <50%]
  9. Bill, R.W., Liu, Y., McInnes, B.T., Melton, G.B., Pedersen, T., Pakhomov S.V. (2012). “Evaluating Semantic Relatedness and Similarity Measures with Standardized MedDRA Queries.” In Proceedings of the American Medical Informatics Association Symposium. 2012:43-50.
  10. Liu, Y., McInnes, B., Pedersen, T., Melton, G.B., Pakhomov, S.V. (2012). “Semantic Relatedness Study Using Second Order Co-Occurrence Vector Computed by Biomedical Corpora, UMLS and WordNet.” In Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium (IHI 2012) (January, 2012). Miami, Florida, pp. 363-372.
  11. Zhang, R., Pakhomov, S.V., Melton, G.B. (2012). “Automated Identification of Relevant New Information in Clinical Narrative.” In Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium (IHI 2012) (January, 2012). Miami, Florida, pp. 837-42.

 

2011

 

  1. Pakhomov, S.V., Kaiser, E., Boley, D., Marino, S., Knopman, D., Birnbaum, A. (2011). Effects of age and dementia on temporal cycles in spontaneous speech fluency. Journal of Neurolinguistics. 24(6):619-635.
  2. Pakhomov, S.V. and Kotlyar, M. (2011). “Prosodic Correlates of Individual Physiological Response to Stress.” In Proceedings of 12thAnnual Conference of the International Speech Communication Association (Interspeech’11) (Aug. 2011), Florence, Italy, pp. 2949-52.
  3. Pakhomov, S.V., Shah, N., Van Houten, H., Hanson, P., Smith, S. (2011). “The Role of the Electronic Medical Record in the Assessment of Health Related Quality of Life.” In Proceedings of the American Medical Informatics Symposium (November 2011), pp. 1080-8.
  4. Zhang, R., Pakhomov, S.V., McInnes, B., Melton, G.B. (2011). “Evaluating Measures of Redundancy in Clinical Texts.” In Proceedings of the American Medical Informatics Symposium (November 2011), pp. 1612-20.
  5. Moon, S.R., Pakhomov, S.V., Ryan, J., Melton, G.B. (2011). “Automated Non-Alphanumeric Symbol Resolution in Clinical Text.” In Proceedings of the American Medical Informatics Symposium (November 2011), pp. 979-86.
  6. Wang, Y., Melton, G.B., Pakhomov, S.V. (2011). “It’s about This and That: A Description of Anaphoric Expressions in Clinical Text.” In Proceedings of the American Medical Informatics Symposium (November 2011), pp. 1471-80.
  7. McInnes, B., Pedersen, T., Liu, Y., Pakhomov, S.V., Melton, G.B. (2011). “Knowledge-based Method for Determining the Meaning of Ambiguous Biomedical Terms Using Information Content Measures of Similarity.” In Proceedings of the American Medical Informatics Symposium (November 2011), pp. 895-904.
  8. McInnes, B., Pedersen, T., Liu, Y., Pakhomov, S.V., Melton, G. (2011). “Using Second-order Vectors in a Knowledge-based Method for Acronym Disambiguation.” In Proceedings of the Fifteenth Conference on Computational Natural Language Learning (CoNLL 2011) (June 2011). Portland, OR, pp. 145 – 153. 

 

2010

 

  1. Pakhomov, S.V., Chacon, D., Wicklund, M., Gundel, J. (2010). Computerized assessment of syntactic complexity in Alzheimer’s disease: a case study of Iris Murdoch’s writing. Behavior Research Methods. 43(1):136-144.
  2. Pakhomov, S.V, Pedersen, T., McInnes, B., Melton, G., Ruggieri, A., Chute, C. (2010). Towards a framework for developing semantic relatedness reference standards. Journal of Biomedical Informatics. 44(2):251-265.
  3. Pakhomov, S.V., Shah, N., Hanson, P., Balasubramaniam, S., Smith, S. (2010). Automated monitoring of aspirin use in populations at risk for cardiovascular events. Informatics in Primary Care. 18(2):125-33.
  4. Pakhomov, S.V., Smith, G., Chacon, D., Feliciano, Y., Graff-Radford, N., Caselli, R., Knopman, D. (2010). Computerized analysis of speech and language to identify psycholinguistic correlates of frontotemporal lobar degeneration. Cognitive and Behavioral Neurology 23(3):165-177.
  5. Pakhomov, S.V., McInnes, B., Adam, T., Liu, Y., Pedersen, T., Melton, G. (2010). “Semantic Similarity and Relatedness between Clinical Terms: An Experimental Study.” In Proceedings of the American Medical Informatics Symposium (November 2010), pp. 572-576. [acceptance rate <50%]
  6. Melton, G., Moon, R. McInnes, B., Pakhomov, S.V. (2010). “Automated Identification of Synonyms in Biomedical Acronym Sense Inventories.” In Proceedings of Louhi 02 Workshop at the North American Association of Computational Linguistics, Los Angeles, CA, pp. 46-52.

 

2009

 

  1. Pakhomov, S.V., Smith, G., Marino, S., Birnbaum, A., Graff-Radford, N., Caselli, R., Boeve, B., Knopman, D. (2009). A computerized technique to assess language use patterns in patients with frontotemporal dementia. Journal of Neurolinguistics. 23(2):127-144.
  2. Melton, G., Raman, N., Chen, E., Sarkar, I., Pakhomov, S.V., Madoff, R. (2009) Evaluation of family history information within clinical documents and adequacy of HL7 clinical statement and clinical genomics family history models for its representation. Journal of American Medical Informatics Association. 17(3):337-340.
  3. McInnes, B., Pedersen, T., Pakhomov, S.V. (2009). “UMLS-Interface and UMLS-Similarity: Open Source Software for Measuring Paths and Semantic Similarity.” In Proceedings of the American Medical Informatics Symposium (November 2009). San Francisco, CA, pp. 431- 435.
News and Events

Recurring Events

CAIR Lab Meeting: TBD 

Education

Tutorials

Tools

A list of tools and systems developed in CAIR Lab. Access to some of these tools is restricted.

  • Speech-to-text Service
  • Text-to-speech Service
  • Llama 3 70bn LLM Service
  • MnSTAR Chatbot
  • SALSA 2
  • PASCAL
  • FASTER