Serguei Pakhomov, PhD

Professor, Department of Pharmaceutical Care & Health Systems

Serguei Pakhomov

Contact Info

pakh0002@umn.edu

Office Phone 612-624-1198

Office Address:
7-125F Weaver-Densford Hall

Mailing Address:
7-125F Weaver-Densford Hall
308 Harvard Street SE
Minneapolis, MN 55455
USA

Professor, Department of Pharmaceutical Care & Health Systems

Affiliate Faculty, Institute for Health Informatics

Member of NLP/IE Group, Institute for Health Informatics


PhD, University of Minnesota (Linguistics), 2001

MA, University of Minnesota - Duluth (English Studies), 1994; MA, University of Minnesota (Linguistics), 1999; MS, Mayo Graduate School (Biomedical Sciences), 2008

Bachelors Degree, Petrozavodsk Pedagogical University (English and German), 1992

Summary

Dr. Pakhomov's current areas include natural language and speech processing and their applications in the biomedical domain. His research agenda consists of two areas: a) spontaneous speech and language characteristics indicative of effects of medications and neurodegenerative disorders on human cognition, and b) application of natural language processing and machine learning techniques to extracting information from clinical reports and biomedical literature. In the latter area, he is interested in secondary uses of patient information contained in clinical reports to enable clinical and translational research and improve patient safety through automated monitoring of adverse reactions to medications.

Awards & Recognition

  • NIH Roadmap Multidisciplinary Clinical Research Career Development Award
  • Research Fellowship - National Library of Medicine Fellow
  • Nominated for Homer Warner Award at the American Medical Informatics Association Symposium
  • Nominated for Best Student Paper Award at the American Medical Informatics Association Symposium

Research

Research Summary/Interests

Research Interests

Dr. Pakhomov's current areas include natural language and speech processing and their applications in the biomedical domain. His research agenda consists of two areas: a) spontaneous speech and language characteristics indicative of effects of medications and neurodegenerative disorders on human cognition, and b) application of natural language processing and machine learning techniques to extracting information from clinical reports and biomedical literature. In the latter area, he is interested in secondary uses of patient information contained in clinical reports to enable clinical and translational research and improve patient safety through automated monitoring of adverse reactions to medications.

Research Projects

Discovery and Visualization of New Information from Clinical Reports
Role: Co-Investigator
Funding Agency: AHRQ 1 R01 HS022085-01 (Melton-Meaux)
Project Dates: 09/01/2013 – 08/30/2017
This grant develops and evaluates visualization methods by “highlighting” important information from clinical texts, improving user interface design for clinical texts, and conducts a prospective clinical trial with a tool in the EHR to highlight new, non-redundant information in clinical documents.

Natural Language Processing for Clinical and Translational Research
Role: Co-Principal Investigator
Funding Agency: NIH/NIGMS 1 R01 GM102282-01A1 (Liu/Pakhomov/Xu)
Project Dates: 04/01/2013 – 03/31/2017
The overall goal of this project is to develop a novel framework to enable the use of clinical information embedded in clinical narratives for clinical and translational research.

Characterizing and Predicting Drug Effects on Cognition
Role: Co-Investigator
Funding Agency: NIH/NINDS 5 R01 NS076665-02 (Marino)
Project Dates: 09/01/2012 – 08/31/2016
The purpose of this project is to study mechanisms of medications affecting cognitive function through computerized behavioral and neuroimaging assessments.

Leveraging the EHR to Collect and Analyze Social, Behavioral & Familial Factors
Role: Co-Investigator 1 R01 LM011364-01 (Chen/Melton)
Funding Agency: NIH/NLM
Project Dates: 09/01/2012 – 08/31/2016
The overall goal of this project is to develop and evaluate computational methods for generating knowledge regarding the relationships between diseases and social, behavioral, and familial factors.

Automated Semantic Indices for Early Detection of Cognitive Changes
Role: Principal Investigator
Funding Agency: Alzheimer’s Association Private Foundation Grant (Pakhomov)
Project Dates: 08/01/2012 – 07/31/2014
The overall goal of this project is to develop and evaluate computational methods for generating knowledge regarding the relationships between diseases and social, behavioral, and familial factors.

University of Minnesota Clinical and Translational Science Institute (CTSI)
Role: Co-Investigator
Funding Agency: NIH/NCRR 1 U54 RR026066-01A2 (Blazar)
Project Dates: 06/01/2011 – 05/31/2016
The major goals of this infrastructure award are to support clinical and translational research at the University of Minnesota to transform research processes within the institution and community. Dr. Melton is involved with the biomedical informatics portion of the infrastructure. My role on this project is to provide consulting related to Natural Language processing of Electronic health Records.

Publications

  • Pakhomov, S., Hemmy, L. (2013). A computational linguistic measure of clustering behavior on semantic verbal fluency task predicts risk of future dementia in the Nun Study. Cortex. (Epub ahead of print).
  • Pakhomov, S., Marino, S., Birnbaum, AK, (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
  • Ryan, J., Pakhomov, S., Marino, S., Bernick, C., Banks, S. (2013). Computerized Analysis of a Verbal Fluency Test." Proceedings of the Association for Computational Linguistics (ACL-2013) Symposium (Short paper).
  • Farri, O., Monsen, K., Pakhomov, S., 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. (in press).
  • Zhang, R., Pakhomov, S., Lee, J., Meltoon, G. (2013). Navigating Longitudinal Clinical Notes with an Automated Method for Detecting New Information. Proceedings of MedInfo Symposium, Copenhagen, Denmark. (in press).
  • Wang, Y., Pakhomov, S., Melton, G. (2013). Predicate Argument Structure Frames for Modeling Information in Operative Notes. Proceedings of MedInfo Symposium, Copenhagen, Denmark. (in press).
  • Pakhomov, S., Hemmy, L., Lim, K. (2012). Automated Semantic Indices Related to Cognitive Function and Rate of Cognitive Decline. Neuropsychologia. 50(9):2165-2175.
  • Pakhomov, S., 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.
  • Marino, S. Pakhomov, S., 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.
  • Moon S, Pakhomov S, Melton GB. Automated Disambiguation of Acronyms and Abbreviations in Clinical Texts: Window and Training Size Considerations. Proceedings of the American Medical Informatics Association Symposium. 2012: 3010-9.
  • Farri O, Pieckiewicz DS, Rahman AS, Adam TJ, Pakhomov SV, Melton GB. A Qualitative Analysis of EHR Clinical Document Synthesis by Clinicians. Proceedings of the American Medical Informatics Association Symposium. 2012:1211-20.
  • Wang Y, Pakhomov S, Burkart NE, Ryan JO, Melton GB. A Study of Actions in Operative Notes. Proceedings of the American Medical Informatics Association Symposium. 2012:1431-40.
  • Zhang R, Pakhomov S, Gladding S, Aylward M, Borman-Shoap E, Melton GB. Automated Assessment of Medical Training Evaluation Text. Proceedings of the American Medical Informatics Association Symposium. 2012:1459-68.
  • Liu Y, Bill R, Fiszman M, Rindflesch T, Pedersen T, Melton GB, Pakhomov SV. Using SemRep to Label Semantic Relations Extracted from Clinical Text. Proceedings of the American Medical Informatics Association Symposium. 2012:587-95.
  • Bill RW, Liu Y, McInnes BT, Melton GB, Pedersen T, Pakhomov S. Evaluating Semantic Relatedness and Similarity Measures with Standardized MedDRA Queries. Proceedings of the American Medical Informatics Association Symposium. 2012:43-50.
  • Farri O, Rahman A, Monsen KA, Zhang R, Pakhomov SV, Pieckiewicz DS, Speedie, SM, Melton GB. Impact of a prototype visualization tool for new information in EHR Clinical Documents. Applied Clinical Informatics. 2012. 3(4):404-18.
  • Liu, Y., McInnes, B., Pedersen, T., Melton, G.B., Pakhomov, S. (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.
  • Zhang, R., Pakhomov, S., 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.
  • Pakhomov, S., 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.
  • Pakhomov, S. and Kotlyar, M. (2011). Prosodic Correlates of Individual Physiological Response to Stress. In Proceedings of 12th Annual Conference of the International Speech Communication Association (Interspeech-11) (Aug. 2011), Florence, Italy.
  • Pakhomov, S., 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.
  • Zhang, R., Pakhomov, S., 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.
  • Moon, S.R., Pakhomov, S., 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.
  • Wang, Y., Melton, G.B., Pakhomov, S. (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.
  • McInnes, B., Pedersen, T., Liu, Y., Pakhomov, S., 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.
  • McInnes, B., Pedersen, T., Liu, Y., Pakhomov, S., 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.
  • Pakhomov, S., 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 fronto-temporal lobar degeneration. Cognitive and Behavioral Neurology 23(3):165-177.
  • Pakhomov, S., Chacon, D., Wicklund, M., Gundel, J. (2010). Computerized Assessment of Syntactic Complexity in Alzheimer's Disease: a Case Study of Iris Murdoch's Writing. Behavioral Research Methods. 43(1):136-144.
  • Pakhomov, S. Shah, N., Penny Hanson, Balasubramaniam, S., Smith, S. (2010). Automated Monitoring of Aspirin Use in Populations at Risk for Cardiovascular Events. Informatics in Primary Care. (in press).
  • Melton-Meaux,, Moon, R. McInness, B., Pakhomov. S. (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.
  • Pakhomov, S., 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.
  • Pakhomov, S. 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.
  • Pakhomov, S., 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.
  • Melton-Meaux, G., Raman, N., Chen, E., Sarkar, I., Pakhomov, S., 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, 17:337-340.
  • McInnes, B., Pedersen, T., and Pakhomov, S. (2009). UMLS-Interface and UMLS-Similarity: Open Source Software for Measuring Paths and Semantic Similarity. To appear in Proceedings of the American Medical Informatics Symposium (November 2009). San Francisco, CA.
  • Melton-Meaux, G., Raman, N., Chen, E., Sarkar, I., Pakhomov, S., 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. To appear in Proceedings of the American Medical Informatics Symposium (November 2009).
  • Pakhomov, S., Jacobsen, S., Chute, C., & Roger, V. (2008). Agreement between Patient-reported Symptoms and their Documentation in the Medical Record. American Journal of Managed Care, 14(8):530-539.
  • Schommer, JC., Worley, MM., Kjos, AL., Pakhomov, S., Schondelmeyer, SW. (2008). A Thematic Analysis for How Patients, Prescribers, Experts, and Patient Advocates View the Prescription Choice Process. Social and Administrative Pharmacy, (in press available on-line).
  • Pakhomov, S., Shah, N., Hanson, P., Balasubramaniam, S., & Smith, S. (2008). Automatic Quality of Life Prediction Using Electronic Medical Records. In Proceedings of the American Medical Informatics Association Annual Symposium, November 2008, Washington, DC, pp. 545-549.
  • Pakhomov, S., Richardson, J., Finholt-Daniel, M., & Sales, G. (2008). Forced-alignment and Edit-Distance Scoring for Vocabulary Tutoring Applications, In Proceedings of Text Speech and Dialogue (TSD2008) Conference, September 2008, Brno, Czech Republic, (Lecture Notes in Artificial Intelligence): 443-450.
  • Pakhomov, S., Bjornsen, S., Hanson, P., & Smith, S. (2007). Quality Performance Measurement Using the Text of Electronic Medical Records. Medical Decisions Making. 28(4):462-470.
  • Pakhomov, S., Hanson, P., Bjornsen, S., & Smith, S. (2007). Automatic Classification of Foot Examination Findings using Statistical Natural Language Processing and Machine Learning Journal of American Medical Informatics Association. 15(2):198-202.
  • Pakhomov, S., Hemingway, H., Weston, S., Jacobsen, S., Rodeheffer, R.., & Roger, V. (2007). Epidemiology of Angina Pectoris: Role of Natural Language Processing of the Medical Record. American Heart Journal 2007; 153(4):666-673.
  • Pakhomov, S., Weston, S., Jacobsen, S., Chute, C., Meverden, R., & Roger, V. (2007). Electronic Medical Records for Clinical Research: Application to the Identification of Heart Failure. American Journal of Managed Care, 2007; 13:281-288.
  • Bursi, F., Weston, SA., Redfield, MM., Jacobsen, SJ., Pakhomov, S., Nkomo, VT., Meverden, RA., & Roger, VL. (2007) Systolic and Diastolic Heart Failure in the Community. JAMA 2006; 296(18):2209-2216.
  • Pakhomov, S., Buntrock, J., & Chute, C. (2005). Prospective Recruitment of Patients with Congestive Heart Failure using an Ad-hoc Binary Classifier. Journal of Biomedical Informatics, 38 (2), 145-153.
  • Pakhomov, S., Coden, A., & Chute, C. (2005). Developing a Corpus of Clinical Notes Manually Annotated for Part-of-Speech. International Journal of Medical Informatics, 75 (6):418-29.
  • Pakhomov, S., Ruggieri, A., & Chute, C. G. (2002). Maximum Entropy Modeling for Mining Patient Medication Status from Free Text. In Proceedings of the American Medical Informatics Association Symposium, November 2002, San Antonio, TX, pp. 587-591.