Thomas A. Lasko, MD, PhD
2525 West End Ave, Ste 800, Nashville TN 37203 • 615-936-5372 • tom.lasko@vanderbilt.edu
Education
BS
University of California, San Diego, La Jolla, CA.
Physics, June 1991. Magna Cum Laude.
MD
University of California, San Diego School of Medicine, La Jolla, CA.
June 2000.
Clinical
Internship
Gundersen Lutheran Medical Center, La Crosse, WI.
Transitional, 2000 – 2001.
SM
Massachusetts Institute of Technology, Cambridge, MA.
Medical Informatics, June 2004.
Thesis: Automated Identification of a Physician’s Primary Patients.
Advisors: G. Octo Barnett and Henry C. Chueh.
Postdoctoral Harvard Medical School, Boston, MA.
Fellowship
National Institutes of Health Postdoctoral Fellow, June 2001 – June 2006.
Massachusetts General Hospital Laboratory of Computer Science and
Brigham and Women’s Hospital Decision Systems Group.
Mentors: G. Octo Barnett and Lucila Ohno-Machado.
PhD
Massachusetts Institute of Technology, Cambridge, MA.
Computer Science, September 2007.
Dissertation: Spectral Anonymization of Data.
Advisors: Staal A. Vinterbo and Peter Szolovits.
Licensure and Certification
California Medical License CFE53160 (Feb 2008 – present, currently retired status)
Academic Appointments
Assistant Professor, Department of Biomedical Informatics, Vanderbilt University School of
Medicine, (September 2010 – Present).
Employment
Science Applications International Corporation, La Jolla, CA. Scientist (1991 – 1995).
Genetic Biosystems, Inc., San Diego, CA Optical Engineer (1995 – 1996).
Google, Inc., Mountain View, CA. Software Engineer. (September 2007 – August 2010).
Professional Activities
Early Career Reviewer for NIH/BCHI Study Section, Summer 2013.
Reviewer for NIH/NINDS Special Study Section, January 2014.
Ad-hoc reviewer:
• Journal of the American Medical Informatics Association
• Journal of Biomedical Informatics (Outstanding Reviewer, 2015)
• Methods of Information in Medicine
Lasko, page 2
• IEEE Transactions on Knowledge and Data Engineering.
Memberships:
• American Medical Informatics Association (AMIA), 2000 – Present.
• Association for Computing Machinery (ACM), 2002 – Present.
• Association for the Advancement of Artificial Intelligence (AAAI), 2014 – Present.
Teaching Activities
Graduate School Courses
• Massachusetts Institute of Technology, Medical Artificial Intelligence, Guest lecture,
Spring 2005. Classifier evaluation using Receiver Operating Characteristic curves.
• Massachusetts Institute of Technology, Medical Decision Making. Guest lecture, Fall
2006: Computational Diagnosis.
• Vanderbilt University, Introduction to Artificial Intelligence. Guest lecture, fall 2011:
Selected Applications and Unique Issues of Machine Learning over Large Datasets.
• Vanderbilt University, Data Privacy in Biomedicine. Guest lecture, Spring 2013: Spectral
Anonymization of Data.
• Vanderbilt University, Machine Learning. Guest lecture, Spring 2014: Deep Learning.
• Vanderbilt University, Methodological Foundations of Biomedical Informatics, Spring
2015. Seven lectures on Machine Learning.
Continuing Medical Education
• Computational Phenotype Discovery Using Deep Learning Over Noisy, Sparse, and
Irregular EMR Data, April 3, 2013 (Lecturer).
• Phenotype Discovery from Messy EMR Data. Oct 18, 2013 (Lecturer).
• Phenotype Discovery from Electronic Medical Records. May 15, 2015 (Lecturer).
Primary Research Supervision
• Jacob VanHouten, MSTP student, 2011 – present.
Advising on Thesis/Dissertation Committee
• Robert Carroll, PhD student 2011 – present.
• Ravi Atreya, MSTP student, 2012 – present.
• Pedro Texeira, MSTP student, 2012 – present.
• Yukun Chen, PhD student, 2012 – present.
• Sharon Davis, PhD student, 2014 – present.
• Mara Kim, PhD student (Department of Biological Sciences), 2015 – present.
Other Mentoring
• Renaid Kim, Summer Intern 2013
Research Program
Scalable Biomedical Pattern Recognition via Deep Learning (PI Lasko)
$175,500, NIH/NLM 0.30 Effort: 07/01/2013 – 06/30/2016.
Scalable Biomedical Pattern Recognition via Deep Learning (PI Lasko)
$60,000, Edward Mallinkrodt, Jr. Foundation. 0.25 Effort: 03/01/2012 – 02/28/2015.
Lasko, page 3
Evidence-based Diagnostic Tools for Translational and Clinical Research (eTfor2) (PI
Miller) $235,200, NIH/NLM. 0.05 Effort: 09/30/2010 – 09/29/2013.
From GWAS to PheWAS: Scanning the EMR Phenome for Gene-Disease Associations (PI
Denny) $213,150, NIH/NLM 0.10 Effort: 09/01/2011 – 08/31/2018.
Preserving Privacy in Medical Data Sets (PI Vinterbo)
$350,000, NIH/NLM 1.00 Effort 09/15/2006 – 09/14/2009.
Publications
Articles in refereed journals
1. Mathis RF, May BA, Lasko TA. Polarization Coupling in Unpolarized Interferometric
Fiberoptic Gyroscopes: Effect of Imperfect Components. Proceedings, SPIE Fiber Optic and
Laser Sensors XII, 1994, 2292:283-91.
2. Lasko TA, Kripke DF, Elliot JA. Melatonin suppression by illumination of upper and lower
visual fields. J of Biol Rhythms, 1999 Apr, 14(2):122–5.
3. Lasko TA, Hauser SE. Approximate string matching algorithms for limited-vocabulary OCR
output correction. Proc. SPIE, Vol. 4307, Document Recognition and Retrieval VIII, San
Jose, CA, January 2001, 232–40.
4. Atlas SJ, Lasko TA, Chueh HC, Chang Y, Grant RW, Barry MJ. Accurately Identifying
Patients Loyal to Providers in a Primary Care Practice Network: Implications for Quality
Assessment and Improvement Efforts. Annual Meeting of the Society of General Internal
Medicine. J Gen Intern Med 2005; 20 (suppl 1)
5. Lasko TA, Baghwat JG, Zou KH, Ohno-Machado L. The use of receiver operating
characteristic curves in biomedical informatics. J Biomed Inform 2005; 38: 404–415.
6. Atlas SJ, Chang Y, Lasko TA, Chueh HC, Grant RW, Barry MJ. Is this “my” patient?
Development and validation of a predictive model to link patients to primary care providers.
J Gen Intern Med 2006, 21(9):973-978. PMC1831616.
7. Lasko TA, Atlas SJ, Barry MJ, Chueh HC. Automated identification of a physician’s primary
patients. J Am Med Inform Assoc 2006; 13: 74–79. PMC1380200.
8. Lasko TA, Vinterbo, SA. Spectral anonymization of data. IEEE Transactions on Knowledge
and Data Engineering 2010, 22(3):437-446. PMC3046817.
9. Carroll RJ, Thompson WK, Eyler AE, Mandelin AM, Cai T, Zink RM, Pacheco JA,
Boomershine CS, Lasko TA, Xu H, Karlson EW, Perez RG, Gainer VS, Murphy SN,
Ruderman EM, Pope RM, Plenge RM, Kho AN, Liao KP, Denny JC. Portability of an
algorithm to identify rheumatoid arthritis in electronic health records. J Am Med Inform
Assoc 2012 Jun;19(e1):e162-9. Epub 2012 Feb 28. PMC3392871.
Lasko, page 4
10. Wei-Qi W, Cronin RM, Xu H, Lasko TA, Bastarache L, Denny JC. Development of an
ensemble resource linking MEDications to their Indications (MEDI). Proceedings of the
2013 AMIA Summit on Translational Bioinformatics. March 2013.
11. Lasko TA, Denny JC, Levy MA. Computational Phenotype Detection Using Unsupervised
Feature Learning Over Noisy, Sparse, and Irregular Clinical Data. PLoS ONE 2013 8(6):
e66341. doi:10.1371/journal.pone.0066341.
12. Wei WQ, Cronin RM, Xu H, Lasko TA, Bastarache L, Denny JC. Development and
evaluation of an ensemble resource linking medications to their indications. J Am Med
Inform Assoc, 2013 20:954–961.
13. Sun J, McNaughton C, Zhang P, Perer A, Gkoulalas-Divans A, Denny JC, Kirby J, Lasko T,
Saip A, Malin BA. Predicting Changes in Hypertension Control Using Electronic Health
Records from a Chronic Disease Management Program. 2013. J Am Med Inform Assoc,
Published Online First: 17 Sep 2013 doi:10.1136/amiajnl- 2013-002033
14. Lasko TA. Efficient Inference of Gaussian Process Modulated Renewal Processes with
Application to Medical Event Data. Proceedings of the Thirtieth Conference on Uncertainty
in Artificial Intelligence, 2014.
15. VanHouten JP, Starmer J, Lorenzi N, Maron DJ, Lasko TA. Machine Learning for Risk
Prediction of Acute Coronary Syndrome. Proceedings of the 2014 AMIA Annual
Symposium.
16. Lasko TA. Nonstationary Gaussian Process Regression for Evaluating Clinical Laboratory
Test Sampling Strategies. Proceedings of the Twenty-Ninth AAAI Conference on Artificial
Intelligence, 2015.
Patents
1. US Patent 5,323,229. May BA, Lasko TA, Everett DH. Science Applications International
Corporation, assignee. Measurement system using optical coherence shifting interferometry.,
1992.
2. US Patent 8,473,489. Lasko TA, Tomkins A, Angelo M, Gray MK, Ryan R, Godbole NU,
Zeiger RF. Google, Inc, assignee. Identifying Entities Using Search Results, 2013. (This and
the next three are Google Symptom Search patents).
3. US Patent 8,775,439. Lasko TA, Tomkins A, Angelo M, Gray MK, Ryan R, Godbole NU,
Zeiger RF. Google, Inc, assignee. Identifying Entities Using Search Results, 2014.
4. US Patent 8,843,466. Zeiger RF, Lasko TA, Tomkins A, Angelo M, Gray MK, Ryan R,
Godbole NU. Google, Inc, assignee. Identifying Entities Using Search Results, 2014.
5. US Patent 8,856,099. Lasko TA, Tomkins A, Angelo M, Gray MK, Ryan R, Godbole NU,
Zeiger RF. Google, Inc, assignee. Identifying Entities Using Search Results, 2014.
Lasko, page 5
Peer-Reviewed Abstracts
1. Lasko TA, Feldman MJ, Barnett GO. DXplain Evoking Strength – clinician interpretation
and consistency. Proc AMIA Symp. Bethesda, MD, December 2002.
2. Lasko TA, Atlas SJ, Barry M, Wong N, Chueh, HC. When My Patient Is Not My Patient:
Inferring Primary-Care Relationships Using Machine Learning. MGH Clinical Research
Day, Boston, MA, June 2004.
3. Cowan J, Basford, M, Carroll R, Harris P, Lasko TA, Malin B, Denny JC. Big data in
medical research: using a big data appliance to manage genomic and EHR data. Proceedings
of the 2013 AMIA Summit on Translational Bioinformatics . March 2013.
4. Atreya RV, Lasko TA, Levy MA. Role of ICD Granularity in Phenotyping Hematologic
Malignancies for Tumor Registries. Proceedings of the 2013 AMIA Annual Symposium.
5. VanHouten JP, Starmer JM, Lorenzi NM, Lasko TA. Random Forest Classification of Acute
Coronary Syndromes. Proceedings of the 2013 AMIA Annual Symposium.
6. Wei-Qi W, Cronin RM, Xu H, Lasko TA, Bastarache L, Denny JC. Development of an
ensemble resource linking MEDications to their Indications (MEDI). Proceedings of the
2013 AMIA Summit on Translational Bioinformatics. March 2013.
7. Lasko TA, Denny JC, Levy MA. Scalable Data-driven Phenotypes via Unsupervised Feature
Learning. Proceedings of the 2013 AMIA Summit on Translational Bioinformatics. March
2013.
8. Lasko TA. Inferring the Latent Intensity of Clinical Events using Modulated Renewal
Processes. NIPS 2013 Workshop on Machine Learning for Clinical Data Analysis and
Healthcare, 2013.
9. Chen Y, Lasko TA, Mei Q, Denny J, Xu, H. A Study of Active Learning Methods for
Clinical Entities Recognition. Proceedings of the 2013 AMIA Annual Symposium.
10. Chen Y, Zhang Y, Mei Q, Lasko TA, Denny JC. A Preliminary Study of Coupling Transfer
Learning with Active Learning for Clinical Named Entity Recognition Between Two
Institutions. Proceedings of the 2014 AMIA Annual Symposium.
Scientific Presentations
1. Lasko TA. The Denominator Problem. Massachusetts General Hospital Primary Care
Operations Improvement Program, May 2004. (Invited presentation.)
2. Lasko, TA. Inferring Primary-Care Relationships using Machine Learning. National Library
of Medicine Informatics Training Conference, June 2004.
3. Lasko TA. Structure and Interpretation of Evoking Strength. Decision Systems Group,
Brigham and Women’s Hospital, June 2005. (Invited presentation.)
4. Lasko TA. Interpretation of Evoking Strength. National Library of Medicine Informatics
Training Conference, July 2005.
5. Lasko TA. Spectral Anonymization of Data. Lister Hill Center, National Library of
Medicine, February 2010. (Invited presentation.)
6. Lasko TA. Spectral Anonymization of Data. Vanderbilt University, March 2010. (Invited
presentation.)
Lasko, page 6
7. Lasko TA. On the Promise of Computational Medicine and the Reduction of its Privacy
Threats. Engineering Systems Division, Massachusetts Institute of Technology, March 2010.
(Invited presentation.)
8. Lasko TA. Computational Phenotype Discovery Using Deep Learning Over Noisy, Sparse,
and Irregular EMR Data. Vanderbilt University Department of Biomedical Informatics
Seminar, April 2013.
9. Wei-Qi W, Cronin RM, Xu H, Lasko TA, Bastarache L, Denny JC. Development of an
ensemble resource linking MEDications to their Indications (MEDI). Proceedings of the
2013 AMIA Summit on Translational Bioinformatics. March 2013. (Peer reviewed).
10. Lasko TA, Denny JC, Levy MA. Scalable Data-driven Phenotypes via Unsupervised Feature
Learning. Proceedings of the 2013 AMIA Summit on Translational Bioinformatics. March
2013. (Peer reviewed.)
11. Lasko TA. Phenotype Discovery from Messy EMR Data. Vanderbilt Center for Quantitative
Sciences. Oct 18, 2013. (Invited Presentation).
12. Lasko TA. Inferring the Latent Intensity of Clinical Events using Modulated Renewal
Processes. NIPS 2013 Workshop on Machine Learning for Clinical Data Analysis and
Healthcare, Dec 10, 2013. (Peer reviewed.)
13. Lasko TA. The Phenotype Discovery Problem. Conference on the Meaningful Use of
Complex Medical Data (MUCMD), Aug 8, 2014. (Invited Presentation).
14. Lasko TA. The Phenotype Discovery Problem. Vanderbilt Department of Biomedical
Engineering, Nov 20, 2014. (Invited Presentation).
15. Lasko TA. Phenotype Discovery from Electronic Medical Records. Vanderbilt Center for
Quantitative Sciences, May 15, 2015. (Invited Presentation).
16. Lasko TA. The Computational Phenotype Discovery Problem. Greater Nashville Healthcare
Analytics Group, May 12, 2015. (Invited Presentation).
17. Lasko TA. Phenotype Discovery from Electronic Medical Records. Harvard T.H. Chan
School of Public Health. Jun 3, 2015. (Invited lecture in their executive education course
Leadership Strategies for Information Technology in Health Care).