Farrokh Alemi, PhD

Professor, HAP

Contact Information

Email: falemi@gmu.edu
Phone: 703-993-5779
Building: Peterson Hall
Room 4418

CV

Download Farrokh Alemi curriculum vitae (CV) here.

Personal Websites

Open Online Courses
NCBI Bibliography

In the News

Biography

Dr. Farrokh Alemi was trained as an operations researcher and industrial engineer and has worked in both academia and health industry. He maintains patents on (1) sentiment analysis, (2) measurement of episodes of illness and (3) personalized medicine. He has more than 125 peer reviewed publications in journals such as Health Services Research, Medical Care, eClinicalMedicine and Palliative Medicine. His research focuses on causal analysis of massive data available in electronic health records. This research has required balancing of data to remove confounding prior to estimating causal impact of interventions. His publications have contributed to predictive medicine, precision medicine, comparative effectiveness of medications, sentiment analysis, natural language processing, risk adjusted analysis of cost effectiveness, causal networked models, identifying trajectories of diseases, and predicting prognosis of patients with multiple morbidities. Alemi maintains a decision aid for selection of antidepressants at http://MeAgainMeds.com. Alemi is the author of Multi-Morbidity index, used in management of polypharmacy patients. He has worked with diverse groups of patients including children, nursing home residents and patients with diabetes, major depression, heart failure, anemia, hypertension, trauma, drug abuse, and other diseases. In addition, Alemi was a pioneer in online management of patients and has provided Congressional testimony on role of Internet in health delivery. He is the author of a book on decision analysis and another on policy systems and a third on application of process improvement to personal health. A fourth book, on causal statistical analysis, was published in 2020.

Research

Research Interests

  • Causal analysis of massive data in electronic health records, including Bayesian Probability Networks
  • Predictive and personalized medicine
  • Trajectory of diseases and prognosis of patients with multiple comorbidities
  • Precision medicine and comparative effectiveness of medications
  • Online management of patients, including online counseling
  • Balanced (propensity matched) analysis of cost effectiveness of interventions
  • Use of data mining, natural language processing, and artificial intelligence in health services research

Select Publications:

Degrees

  • PhD, Industrial and Systems Engineering, University of Wisconsin, Madison
  • MS, System Analysis, University of Wisconsin, Madison
  • BS, Industrial Engineering, University of Wisconsin, Madison