Staff Profile - MGH Institute for Technology Assessment

Seyedeh Nazanin Khatami, PhD

Postdoctoral Fellow, Harvard Medical School

Nazanin is currently a postdoctoral research fellow at Harvard Medical School and Mass General Hospital. Nazanin received her PhD in Industrial Engineering and Operations Research from the University of Massachusetts (UMass) Amherst. She also holds an MS in Electrical and Computer Engineering from UMass Amherst. Her research focuses on disease modeling, control, and optimization. During her Ph.D., she closely collaborated with the HIV division at the CDC where she developed a sequential decision-making model to control HIV at a national level using reinforcement learning algorithms.

Dr. Khatami currently works with Dr. Mohammad Jalali (MJ) at ITA to develop machine learning models to address opioid crisis in the US. She also works with Dr. Krishna Reddy at the MGH Medical Practice Evaluation Center (MPEC) to develop a tuberculosis transmission module for people with HIV.

Selected Publications

Khatami, Seyedeh N.; Gopalappa, Chaitra

A reinforcement learning model to inform optimal decision paths for HIV elimination. Journal Article

In: Mathematical biosciences and engineering : MBE, vol. 18, iss. 6, pp. 7666–7684, 2021, ISSN: 1551-0018.

Abstract | Links | BibTeX

@article{Khatami2021,

title = {A reinforcement learning model to inform optimal decision paths for HIV elimination.},

author = {Seyedeh N. Khatami and Chaitra Gopalappa},

url = {https://pubmed.ncbi.nlm.nih.gov/34814269/},

doi = {10.3934/mbe.2021380},

issn = {1551-0018},

year  = {2021},

date = {2021-09-01},

journal = {Mathematical biosciences and engineering : MBE},

volume = {18},

issue = {6},

pages = {7666--7684},

abstract = {The 'Ending the HIV Epidemic (EHE)' national plan aims to reduce annual HIV incidence in the United States from 38,000 in 2015 to 9300 by 2025 and 3300 by 2030. Diagnosis and treatment are two most effective interventions, and thus, identifying corresponding optimal combinations of testing and retention-in-care rates would help inform implementation of relevant programs. Considering the dynamic and stochastic complexity of the disease and the time dynamics of decision-making, solving for optimal combinations using commonly used methods of parametric optimization or exhaustive evaluation of pre-selected options are infeasible. Reinforcement learning (RL), an artificial intelligence method, is ideal; however, training RL algorithms and ensuring convergence to optimality are computationally challenging for large-scale stochastic problems. We evaluate its feasibility in the context of the EHE goal. We trained an RL algorithm to identify a 'sequence' of combinations of HIV-testing and retention-in-care rates at 5-year intervals over 2015-2070 that optimally leads towards HIV elimination. We defined optimality as a sequence that maximizes quality-adjusted-life-years lived and minimizes HIV-testing and care-and-treatment costs. We show that solving for testing and retention-in-care rates through appropriate reformulation using proxy decision-metrics overcomes the computational challenges of RL. We used a stochastic agent-based simulation to train the RL algorithm. As there is variability in support-programs needed to address barriers to care-access, we evaluated the sensitivity of optimal decisions to three cost-functions. The model suggests to scale-up retention-in-care programs to achieve and maintain high annual retention-rates while initiating with a high testing-frequency but relaxing it over a 10-year period as incidence decreases. Results were mainly robust to the uncertainty in costs. However, testing and retention-in-care alone did not achieve the 2030 EHE targets, suggesting the need for additional interventions. The results from the model demonstrated convergence. RL is suitable for evaluating phased public health decisions for infectious disease control.},

keywords = {},

pubstate = {ppublish},

tppubtype = {article}

}

The 'Ending the HIV Epidemic (EHE)' national plan aims to reduce annual HIV incidence in the United States from 38,000 in 2015 to 9300 by 2025 and 3300 by 2030. Diagnosis and treatment are two most effective interventions, and thus, identifying corresponding optimal combinations of testing and retention-in-care rates would help inform implementation of relevant programs. Considering the dynamic and stochastic complexity of the disease and the time dynamics of decision-making, solving for optimal combinations using commonly used methods of parametric optimization or exhaustive evaluation of pre-selected options are infeasible. Reinforcement learning (RL), an artificial intelligence method, is ideal; however, training RL algorithms and ensuring convergence to optimality are computationally challenging for large-scale stochastic problems. We evaluate its feasibility in the context of the EHE goal. We trained an RL algorithm to identify a 'sequence' of combinations of HIV-testing and retention-in-care rates at 5-year intervals over 2015-2070 that optimally leads towards HIV elimination. We defined optimality as a sequence that maximizes quality-adjusted-life-years lived and minimizes HIV-testing and care-and-treatment costs. We show that solving for testing and retention-in-care rates through appropriate reformulation using proxy decision-metrics overcomes the computational challenges of RL. We used a stochastic agent-based simulation to train the RL algorithm. As there is variability in support-programs needed to address barriers to care-access, we evaluated the sensitivity of optimal decisions to three cost-functions. The model suggests to scale-up retention-in-care programs to achieve and maintain high annual retention-rates while initiating with a high testing-frequency but relaxing it over a 10-year period as incidence decreases. Results were mainly robust to the uncertainty in costs. However, testing and retention-in-care alone did not achieve the 2030 EHE targets, suggesting the need for additional interventions. The results from the model demonstrated convergence. RL is suitable for evaluating phased public health decisions for infectious disease control.

Baker, Erin D.; Khatami, Seyedeh Nazanin

The levelized cost of carbon: a practical, if imperfect, method to compare CO2 abatement projects Journal Article

In: Climate Policy, vol. 19, no. 9, pp. 1132-1143, 2019.

Links | BibTeX