Position: Graduate Student

Current Institution: MIT

Abstract:
Estimating the Response and Effect of Clinical Interventions

Much prior work in clinical modeling has focused on building discriminative models to detect specific easily coded outcomes with little clinical utility (e.g., hospital mortality) under specific ICU settings, or understanding the predictive value of various types of clinical information without taking interventions into account. In this work, we focus on understanding the impact of interventions on the underlying physiological reserve of patients in different clinical settings. Reserve can be thought of as the latent variability in patient response to treatment after accounting for their observed state.

In this work, we focus on understanding the impact of interventions on the underlying physiological reserve of patients in different clinical settings. Reserve can be thought of as the latent variability in patient response to treatment after accounting for their observed state. Understanding reserve is therefore important to performing successful interventions, and can be used in many clinical settings. I attempt to understand reserve in response to intervention in two settings: 1) the response of intensive care unit (ICU) patients to common clinical interventions in the ICU, and 2) the response of voice patients to behavioral and surgical treatments in an ambulatory outpatient

I attempt to understand reserve in response to intervention in two settings: 1) the response of intensive care unit (ICU) patients to common clinical interventions in the ICU, and 2) the response of voice patients to behavioral and surgical treatments in an ambulatory outpatient setting. In both settings, we use large sets of clinical data to investigate whether specific interventions are meaningful to patients in an empirically sound way.

Bio:
Marzyeh Ghassemi is a PhD student in the Clinical Decision Making Group (MEDG) in MIT’s
Computer Science and Artificial Intelligence Lab (CSAIL) supervised by Prof. Peter Szolovits. Her research uses machine learning techniques and statistical modeling to predict and stratify relevant human risks.

Marzyeh is interested in creating probabilistic latent variable models to estimate the underlying physiological state of patients during critical illnesses. She is also interested in understanding the development and progression of conditions like hearing loss and vocal hyperfunction using a combination of sensor data, clinical observations, and other physiological measurements.

While at MIT, Marzyeh has served on MIT’s Women’s Advisory Group Presidential Committee,
as Connection Chair to the Women in Machine Learning Workshop, on MIT’s Corporation Joint Advisory Committee on Institute-wide Affairs, and on MIT’s Committee on Foreign Scholarships. Prior to MIT, Marzyeh received two B.S. degrees in computer science and electrical engineering with a minor in applied mathematics from New Mexico State University as a Goldwater Scholar, and a MSc. degree in biomedical engineering from Oxford University as a Marshall Scholar. She also worked at Intel Corporation in the Rotation Engineering Program, and then as a Market Development Manager for the Emerging Markets Platform Group.