Matthew Nemesure

Senior Data Scientist

D^3 Institute, Harvard Business School

Biography

Matthew Nemesure received his Ph.D. in Quantitative Biomedical Sciences at Dartmouth College. He has a Bachelor’s degree from Binghamton University in Integrative Neuroscience. Matthew’s primary interest is in data science, specifically in applied machine learning and NLP. Currently he is a senior data scientist at Harvard Business School in the D^3 Institute.

His research to date has focused on using cutting edge machine learning and AI technologies to address the complexities of data in the social sciences, specifically in the context of psychological disorders.

In his spare time, Matthew Enjoys the occasional game of pick-up basketball or soccer and attending trivia with his friends. During the colder months, he can be found skiing on the weekends or endeavoring to cook something new in the kitchen.

Selected Publications

Nemesure MD, Heinz MV, Huang R, Jacobson NC.

Predictive modeling of depression and anxiety using electronic health records and a novel machine learning approach with artificial intelligence - PubMedGeneralized anxiety disorder (GAD) and major depressive disorder (MDD) are highly prevalent and impairing problems, but frequently go undetected, leading to substantial treatment delays. Electronic health records (EHRs) collect a great deal of biometric markers and patient characteristics that could …

Nemesure MD, Schwedhelm TM, Sacerdote S, O'Malley AJ, Rozema LR, Moen EL.

A measure of local uniqueness to identify linchpins in a social network with node attributes - Applied Network ScienceNetwork centrality measures assign importance to influential or key nodes in a network based on the topological structure of the underlying adjacency matrix. In this work, we define the importance of a node in a network as being dependent on whether it is the only one of its kind among its neighbors’ ties. We introduce linchpin score, a measure of local uniqueness used to identify important nodes by assessing both network structure and a node attribute. We explore linchpin score by attribute type and examine relationships between linchpin score and other established network centrality measures (degree, betweenness, closeness, and eigenvector centrality). To assess the utility of this measure in a real-world application, we measured the linchpin score of physicians in patient-sharing networks to identify and characterize important physicians based on being locally unique for their specialty. We hypothesized that linchpin score would identify indispensable physicians who would not be easily replaced by another physician of their specialty type if they were to be removed from the network. We explored differences in rural and urban physicians by linchpin score compared with other network centrality measures in patient-sharing networks representing the 306 hospital referral regions in the United States. We show that linchpin score is uniquely able to make the distinction that rural specialists, but not rural general practitioners, are indispensable for rural patient care. Linchpin score reveals a novel aspect of network importance that can provide important insight into the vulnerability of health care provider networks. More broadly, applications of linchpin score may be relevant for the analysis of social networks where interdisciplinary collaboration is important.

Nemesure MD, Streltzov N, Schommer LM, Lekkas D, Jacobson NC, Bujarski KA.

Predictive Modeling of Suicidal Ideation in Patients with Epilepsy - PubMedThe results of this modeling approach allow the healthcare team to be prepared, in advance to a clinical visit, for the potential endorsement of SI. By allowing the necessary support to be prepared ahead of time, it can be better integrated at the point-of-care, where patients are most likely to upt …

CV_Nemesure.pdf

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