December 11, 2019 – 12:00-1:00pm EDT
Dr. Jeanette A Stingone
Incorporating machine learning approaches into perinatal and pediatric epidemiology: opportunities and challenges
The use of machine learning, broadly defined as analytic techniques that fit models algorithmically by adapting to patterns in data, is growing in use across many areas within public health and epidemiology. This talk will provide attendees with broad exposure to the elements of machine learning and its practical applications within perinatal and pediatric epidemiology. The talk will include discussion of technical aspects of machine learning, as well as important ethical and scientific considerations of using data-driven methods for epidemiologic research. A number of examples from the scientific literature will be presented and a general listing of resources for additional information and training will be provided.
Jeanette A Stingone PhD MPH
Assistant Professor, Department of Epidemiology
Mailman School of Public Health, Columbia University
Dr. Jeanette Stingone is an environmental epidemiologist with a focus on perinatal and pediatric health. She received her BS in Biomedical Engineering from Boston University, an MPH from the Mount Sinai School of Medicine and a PhD in Epidemiology from the University of North Carolina, Chapel Hill. Now an Assistant Professor in the Department of Epidemiology at Columbia University’s Mailman School of Public Health, she conducts research that couples data science techniques with epidemiologic methods to address research questions in children’s environmental health. Supported by an NIEHS-funded career development award, her current research seeks to uncover the combinations of air pollutants associated with adverse child health outcomes within high dimensional public health data. Read more
It is recommended to read the overview by Bi et al and then skimming the others, as Dr. Stingone will refere to these in teh talk when providing examples.
1. Overview of ML approaches: Bi Q, Goodman KE, Kaminsky J, Lessler J. What is machine learning? A Primer for the epidemiologist. AJE 2019; https://doi.org/10.1093/1je/kwz189 [epub ahead of print]
2. Examples of implementation:
a. Pan I, Nolan LB, Brown RR, Khan R et al Machine learning for social services: a study of prenatal case management in Illinois. AJPH 2017; 107:938-944.
b. Chiavegatto Filho ADP, Dos Santos HG, do Nasciemento CF, Massa K, Kawachi I Overachieving municipalities in public health: a machine learning approach. Epidemiology 2018; 29:836-840.
c. Das LT, Abramson EL, Stone AE, Kondrich JE, Kern LM, Grinspan ZM. Predicting frequent emergency department visits among children with asthma using HER data. Pediatr Pulmonol 2017; 52:880-890.