( events)   Timezone:  
Sat Dec 12 06:00 AM -- 02:00 PM (PST)
MLPH: Machine Learning in Public Health
Rumi Chunara · Abraham Flaxman · Daniel Lizotte · Chirag Patel · Laura Rosella

Workshop Home Page

Public health and population health refer to the study of daily life factors and prevention efforts, and their effects on the health of populations. We expect that work featured in this workshop will differ from Machine Learning in Healthcare as it will focus on data and algorithms related to the non-medical conditions that shape our health including structural, lifestyle, policy, social, behavior and environmental factors. Indeed, much of the data that is traditionally used in machine learning and health problems are really about our interactions with the health care system, and this workshop aims to balance this with machine learning work using data on the non-medical conditions that shape our health. There are many machine learning opportunities specific to these data and how they are used to assess and understand health and disease, that differ from healthcare specific data and tasks (e.g. the data is often unstructured, must be captured across the life-course, in different environments, etc.) This is pertinent for both infectious diseases such as COVID-19 and non-communicable diseases such as diabetes, stroke, etc. Indeed, this workshop topic is especially timely given the COVID outbreak, protests regarding racism, and associated interest in exploring relevance of machine learning to questions around disease incidence, prevention and mitigation related to both of these and their synergy. These questions require the use of data from outside of healthcare, as well as considerations of how machine learning can augment work in epidemiology and biostatistics.

Opening Remarks - Rumi Chunara (Live introduction)
Participatory Epidemiology and Machine Learning for Innovation in Public Health - Daniela Paolotti (Invited Talk (Recorded Presentation + Live Q&A))
Unsupervised Discovery of Subgroups with Anomalous Maternal and Neonatal Outcomes with WHO´s Safe Childbirth Checklist as Intervention - Girmaw Abebe Tadesse (Recorded talk)
Detection of Malaria Vector Breedding Habitats using Topographic Models - Aishwarya Jadhav (Recorded talk)
AutoODE: Bridging Physics-based and Data-driven modeling for COVID-19 Forecasting - Rui Wang (Recorded talk)
FireNet - Dense Forecasting of Wildfire Smoke Particulate Matter Using Sparsity Invariant CNNs - Renhao Wang (Recorded talk)
Predicting air pollution spatial variation with street-level imagery - Esra Suel (Recorded lightning talk)
Automated Medical Assistance: Attention Based Consultation System - Raj Pranesh (Recorded lightning talk)
A Expectation-Based Network Scan Statistic for a COVID-19 Early Warning System - Thorpe Woods (Recorded lightning talk)
Incorporating Healthcase Motivated Constraints in Restless Multi-Armed Bandit Based Resource Allocation - Aviva Prins (Recorded lightning talk)
Temporal Graph Analysis for Outbreak Pattern Detection in Covid-19 Contact Tracing Networks - Dario Antweiler (Recorded lightning talk)
Public Health in Practice Panel: Matthew Biggerstaff (CDC), Brian DeRenzi (Dimagi), Roni Rosenfeld (CMU), Zainab Samad (AKU) (Panel)
Images and Audio Data as a Resource for Environmental Health - Scott Weichenthal (Invited Talk (Recorded Presentation + Live Q&A))
Speed research encounter (Paired meetings)
Understanding Big Data in Biomedicine and Public Health - Latifa Jackson (Invited Talk (Recorded Presentation + Live Q&A))
How the COVID-19 Community Vulnerability Index (CCVI) and machine learning can enable a precision public health response to the pandemic - Nicholas Stewart (Recorded lightning talk)
Addressing Public Health Literacy Disparities through Machine Learning: A Human in the Loop Augmented Intelligence based Tool for Public Health - Anjala Susarla (Recorded lightning talk)
Twitter Detects Who is Social Distancing During COVID-19 - Paiheng Xu (Recorded lightning talk)
Sequential Stochastic Network Structure Optimization With Applications to Addressing Canada's Obesity Epidemic - Nicholas Johnson (Recorded lightning talk)
Detecting Individuals with Depressive Disorder From Personal Google Search and YouTube History Logs - Boyu Zhang (Recorded lightning talk)
Scalable Gaussian Process Regression Via Median Posterior Inference for Estimating Multi-Pollutant Mixture Health Effects - Aaron Sonabend (Recorded lightning talk)
Steering a Historical Disease Forecasting Model Under a Pandemic: A Case of Flu and COVID-19 - Alexander Rodriguez (Recorded lightning talk)
Break 2 (Break)
High Performance AI for Pandemic Prediction and Response - Madhav Marathe (Invited Talk (Recorded Presentation + Live Q&A))
Closing remarks (Live closing remarks)