`

( events)   Timezone: »  
Workshop
Tue Dec 14 05:55 AM -- 12:40 PM (PST)
Machine Learning in Public Health
Rumi Chunara · Daniel Lizotte · Laura Rosella · Esra Suel · Marie Charpignon





Public health and population health refer to the study of daily life factors, prevention efforts, and their effects on the health of populations. Building on the success of our first workshop at NeurIPS 2020, this workshop 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. 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 non-medical conditions. This year we also broaden and integrate discussion on machine learning in the closely related area of urban planning, which is concerned with the technical and political processes regarding the development and design of land use. This includes the built environment, including air, water, and the infrastructure passing into and out of urban areas, such as transportation, communications, distribution networks, sanitation, protection and use of the environment, including their accessibility and equity. We make this extension this year due to the fundamentally and increasingly relevant intertwined nature of human health and environment, as well as the recent emergence of more modern data analytic tools in the urban planning realm. Public and population health, and urban planning are at the heart of structural approaches to counteract inequality and build pluralistic futures that improve the health and well-being of populations.

Welcoming Remarks (Live)
Keynote #1 Dr. Subhrajit "Subhro" Guhathakurta (Talk)
Keynote #1 Dr. Guhathakurta Live Q&A (Live Q&A)
Deep Learning for Spatiotemporal Modeling of Urbanization (Contributed talk 1)
Deep Learning for Spatiotemporal Modeling of Urbanization (Contributed talk 1 Q&A)
Restless Bandits in the Field: Real-World Study for Improving Maternal and Child Health Outcomes (Contributed talk 2)
Restless Bandits in the Field: Real-World Study for Improving Maternal and Child Health Outcomes (Contributed talk 2 Q&A)
A Brief Summary on Covid-19 Pandemic & Machine Learning Approaches (Lightning talk 1)
Assisted Living in the United States: an Open Dataset (Lightning talk 2)
A probabilistic approach to evaluating Cryptosporidium health risk in drinking water (Lightning talk 3)
ML in urban planning panel (Discussion Panel)
Keynote #2 Dr. Andrea Parker (Talk)
Keynote #2 Dr. Parker Live Q&A (Live Q&A)
Demand prediction of mobile clinics using public data (Contributed talk 3)
Demand prediction of mobile clinics using public data (Live Q&A)
Role of Attachment Variables in Resilient Families (Contributed talk 5)
Role of Attachment Variables in Resilient Families (Live Q&A)
Reconciling Risk Allocation and Prevalence Estimation in Public Health Using Batched Bandits (Lightning talk 4)
An mHealth Intervention for African American and Hispanic Adults: Preliminary Results from a One-Year Field Test (Lightning talk 5)
Kronecker Factorization for Preventing Catastrophic Forgetting (Lightning talk 6)
Keynote #3 Dr. Sanjay Basu (Talk)
Keynote #3 Live Q&A (Live Q&A)
Learning after Deployment: The Missed Tale of Supervision (Lightning talk 7)
Contrastive Learning for PM2.5 Prediction from Satellite Imagery (Lightning talk 8)
Encoding Cardiopulmonary Exercise Testing Time Series as Images for Classification using Convolutional Neural Network (Poster)
Discovering Alternative Food Proteins with Manifold Exploration and Spectral Clustering (Poster)
A Markov Chain Based Compartmental Model for COVID-19 in South Korea (Poster)
Exploring the Temporal Dynamics of County-Level Vulnerability Factors on COVID-19 Outcomes (Poster)
A probabilistic approach to evaluating Cryptosporidium health risk in drinking water (Poster)
A probabilistic approach to evaluating Cryptosporidium health risk in drinking water (Oral)
Reconciling Risk Allocation and Prevalence Estimation in Public Health Using Batched Bandits (Oral)
Assisted Living in the United States: an Open Dataset (Oral)
An mHealth Intervention for African American and Hispanic Adults: Preliminary Results from a One-Year Field Test (Oral)
A Brief Summary on Covid-19 Pandemic & Machine Learning Approaches (Poster)
A Brief Summary on Covid-19 Pandemic & Machine Learning Approaches (Oral)
Modelling Patient Journeys with Sharded Encoder Blocks and Federated Split Learning (Poster)
Reaching out : Towards a sustainable allocation strategy between users and therapists (Poster)
An mHealth Intervention for African American and Hispanic Adults: Preliminary Results from a One-Year Field Test (Poster)
Reconciling Risk Allocation and Prevalence Estimation in Public Health Using Batched Bandits (Poster)
Learning after Deployment: The Missed Tale of Supervision (Oral)
Kronecker Factorization for Preventing Catastrophic Forgetting (Oral)
Contrastive Learning for PM2.5 Prediction from Satellite Imagery (Poster)
General Framework for Evaluating Outbreak Prediction, Detection and Annotation Algorithms (Poster)
Learning after Deployment: The Missed Tale of Supervision (Poster)
A Recommendation System to Enhance Midwives’ Capacities in Low-Income Countries (Poster)
Assisted Living in the United States: an Open Dataset (Poster)
Kronecker Factorization for Preventing Catastrophic Forgetting (Poster)
Contrastive Learning for PM2.5 Prediction from Satellite Imagery (Oral)
Predicting Migraine Early from Fitbit Data with Deep Learning (Poster)
COVID-19 India Dataset: Parsing Detailed COVID-19 Data in Daily Health Bulletins from States in India (Poster)