GeoAI and Deep Learning Symposium: Machine Learning and Sensor Data Analytics for Health Research

Type: Paper
Theme: Geography, GIScience and Health: Building an International Geospatial Health Research Network (IGHRN)
Sponsor Groups:
Poster #:
Day: 4/3/2019
Start / End Time: 9:55 AM / 11:35 AM
Room: Wilson A, Marriott, Mezzanine Level
Organizers: Eun-Kyeong Kim, Wei Luo
Chairs: Eun-Kyeong Kim

Description

Advanced technologies and new scientific methods are rapidly integrated in health research. In the last decade, applied machine learning (ML) and sensor data analytics have been applied to the health domain, thanks to an abundance of available data and sensing techniques. ML enables automatic detection and/or highly accurate prediction of various infectious/ environmental diseases and mental/physical conditions. Hence, it may drive significant innovation in geospatial health research.

Owing to advances in sensor technologies, the physical and mental health status in real life can be captured and analyzed at a finer temporal and geographical resolution than ever before. Massive data is produced from wearable sensors including GPS, accelerometer, gyroscope, magnetometer sensors, heart beat sensors, and stress sensors as well as ambulatory/ momentary assessments. This trend opens new research opportunities to delve into many aspects of individuals’ health from geographical perspectives in an objective manner.

This session seeks for participants who are applying advanced ML and sensing techniques to geospatial health research. The potential topics include, but are not limited to:
1) Applied ML in the health domain;
2) Automated disease detection and diagnostics;
3) Sensor data-driven geospatial health research;
4) Health research based on ambulatory/momentary assessments;
5) Physical activities and physical/mental health;
6) Wearable/mobile sensor data processing and feature extraction (from GPS, accelerometer, gyroscope, magnetometer sensor data);
7) Spatiotemporal data analysis on sensor data and momentary assessments;
8) Visualization of wearable/mobile sensor data;
9) Interpretability of ML in health research.

To present your research in our session, please submit your abstract through the AAG website (http://www.aag.org/cs/annualmeeting/register) and send your PIN to Eun-Kyeong Kim (eun-kyeong.kim@geo.uzh.ch) or Wei Luo (weivluo@asu.edu). 

This session is part of the 2nd GeoAI and Deep Learning Symposium (http://goo.gl/n6GJVS) and the selected AAG 2019 theme: Geography, GIScience, and Health: Building an International Geospatial Health Research Network (IGHRN).

Organizers:
Eun-Kyeong Kim, University of Zurich
Wei Luo, Arizona State University


Agenda

Type Details Minutes Start Time
Presenter Sandy Wong*, Florida State University, Martha María Téllez-Rojo, Instituto Nacional de Salud Pública, Alejandra Cantoral, Instituto Nacional de Salud Pública, Ivan Pantic, Instituto Nacional de Salud Pública, Emily Oken, Harvard Pilgrim Health Care, Jennifer W. Thompson, Harvard Pilgrim Health Care, Katherine Svensson, Icahn School of Medicine at Mount Sinai, Kodi Arfer, Icahn School of Medicine at Mount Sinai, Robert O. Wright, Icahn School of Medicine at Mount Sinai, Andrea A. Baccarelli, Columbia University Mailman School of Public Health, Itai Kloog, Ben-Gurion University of the Negev, Allan C. Just, Icahn School of Medicine at Mount Sinai, Children not at play: Associations between sedentary activity and environmental exposures among Mexico City youth 20 9:55 AM
Presenter Kenan Li*, University of Southern California, Katherine Sward, University of Utah, Sandrah Eckel, University of Southern California, Predicting asthma symptoms with a Long Short Term Memory neural network and Automatic Feature Extraction using Convolutional Autoencoder 20 10:15 AM
Presenter Oluwatobi Oke*, Department of Civil and Environmental Engineering, Colorado State University, Ellison Carter, Department of Civil and Environmental Engineering, Colorado State University, Sheryl Magzamen, Department of Environmental and Radiological Health Sciences, Colorado State University, Shantanu Jathar, Department of Mechanical Engineering, Colorado State University, Ander Wilson, Department of Statistics, Colorado State University, Bruce Draper, Department of Computer Science, Colorado State University, Jeremy Auerbach, Department of Environmental and Radiological Health Sciences, Colorado State University, Charles He, Department of Mechanical Engineering, Colorado State University, Using Deep Learning to Examine the Association between the Built Environment and Resident’s Exposure to Chemical Pollutants. 20 10:35 AM
Presenter Lizhi Miao*, 1.College of Geographical and Biological Information Nanjing University of Posts and Telecommunications, Nanjing, China;2. Department of Geography and Geographic Information Science, University of Illinois at Urbana–Champaign, Mei-Po Kwan, Department of Geography and Geographic Information Science, University of Illinois at Urbana–Champaign, Jiyao Diao, College of Telecommunications & Information Engineering Nanjing University of Posts and Telecommunications, Nanjing, China, Donglai Jiao, College of Geographical and Biological Information Nanjing University of Posts and Telecommunications, Nanjing, China, Using Apache Spark and Random Forest Algorithm to Implement Breast Cancer Risk Prediction Analysis 20 10:55 AM
Presenter Eun-Kyeong Kim*, University of Zurich, Sensor Data Analytics and Machine Learning for Health Research: a review 20 11:15 AM

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