NDLI: From Personal Wellness to Healthcare Support Systems: A Big Data Driven Approach

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From Personal Wellness to Healthcare Support Systems: A Big Data Driven Approach

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From Personal Wellness to Healthcare Support Systems: A Big Data Driven Approach

Content Provider	ACM Digital Library
Author	Chua, Tat-Seng
Abstract	The abundance and growing usage of social media has resulted in a huge depository of users' social posts, which provide a stethoscope for inferring individuals' habits, preferences, lifestyle and wellness. In this research, we look into mining users' wellness in terms of three aspects of users' lifestyle: activities, food and medication. This is done by analysing users' public social posts on twitter (text), FourSquare (location), Instagram (image) and Endomonda (Sensor). We then develop a multi-source multi-task learning model to mine users' individual wellness profiles as well as group profiles for critical illnesses, such as the debate and obesity. Such illnesses are of key concerns to many individuals and government agencies. From critical illnesses, we further explore the combination of social wellness analytics with other healthcare resources towards building a "big-data-driven knowledge-oriented healthcare support network". The network offers the latest healthcare knowledge drawn upon information from five heterogeneous sources. They are: (a) the authoritative and reliable healthcare knowledge bases such as the WebMD; (b) the community QA sites such as the HealthTap where questions by users were answered by doctors; (c) crowd-based sources such as the Patientslikeme that contains many treatment records voluntarily shared by the patients; (d) the social signals gathered from social media platforms that cover mostly safety and critical illness-related issues; and (e) other miscellaneous information such as the weather, health sensors and medication related knowledge. Extensive research has been done to automatically integrate the gathered information into a comprehensive healthcare taxonomy and a vertical domain question-answering system. One use case of such system is to realize a social D2D (Doctor-to-Doctor) network, aiming to bridge the know-how gap between the well-trained doctors in the cities and community doctors serving the rural areas. Such network will help to enrich the knowledge and bring credibility to community doctors, and hence alleviate the imbalance in healthcare resources in many developing countries.
Starting Page	1
Ending Page	1
Page Count	1
File Format	PDF
ISBN	9781450345187
DOI	10.1145/2985766.2985777
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2016-10-16
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Multi-source learning Healthcare
Content Type	Text
Resource Type	Article

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