Obesity risk factors ranking using multi-task learning
Obesity risk factors ranking using multi-task learning
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DOI：
10.1109/bhi.2018.8333449
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发表时间：
2018-03
期刊：
影响因子：
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通讯作者：
Lu Wang;D. Zhu;E. Towner;Ming Dong
中科院分区：
文献类型：
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作者：
Lu Wang;D. Zhu;E. Towner;Ming Dong
肥胖是美国主要的可预防死亡原因之一。风险因素分析是识别和了解导致特定疾病的风险因素的过程，是制定高效和有效预防和干预措施的必要组成部分。现有的大多数方法通常旨在建立一个通用的模型来识别人群水平的风险因素。然而，这种类型的方法没有考虑到群体的异质性。为了克服这一局限性，我们制定了亚群特定的肥胖风险因素排名问题，在多任务学习（MTL）的框架下，确定一个排名列表的肥胖风险因素为每个亚群（任务），同时利用适当的共享信息跨任务。通过同步学习多个相关的任务，MTL提供了一个范例，在亚群体和人口水平的风险因素排名。
Obesity is one of the leading preventable causes of death in the United States (U.S.). Risk factor analysis is a process to identify and understand the risk factors contributing to a particular disease, and is an imperative component in the development of efficient and effective prevention and intervention efforts. Most existing methods usually aim to build a one-size-fits-all model to identify the risk factors at the population-level. However, this type of methods does not take into consideration of heterogeneity in the population. To overcome this limitation, we formulate the subpopulation specific obesity risk factors ranking problem, under the framework of multi-task learning (MTL), to identify a ranked list of obesity risk factors for each subpopulation (task) simultaneously with utilizing appropriate shared information across tasks. By synchronously learning multiple related tasks, MTL provides a paradigm to rank risk factors both at the subpopulation and population-levels.