预测老年抑郁症:将生命历程数据与机器学习相结合。

IF 2.2 3区 医学 Q2 ECONOMICS Economics & Human Biology Pub Date : 2023-11-24 DOI:10.1016/j.ehb.2023.101331
Carlotta Montorsi , Alessio Fusco , Philippe Van Kerm , Stéphane P.A. Bordas
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引用次数: 0

摘要

随着人口老龄化,了解提高老年抑郁症风险的生命过程因素可能有助于预测需求,并从长远来看降低医疗成本。我们通过在监督机器学习算法中结合成人生活轨迹和童年条件来估计老年抑郁症的风险。使用来自欧洲健康、老龄化和退休调查(SHARE)的数据,我们实现并比较了六种替代机器学习算法的性能。我们使用不同的生命周期数据配置来分析算法的性能。虽然我们在算法之间获得了相似的预测能力,但当使用序列数据使用生命过程的半结构化表示时,我们获得了最高的预测性能。我们使用Shapley加性解释方法来提取最具决定性的预测模式。年龄、健康状况、童年状况和低教育水平可以预测以后生活中大多数抑郁风险,但我们在生命过程不稳定和牙科保健服务利用率低的指标中发现了新的预测模式。
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Predicting depression in old age: Combining life course data with machine learning

With ageing populations, understanding life course factors that raise the risk of depression in old age may help anticipate needs and reduce healthcare costs in the long run. We estimate the risk of depression in old age by combining adult life course trajectories and childhood conditions in supervised machine learning algorithms. Using data from the Survey of Health, Ageing and Retirement in Europe (SHARE), we implement and compare the performance of six alternative machine learning algorithms. We analyse the performance of the algorithms using different life-course data configurations. While we obtain similar predictive abilities between algorithms, we achieve the highest predictive performance when employing semi-structured representations of life courses using sequence data. We use the Shapley Additive Explanations method to extract the most decisive predictive patterns. Age, health, childhood conditions, and low education predict most depression risk later in life, but we identify new predictive patterns in indicators of life course instability and low utilization of dental care services.

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来源期刊
Economics & Human Biology
Economics & Human Biology 医学-公共卫生、环境卫生与职业卫生
CiteScore
4.50
自引率
12.00%
发文量
85
审稿时长
61 days
期刊介绍: Economics and Human Biology is devoted to the exploration of the effect of socio-economic processes on human beings as biological organisms. Research covered in this (quarterly) interdisciplinary journal is not bound by temporal or geographic limitations.
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