Analysis of health recommendations using longitudinal quality of life data: QoL@TbA - A transformer-based approach

被引:0
作者
Siebra, Clauirton [1 ,2 ]
Kurpicz-Briki, Mascha [3 ]
Wac, Katarzyna [1 ]
机构
[1] Univ Geneva, Qual Life Technol Lab, 7 Route Drize,Battelle Bldg A,Office 234, CH-1211 Geneva, Switzerland
[2] Univ Fed Paraiba, Informat Ctr, Joao Pessoa, Brazil
[3] Bern Univ Appl Sci, Bern, Switzerland
基金
欧盟地平线“2020”;
关键词
Healthy behavior; recommendations; inductive reasoning; deep learning; DEPRESSION;
D O I
10.1177/14604582241291789
中图分类号
R19 [保健组织与事业(卫生事业管理)];
学科分类号
摘要
Objective: Health recommendation systems suggest behavioral modifications to improve quality of life. However, current approaches do not facilitate the generation or examination of such recommendations considering the multifeature longitudinal evolution of behaviors. This paper proposes the use of a deep learning transformer-based model that allows the analysis of recommendations for behavior changes. Methods: We adapted a prediction approach, namely Behavior Sequence Transformer (BST), which analyzes temporal human routines and patterns, generating inductive outcomes. The evaluation relied on a case study that employed the behavioral history and profile of the English Longitudinal Study of Ageing (ELSA) participants (n = 2682), predicting their psychological mood (normal, pre-depressed, depressed) according to input recommendations for behavioral changes. Root mean squared error (RMSE) and learning curves were used to track the recommendation accuracy evolution and possible overfitting problems. Results: Experiments demonstrated lower RMSE values for the multifeature model (0.28/0.03) when compared to its single-feature versions (marital status, 0.59/0.001), (high pressure, 0.357/0.04), (diabetes, 0.36/0.01), (sleep quality, 0.57/0.02), (level of physical activity, 0.57/0.01). Conclusions: The results demonstrate the architecture's capability to analyze multifeatured longitudinal data, supporting the generation of suggestions for concurrent modifications across multiple input features. Moreover, these suggestions align with findings in specialized literature.
引用
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页数:14
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