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

IF 2.2 3区 医学 Q2 HEALTH CARE SCIENCES & SERVICES Health Informatics Journal Pub Date : 2024-10-01 DOI:10.1177/14604582241291789
Clauirton Siebra, Mascha Kurpicz-Briki, Katarzyna Wac
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Abstract

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.

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利用纵向生活质量数据分析健康建议:QoL@TbA - 基于转换器的方法。
目的:健康建议系统建议通过行为调整来提高生活质量。然而,考虑到行为的多特征纵向演变,当前的方法并不便于生成或检查此类建议。本文建议使用基于深度学习转换器的模型,对行为改变建议进行分析。方法:我们采用了一种预测方法,即行为序列转换器(BST),它可以分析人类的时间常规和模式,并产生归纳结果。评估依赖于一项案例研究,该研究采用了英国老龄化纵向研究(ELSA)参与者(n = 2682)的行为历史和概况,根据输入的行为改变建议预测他们的心理情绪(正常、抑郁前、抑郁)。使用均方根误差(RMSE)和学习曲线来跟踪建议准确性的变化和可能存在的过度拟合问题。结果显示实验表明,与单特征模型(婚姻状况,0.59/0.001)、(高血压,0.357/0.04)、(糖尿病,0.36/0.01)、(睡眠质量,0.57/0.02)和(体育锻炼水平,0.57/0.01)相比,多特征模型的均方根误差值(0.28/0.03)更低。结论结果表明,该架构具有分析多特征纵向数据的能力,支持生成跨多个输入特征的并发修改建议。此外,这些建议与专业文献的研究结果一致。
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来源期刊
Health Informatics Journal
Health Informatics Journal HEALTH CARE SCIENCES & SERVICES-MEDICAL INFORMATICS
CiteScore
7.80
自引率
6.70%
发文量
80
审稿时长
6 months
期刊介绍: Health Informatics Journal is an international peer-reviewed journal. All papers submitted to Health Informatics Journal are subject to peer review by members of a carefully appointed editorial board. The journal operates a conventional single-blind reviewing policy in which the reviewer’s name is always concealed from the submitting author.
期刊最新文献
Empowering healthcare education: A multilingual ontology for medical informatics and digital health (MIMO) integrated to artificial intelligence powered training in smart hospitals. Analysis of health recommendations using longitudinal quality of life data: QoL@TbA - A transformer-based approach. Analysis of total RNA as a potential biomarker of developmental neurotoxicity in silico. Characterizing pituitary adenomas in clinical notes: Corpus construction and its application in LLMs. HealthCheck: A method for evaluating persuasive mobile health applications.
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