北京生物醫(yī)學(xué)工程

基于貝葉斯網(wǎng)絡(luò)的老年綜合評估過程簡化方法

A simplified method for elderly comprehensive geriatric assessment process based on Bayesian network

作者：李關(guān)東鄒函怡王藝樺許楊孫怡寧馬祖長高理升

單位：中國科學(xué)院合肥物質(zhì)科學(xué)研究院（合肥 230031）<br />中國科學(xué)技術(shù)大學(xué)（合肥  230026）<br />蚌埠醫(yī)學(xué)院護理學(xué)院（安徽蚌埠233030）<br />通信作者：高理升, 副研究員。E-mail：[email protected] 

關(guān)鍵詞：老年綜合評估；問卷調(diào)查；貝葉斯網(wǎng)絡(luò)；概率圖模型；人工智能

分類號：R318

出版年·卷·期（頁碼）：2022·41·6（589-596）

摘要：

目的探究基于貝葉斯網(wǎng)絡(luò)的老年綜合評估過程簡化方法在社區(qū)老年綜合評估（comprehensive geriatric assessment, CGA）中的應(yīng)用價值，為普及社區(qū)CGA工作提供一種可行的技術(shù)手段。方法以2018年中國老年健康影響因素跟蹤調(diào)查（Chinese Longitudinal Healthy Longevity Survey，CLHLS）的橫斷面調(diào)查中的958位老年人為研究對象，根據(jù)CGA內(nèi)容篩選有效樣本。選擇70%數(shù)據(jù)作為訓(xùn)練集構(gòu)建貝葉斯網(wǎng)絡(luò)模型并設(shè)計問卷調(diào)查算法。構(gòu)建FPQM模型與基于貝葉斯網(wǎng)絡(luò)的老年綜合評估過程簡化方法進行比較分析。以平均準(zhǔn)確率、平均簡化率、AFβ、占用空間作為模型的評價指標(biāo)。結(jié)果基于貝葉斯網(wǎng)絡(luò)的老年綜合評估過程簡化方法在測試集上的平均準(zhǔn)確率、AFβ均最高，分別為0.983 6，0.849 9；占用空間最小，為23 kB。FPQM的平均簡化率較高，為0.973 7；但平均準(zhǔn)確率、AFβ較低，分別為0.842 0，0.787 6；占用空間巨大，達4.13 GB。結(jié)論基于貝葉斯網(wǎng)絡(luò)的老年綜合評估過程簡化方法在準(zhǔn)確率、綜合性能和空間占用方面均明顯優(yōu)于已報道的簡化算法。在當(dāng)前老齡化日益嚴(yán)峻背景下，該方法對于基層社區(qū)和養(yǎng)老機構(gòu)提升CGA工作效率具有重要應(yīng)用價值。

Objective To explore the application value of the simplified method based on Bayesian network in community comprehensive geriatric assessment(CGA), and provides a feasible technical means for the popularization of community CGA. Methods A total of 958 elderly people from The Chinese Longitudinal Healthy Longevity Survey(CLHLS) 2018 cross-sectional survey were selected as the research subjects, and valid samples were selected according to the CGA content. 70% data were selected as training set to construct bayesian network model and design questionnaire algorithm. The FPQM model is compared with the simplified method based on Bayesian network. Average accuracy, average simplification rate, AFβ and occupied space were used as evaluation indexes of the model. Results The average accuracy and AFβ of the simplified method based on Bayesian network on the test set were 0.983 6 and 0.849 9, respectively. The minimum space is 23 kB. The average simplification rate of FPQM was 0.973 7. But the average accuracy and AFβ were lower, 0.842 0 and 0.787 6, respectively. It takes up a huge 4.13 GB of space. Conclusions The simplified method based on Bayesian network is superior to the reported simplified algorithm in terms of accuracy, comprehensive performance and space occupancy. In the context of increasingly severe aging, this method has important application value for grassroots communities and pension institutions to improve the work efficiency of CGA.

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