AbstractFrailty is a state that is closely associated with adverse health outcomes in the aging process. The frailty index (FI), which measures frailty in terms of cumulative deficits, has been widely used for frailty assessment in elderly people, and its advantage of self-reported information collection makes it applicable to a broader group of elderly people.

摘要虚弱是一种与衰老过程中不良健康结果密切相关的状态。脆弱指数（FI）以累积赤字衡量脆弱性，已被广泛用于老年人的脆弱性评估，其自我报告信息收集的优势使其适用于更广泛的老年人群体。

Our study aims to simplify the Frailty Index Assessment Scale, while maintaining its reliability and accuracy, to easily and quickly assess frailty in elderly people. In this study, participants (age ≥ 65 years) from the Chinese Longitudinal Healthy Longevity Survey (CLHLS), which had 13,339, 372 and 1214 participants in 2008, 2011, and 2014, respectively, were used.

我们的研究旨在简化脆弱指数评估量表，同时保持其可靠性和准确性，以轻松快速地评估老年人的脆弱性。。

The 2008 dataset was split into 80% for training and 20% for internal validation, and the data from 2011 to 2014 as external validation. In order to obtain effective predictors, we used Lasso regression, Boruta algorithm and random forest classifier score for feature selection. We used six models for predictive model construction and evaluated the models in the validation dataset.

2008年的数据集分为80%用于培训，20%用于内部验证，2011年至2014年的数据作为外部验证。为了获得有效的预测因子，我们使用Lasso回归，Boruta算法和随机森林分类器得分进行特征选择。我们使用六个模型进行预测模型构建，并在验证数据集中评估了模型。

Model performance was measured by area under the curve (AUC), accuracy and F1 score. Logistic regression was found to be the best performing and most interpretable algorithm with AUC, accuracy and F1 of 0.974, 0.932 and 0.880 for the validation dataset, respectively. The AUCs for the external independent validation dataset were 0.963 and 0.977, respectively.

模型性能通过曲线下面积（AUC），准确性和F1评分来衡量。发现逻辑回归是性能最好且可解释性最强的算法，验证数据集的AUC，准确度和F1分别为0.974、0.932和0.880。外部独立验证数据集的AUC分别为0.963和0.977。

Subgroup analysis showed that the model had good predictive power in both males and females. The predictive power was stronger among the elderly people over 80 years old, with AUC, accuracy and F1 of 0.973,0.914, and 0.893, respectively. The model also obtained good predictive power in the case of FI measured by differe.

亚组分析表明，该模型对男性和女性均具有良好的预测能力。80岁以上老年人的预测能力更强，AUC，准确度和F1分别为0.973,0.914和0.893。该模型在Differ测量的FI的情况下也获得了良好的预测能力。

IntroductionFrailty reflects the physical and psychological health deficits and social deficits that accumulate during the aging process, as well as the degradation of the organism’s defenses due to loss of physiological reserves1,2,3, a state that is strongly associated with adverse health outcomes, including disability, dependency, falls, need for long-term care, and death4,5,6.

。

And this state usually appears and develops in elderly people4,5, with a higher prevalence of frailty in those over 80 years of age. The current situation of rapidly aging populations is a particularly serious challenge in global public health, which has led to an increase in the number of frail elderly people and further burdened the current public health care system.

这种状态通常在老年人中出现和发展[4,5]，80岁以上人群的虚弱患病率较高。目前人口迅速老龄化的状况是全球公共卫生领域的一个特别严重的挑战，这导致体弱老年人的数量增加，并进一步加重了当前公共卫生保健系统的负担。

Frailty is considered to be an early stage of disability. And, because it is reversible, this suggests that appropriate intervention at the right time can prevent, delay or even reverse this state7.The frailty index (FI), which calculates the proportion of individual deficits, can be used to characterize the frailty status of elderly people, as suggested by Rockwood et al.3 and Mitnitsk et al.8.

虚弱被认为是残疾的早期阶段。并且，由于它是可逆的，这表明在正确的时间进行适当的干预可以预防，延迟甚至逆转这种状态7。脆弱指数（FI）可以用来描述老年人的脆弱状态，如Rockwood等人3和Mitnitsk等人8所建议的。

And the frailty index describing frailty status has been well validated and widely used in the assessment of frailty in elderly people, and applies to a broader population of elderly people than the well-known definition of frailty proposed by Fried et al.9 (a syndrome or phenotype of at least three of the five criteria: weight loss, fatigue, weak grip, slow walking speed, and low physical activity), and as EAMONN et al.10 reported, older inpatients are often unable to complete performance-based tests.

描述脆弱状态的脆弱指数已经得到了很好的验证，并广泛用于评估老年人的脆弱性，并且适用于比Fried等人提出的众所周知的脆弱定义更广泛的老年人9（五个标准中至少三个的综合征或表型：体重减轻，疲劳，握力弱，步行速度慢和体力活动少），正如EAMONN等[10]报道的那样，老年住院患者通常无法完成基于表现的测试。

The number of indicators used to construct the FI in previous studies using FI to measure frailty varies, for example, Goggins et al.11 u.

在先前使用FI测量脆弱性的研究中，用于构建FI的指标数量各不相同，例如，Goggins等[11]。

Data availability

数据可用性

All data used in this study were stored at https://opendata.pku.edu.cn and available upon request.

本研究中使用的所有数据均存储在https://opendata.pku.edu.cn并可根据要求提供。

AbbreviationsCLHLS:

缩写CLHLS：

Chinese Longitudinal Healthy Longevity Survey

中国健康长寿纵向调查

FI:

Frailty Index

脆弱指数

LR:

Logistic Regression

逻辑回归

RF:

射频：

Random Forest

随机森林

SHLNN:

SHLNN公司：

Single-hidden-layer Neural Network

单隐层神经网络

SVM:

支持向量机：

Support Vector Machine

支持向量机

XGBoost:

XGBoost：

Extreme gradient boosting

极端梯度提升

AUC:

AUC：

Area under the curve

ReferencesCollard, R. M., Boter, H., Schoevers, R. A. & Oude Voshaar, R. C. Prevalence of frailty in community-dwelling older persons: A systematic review. J. Am. Geriatr. Soc. 60(8), 1487–1492 (2012).Article

参考文献Collard，R.M.，Boter，H.，Schoevers，R.A。和Oude Voshaar，R.C。社区居住老年人虚弱的患病率：系统评价。J、 。Soc.60（8），1487–1492（2012）。文章

PubMed

PubMed

Google Scholar

谷歌学者

Rockwood, K. et al. A global clinical measure of fitness and frailty in elderly people. Cmaj 173(5), 489–495 (2005).Article

Rockwood，K.等人，《老年人健康和虚弱的全球临床测量》。Cmaj 173（5），489-495（2005）。文章

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Rockwood, K., Hogan, D. B. & MacKnight, C. Conceptualisation and measurement of frailty in elderly people. Drugs Aging 17(4), 295–302 (2000).Article

Rockwood，K.，Hogan，D.B。和MacKnight，C。老年人脆弱性的概念化和测量。药物老化17（4），295-302（2000）。文章

PubMed

PubMed

Google Scholar

谷歌学者

Fried, L. P., Ferrucci, L., Darer, J., Williamson, J. D. & Anderson, G. Untangling the concepts of disability, frailty, and Comorbidity: Implications for improved targeting and care. J. Gerontol. Ser. A 59(3), M255–M63 (2004).Article

Fried，L.P.，Ferrucci，L.，Darer，J.，Williamson，J.D。＆Anderson，G。解开残疾，虚弱和合并症的概念：对改善靶向和护理的影响。J、 Gerontol公司。序列号。A 59（3），M255–M63（2004）。文章

Google Scholar

谷歌学者

Clegg, A., Young, J., Iliffe, S., Rikkert, M. O. & Rockwood, K. Frailty in elderly people. Lancet 381(9868), 752–762 (2013).Article

Clegg，A.，Young，J.，Iliffe，S.，Rikkert，M.O。和Rockwood，K。老年人的脆弱。柳叶刀381（9868），752-762（2013）。文章

PubMed

PubMed

Google Scholar

谷歌学者

Zhu, A., Yan, L., Wu, C. & Ji, J. S. Residential greenness and Frailty among older adults: A longitudinal cohort in China. J. Am. Med. Dir. Assoc. 21(6), 759–65e2 (2020).Article

Zhu，A.，Yan，L.，Wu，C.＆Ji，J.S。老年人的住宅绿色和脆弱性：中国的纵向队列。J、 《美国医学会杂志》21（6），759–65e2（2020）。文章

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Lorenzo-López, L. et al. Nutritional determinants of frailty in older adults: A systematic review. BMC Geriatr. 17(1), 108 (2017).Article

Lorenzo-López，L.等人，《老年人虚弱的营养决定因素：系统综述》。BMC老年人。17（1），108（2017）。文章

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Mitnitski, A. B., Song, X. & Rockwood, K. The estimation of relative fitness and frailty in community-dwelling older adults using self-report data. J. Gerontol. Biol. Sci. Med. Sci. 59(6), M627–M632 (2004).Article

Mitnitski，A.B.，Song，X。＆Rockwood，K。使用自我报告数据估计社区居住老年人的相对适应性和脆弱性。J、 Gerontol公司。生物科学。医学科学。59（6），M627–M632（2004）。文章

Google Scholar

谷歌学者

Fried, L. P. et al. Frailty in older adults: Evidence for a phenotype. J. Gerontol. Biol. Sci. Med. Sci. 56(3), M146–M156 (2001).Article

Fried，L.P.等人，《老年人的脆弱：表型的证据》。J、 Gerontol公司。生物科学。医学科学。56（3），M146–M156（2001）。文章

Google Scholar

谷歌学者

Eeles, E. M., White, S. V., O’Mahony, S. M., Bayer, A. J. & Hubbard, R. E. The impact of frailty and delirium on mortality in older inpatients. Age Ageing 41(3), 412–416 (2012).Article

Eeles，E.M.，White，S.V.，O'Mahony，S.M.，Bayer，A.J。＆Hubbard，R.E。虚弱和deli妄对老年住院患者死亡率的影响。年龄41（3），412-416（2012）。文章

PubMed

PubMed

Google Scholar

谷歌学者

Goggins, W. B., Woo, J., Sham, A. & Ho, S. C. Frailty index as a measure of biological age in a Chinese population. J. Gerontol. Biol. Sci. Med. Sci. 60(8), 1046–1051 (2005).Article

Goggins，W.B.，Woo，J.，Sham，A。＆Ho，S.C。脆弱指数作为中国人群生物年龄的量度。J、 Gerontol公司。生物科学。医学科学。60（8），1046-1051（2005）。文章

Google Scholar

谷歌学者

Gu, D. et al. Frailty and mortality among Chinese at advanced ages. J. Gerontol. B Psychol. Sci. Soc. Sci. 64(2), 279–289 (2009).Article

Gu，D。等人。中国老年人的虚弱和死亡率。J、 Gerontol公司。B心理学。科学。社会科学。64（2），279-289（2009）。文章

PubMed

PubMed

Google Scholar

谷歌学者

Sha, S., Xu, Y. & Chen, L. Loneliness as a risk factor for frailty transition among older Chinese people. BMC Geriatr. 20(1), 300 (2020).Article

Sha，S.，Xu，Y。＆Chen，L。孤独是中国老年人脆弱转变的危险因素。BMC老年人。20（1），300（2020）。文章

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Chikersal, P. et al. Detecting depression and predicting its onset using longitudinal symptoms captured by passive sensing: A machine learning approach with robust feature selection. ACM Trans. Comput. Hum. Interact. 28(1), Article 3 (2021).Qiao, J. & editor A systematic review of machine learning approaches for mental disorder prediction on social media.

Chikersal，P。等人。使用被动感知捕获的纵向症状检测抑郁症并预测其发作：具有强大特征选择的机器学习方法。ACM变速器。计算机。嗯。互动。28（1），第3条（2021）。Qiao，J。&编辑对社交媒体上用于精神障碍预测的机器学习方法的系统评价。

In 2020 International Conference on Computing and Data Science (CDS), 1–2 Aug. 2020 (2020).Sun, Y. H., Liu, Q., Lee, N. Y., Li, X. & Lee, K. A novel machine learning approach to shorten depression risk assessment for convenient uses. J. Affect. Disord. 312, 275–291 (2022).Article .

2020年国际计算与数据科学会议（CDS），2020年8月1日至2日（2020年）。Sun，Y.H.，Liu，Q.，Lee，N.Y.，Li，X。＆Lee，K。一种新的机器学习方法，用于缩短抑郁症风险评估以方便使用。J、 影响。混乱。312275-291（2022）。文章。

PubMed

PubMed

Google Scholar

谷歌学者

Wang, S. et al. Using machine learning algorithms for predicting cognitive impairment and identifying modifiable factors among Chinese elderly people. Front. Aging Neurosci. 14, 977034 (2022).Article

Wang，S.等人。使用机器学习算法预测中国老年人的认知障碍并识别可修改因素。正面。衰老神经科学。14977034（2022年）。文章

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Aznar-Tortonda, V. et al. Detection of frailty in older patients using a mobile app: Cross-sectional observational study in primary care. Br. J. Gen. Pract. 70(690), e29–e35 (2020).Article

Aznar Tortonda，V。等人。使用移动应用程序检测老年患者的虚弱：初级保健横断面观察研究。Br.J.Gen.Pract.公司。70（690），e29–e35（2020）。文章

PubMed

PubMed

Google Scholar

谷歌学者

Man, W., Wang, S. & Yang, H. Exploring the spatial-temporal distribution and evolution of population aging and social-economic indicators in China. BMC Public Health 21(1), 966 (2021).Article

Man，W.，Wang，S。＆Yang，H。探索中国人口老龄化和社会经济指标的时空分布和演变。BMC公共卫生21（1），966（2021）。文章

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Shen, K., Zhang, B. & Feng, Q. Association between tea consumption and depressive symptom among Chinese older adults. BMC Geriatr. 19(1), 246 (2019).Article

Shen，K.，Zhang，B。＆Feng，Q。中国老年人饮茶与抑郁症状之间的关系。BMC老年人。19（1），246（2019）。文章

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Feng, Y., Liu, E., Yue, Z., Zhang, Q. & Han, T. The evolutionary trends of Health behaviors in Chinese Elderly and the influencing factors of these trends: 2005–2014. Int. J. Environ. Res. Public. Health 16(10) (2019).Zeng, Y. Towards deeper research and better policy for healthy aging—Using the unique data of Chinese longitudinal healthy longevity survey.

Feng，Y.，Liu，E.，Yue，Z.，Zhang，Q.＆Han，T。中国老年人健康行为的演变趋势及其影响因素：2005-2014。内景J.环境。公共资源。健康16（10）（2019）。Zeng，Y。利用中国纵向健康长寿调查的独特数据，对健康老龄化进行更深入的研究和更好的政策。

China Econ. J. 5(2–3), 131–149 (2012).Article .

中国经济。J、 5（2-3），131-149（2012）。文章。

ADS

广告

Google Scholar

谷歌学者

Yi, Z., Vaupel, J. W., Zhenyu, X., Chunyuan, Z. & Yuzhi, L. The Healthy Longevity Survey and the Active Life Expectancy of the Oldest Old in China. Population: An English Selection. ;13(1):95–116. (2001).Yi, Z. Reliability of Age Reporting among the Chinese Oldest-Old in the CLHLS Datasets (Springer Netherlands).Searle, S.

Yi，Z.，Vaupel，J.W.，Zhenyu，X.，Chunyuan，Z。＆Yuzhi，L。健康长寿调查和中国老年人的积极预期寿命。人口：英国选择；13（1）：95-116。（2001年）。Yi，Z。CLHLS数据集中中国最年长老人年龄报告的可靠性（荷兰施普林格）。塞尔，S。

D., Mitnitski, A., Gahbauer, E. A., Gill, T. M. & Rockwood, K. A standard procedure for creating a frailty index. BMC Geriatr. 8, 24 (2008).Article .

D、 ，Mitnitski，A.，Gahbauer，E.A.，Gill，T.M。和Rockwood，K。创建脆弱指数的标准程序。BMC老年人。8，24（2008）。文章。

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Gao, T. et al. Long-term tea consumption reduces the risk of frailty in older Chinese people: Result from a 6-year longitudinal study. Front. Nutr. 9, 916791 (2022).Article

高，T。等人。长期喝茶可以降低中国老年人的虚弱风险：一项为期6年的纵向研究结果。正面。营养。9916791（2022）。文章

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Hoover, M., Rotermann, M., Sanmartin, C. & Bernier, J. Validation of an index to estimate the prevalence of frailty among community-dwelling seniors. Health Rep. 24(9), 10–17 (2013).PubMed

Hoover，M.，Rotermann，M.，Sanmartin，C。＆Bernier，J。验证一个指数来估计社区居住老年人的虚弱患病率。《健康代表》24（9），10–17（2013）。PubMed出版社

Google Scholar

谷歌学者

McEligot, A. J., Poynor, V., Sharma, R. & Panangadan, A. Logistic LASSO regression for dietary intakes and breast Cancer. Nutrients 12(9) (2020).Kursa, M. B. & Rudnicki, W. R. Feature selection with the Boruta Package. J. Stat. Softw. 36(11), 1–13 (2010).Article

McEligot，A.J.，Poynor，V.，Sharma，R。＆Panangadan，A。饮食摄入和乳腺癌的逻辑套索回归。营养素12（9）（2020）。Kursa，M.B。和Rudnicki，W.R。使用Boruta软件包进行功能选择。J、 统计软件。36（11），1-13（2010）。文章

Google Scholar

谷歌学者

Rahman, M. S., Rahman, M. K., Kaykobad, M., Rahman, M. S. & isGPT An optimized model to identify sub-golgi protein types using SVM and Random Forest based feature selection. Artif. Intell. Med. 84, 90–100 (2018).Article

Rahman，M.S.，Rahman，M.K.，Kaykobad，M.，Rahman，M.S。＆isGPT使用SVM和基于随机森林的特征选择来识别次高尔基体蛋白质类型的优化模型。人工制品。因特尔。医学杂志84,90-100（2018）。文章

PubMed

PubMed

Google Scholar

谷歌学者

Wu, Y., Xiang, C., Jia, M. & Fang, Y. Interpretable classifiers for prediction of disability trajectories using a nationwide longitudinal database. BMC Geriatr. 22(1), 627 (2022).Article

Wu，Y.，Xiang，C.，Jia，M。＆Fang，Y。使用全国纵向数据库预测残疾轨迹的可解释分类器。BMC老年人。22（1），627（2022）。文章

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Lv, Y. et al. Long-term fine particular exposure and incidence of frailty in older adults: Findings from the Chinese longitudinal healthy longevity survey. Age Ageing 52(2) (2023).Chen, Q. et al. Dynamic statistical model for predicting the risk of death among older Chinese people, using longitudinal repeated measures of the frailty index: A prospective cohort study.

Lv，Y.等人。老年人长期精细特殊暴露和虚弱发生率：中国纵向健康长寿调查的结果。年龄老化52（2）（2023）。Chen，Q.等人。使用纵向重复测量脆弱指数预测中国老年人死亡风险的动态统计模型：一项前瞻性队列研究。

Age Ageing 49(6), 966–973 (2020).Article .

年龄49（6），966-973（2020）。文章。

PubMed

PubMed

Google Scholar

谷歌学者

Xu, W., Liang, Y. & Lin, Z. Association between Neutrophil-Lymphocyte ratio and Frailty: The Chinese longitudinal healthy longevity survey. Front. Med. (Lausanne) 8, 783077 (2021).Article

Xu，W.，Liang，Y。＆Lin，Z。中性粒细胞-淋巴细胞比率与虚弱之间的关联：中国纵向健康长寿调查。正面。医学（洛桑）8783077（2021）。文章

PubMed

PubMed

Google Scholar

谷歌学者

Zhang, J., Wang, Q., Hao, W. & Zhu, D. Long-term food variety and dietary patterns are associated with frailty among Chinese older adults: A cohort study based on CLHLS from 2014 to 2018. Nutrients 14(20) (2022).Slaets, J. P. Vulnerability in the elderly: Frailty. Med. Clin. North. Am.

Zhang，J.，Wang，Q.，Hao，W。＆Zhu，D。长期食物种类和饮食模式与中国老年人的虚弱有关：一项基于2014年至2018年CLHLS的队列研究。营养素14（20）（2022）。Slaets，J.P。老年人的脆弱性：脆弱。医学临床。北方。上午。

90(4), 593–601 (2006).Article .

90（4），593-601（2006）。文章。

PubMed

PubMed

Google Scholar

谷歌学者

Rockwood, K., Fox, R. A., Stolee, P., Robertson, D. & Beattie, B. L. Frailty in elderly people: An evolving concept. Cmaj 150(4), 489–495 (1994).PubMed

Rockwood，K.，Fox，R.A.，Stolee，P.，Robertson，D。和Beattie，B.L。老年人的脆弱：一个不断发展的概念。Cmaj 150（4），489-495（1994）。PubMed出版社

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Rockwood, K., Mogilner, A. & Mitnitski, A. Changes with age in the distribution of a frailty index. Mech. Ageing Dev. 125(7), 517–519 (2004).Article

Rockwood，K.，Mogilner，A。＆Mitnitski，A。脆弱指数的分布随年龄而变化。机械。老龄化发展125（7），517-519（2004）。文章

PubMed

PubMed

Google Scholar

谷歌学者

Mitnitski, A. B., Graham, J. E., Mogilner, A. J. & Rockwood, K. Frailty, fitness and late-life mortality in relation to chronological and biological age. BMC Geriatr. 2, 1 (2002).Article

Mitnitski，A.B.，Graham，J.E.，Mogilner，A.J。＆Rockwood，K。脆弱，健康和晚年死亡率与年龄和生物年龄的关系。BMC老年人。2，1（2002）。文章

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Kulminski, A. et al. Cumulative index of health disorders as an indicator of aging-associated processes in the elderly: Results from analyses of the National Long Term Care Survey. Mech. Ageing Dev. 128(3), 250–258 (2007).Article

Kulminski，A.等人，《作为老年人衰老相关过程指标的健康障碍累积指数：国家长期护理调查分析结果》。机械。老龄化发展128（3），250–258（2007）。文章

PubMed

PubMed

Google Scholar

谷歌学者

Woo, J., Goggins, W., Sham, A. & Ho, S. C. Public health significance of the frailty index. Disabil. Rehabil. 28(8), 515–521 (2006).Article

Woo，J.，Goggins，W.，Sham，A。＆Ho，S.C。脆弱指数的公共卫生意义。《康复障碍》，28（8），515-521（2006）。文章

PubMed

PubMed

Google Scholar

谷歌学者

American Medical Association white paper on elderly health. Report of the Council on Scientific affairs. Arch. Intern. Med. 150(12), 2459–2472 (1990).Article

美国医学会老年健康白皮书。科学事务委员会的报告。拱门。实习生。医学杂志150（12），2459-2472（1990）。文章

Google Scholar

谷歌学者

Download referencesAcknowledgementsThe authors would like to thank the Center for Healthy Aging and Development Studies, Peking University for supporting the database.FundingThis work was supported by the National Natural Sciences Foundation of China [grant number 82304081].Author informationAuthor notesLi Huang and Huajian Chen contributed equally to this work and share first authorship.Authors and AffiliationsSchool of Public Health, Xinxiang Medical University, Xinxiang, 453003, ChinaLi Huang & Zhenzhen LiangSchool of Public Health, Wenzhou Medical University, Wenzhou, 325035, ChinaLi Huang & Huajian ChenAuthorsLi HuangView author publicationsYou can also search for this author in.

下载参考文献致谢作者要感谢北京大学健康老龄化与发展研究中心对该数据库的支持。资助这项工作得到了国家自然科学基金（批准号82304081）的支持。作者信息作者notesLi Huang和Huajian Chen对这项工作做出了同样的贡献，并分享了第一作者身份。作者和附属机构新乡医科大学公共卫生学院，新乡，453003，中国温州医科大学公共卫生学院黄丽珍，温州325035，黄丽珍，陈华健作者黄丽珍，作者出版物你也可以在中搜索这位作者。

PubMed Google ScholarHuajian ChenView author publicationsYou can also search for this author in

PubMed Google ScholarZhenzhen LiangView author publicationsYou can also search for this author in

PubMed谷歌学者梁振珍查看作者出版物您也可以在

PubMed Google ScholarContributionsLH: Formal analysis, Writing - Original Draft. HC: Validation, Visualization, Data Curation. ZL: Conceptualization, Supervision, Project administration. All authors read and approved the final manuscript.Corresponding authorCorrespondence to

PubMed谷歌学术贡献：正式分析，写作-原稿。HC：验证，可视化，数据管理。ZL：概念化，监督，项目管理。所有作者都阅读并批准了最终稿件。对应作者对应

Zhenzhen Liang.Ethics declarations

梁振珍。道德宣言

Competing interests

相互竞争的利益

The authors declare no competing interests.

作者声明没有利益冲突。

Ethics approval and consent to participate

道德批准和同意参与

Not applicable since the dataset used in the study is publicly available. All methods were carried out in accordance with relevant guidelines and regulations.

不适用，因为研究中使用的数据集是公开的。所有方法均按照相关指南和规定进行。

Content for publication

发布内容

Not applicable.

不适用。

Additional informationPublisher’s noteSpringer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Electronic supplementary materialBelow is the link to the electronic supplementary material.Supplementary Material 1Rights and permissions

Additional informationPublisher的noteSpringer Nature在已发布地图和机构隶属关系中的管辖权主张方面保持中立。电子补充材料流是指向电子补充材料的链接。补充材料1权利和许可

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material.

开放获取本文是根据知识共享署名非商业性NoDerivatives 4.0国际许可证授权的，该许可证允许以任何媒介或格式进行任何非商业性使用，共享，分发和复制，只要您对原始作者和来源给予适当的信任，提供知识共享许可证的链接，并指出您是否修改了许可材料。

You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

根据本许可证，您无权共享源自本文或其部分的改编材料。本文中的图像或其他第三方材料包含在文章的知识共享许可证中，除非该材料的信用额度中另有说明。如果材料未包含在文章的知识共享许可证中，并且您的预期用途未被法律法规允许或超出允许的用途，则您需要直接获得版权所有者的许可。

To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/..

要查看此许可证的副本，请访问http://creativecommons.org/licenses/by-nc-nd/4.0/..

Reprints and permissionsAbout this articleCite this articleHuang, L., Chen, H. & Liang, Z. Enhancing the convenience of frailty index assessment for elderly Chinese people with machine learning methods.

转载和许可本文引用本文Huang，L.，Chen，H。＆Liang，Z。使用机器学习方法增强中国老年人脆弱指数评估的便利性。

Sci Rep 14, 23227 (2024). https://doi.org/10.1038/s41598-024-74194-xDownload citationReceived: 07 May 2024Accepted: 24 September 2024Published: 05 October 2024DOI: https://doi.org/10.1038/s41598-024-74194-xShare this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy to clipboard.

Sci Rep 1423227（2024）。https://doi.org/10.1038/s41598-024-74194-xDownload引文接收日期：2024年5月7日接受日期：2024年9月24日发布日期：2024年10月5日OI：https://doi.org/10.1038/s41598-024-74194-xShare本文与您共享以下链接的任何人都可以阅读此内容：获取可共享链接对不起，本文目前没有可共享的链接。复制到剪贴板。

Provided by the Springer Nature SharedIt content-sharing initiative

由Springer Nature SharedIt内容共享计划提供

KeywordsElderly peopleMachine learningFrailty indexShortened questionnaire

关键词Selderly People Machine Learning Fraily IndexShorted问卷

CommentsBy submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

评论通过提交评论，您同意遵守我们的条款和社区指南。如果您发现有虐待行为或不符合我们的条款或准则，请将其标记为不合适。