AbstractRacial and ethnic minorities bear a disproportionate burden of type 2 diabetes (T2D) and its complications, with social determinants of health (SDoH) recognized as key drivers of these disparities. Implementing efficient and effective social needs management strategies is crucial. We propose a machine learning analytic pipeline to calculate the individualized polysocial risk score (iPsRS), which can identify T2D patients at high social risk for hospitalization, incorporating explainable AI techniques and algorithmic fairness optimization.

摘要种族和少数民族承担着2型糖尿病（T2D）及其并发症的不成比例的负担，社会健康决定因素（SDoH）被认为是这些差异的关键驱动因素。实施高效和有效的社会需求管理战略至关重要。我们提出了一种机器学习分析流程来计算个性化多社会风险评分（iPsRS），该评分可以识别住院社会风险高的T2D患者，并结合可解释的AI技术和算法公平性优化。

We use electronic health records (EHR) data from T2D patients in the University of Florida Health Integrated Data Repository, incorporating both contextual SDoH (e.g., neighborhood deprivation) and person-level SDoH (e.g., housing instability). After fairness optimization across racial and ethnic groups, the iPsRS achieved a C statistic of 0.71 in predicting 1-year hospitalization.

我们使用佛罗里达大学健康综合数据库中T2D患者的电子健康记录（EHR）数据，结合了背景SDoH（例如邻里剥夺）和个人层面SDoH（例如住房不稳定）。在种族和族裔群体的公平性优化后，iPsRS在预测1年住院率方面达到了0.71的C统计量。

Our iPsRS can fairly and accurately screen patients with T2D who are at increased social risk for hospitalization..

我们的IPSR可以公平准确地筛查住院社会风险增加的T2D患者。。

IntroductionDiabetes affects 529 million people worldwide and the number is projected to more than double in the next three decades, reaching 1.3 billion by 20501. Over 90% of diabetes cases are type 2 diabetes (T2D)2. Existing research has shown that social determinants of health (SDoH)—“the conditions in the environments where people are born, live, learn, work, play, worship, and age,”3,4 such as education, income, and access to healthy food, play a critical role affecting a wide range of health outcomes, including the development and prognosis of T2D5,6,7.

引言糖尿病影响全球5.29亿人，预计在未来三十年内这一数字将翻一番以上，到20501年达到13亿。超过90%的糖尿病病例是2型糖尿病（T2D）2。现有研究表明，健康的社会决定因素（SDoH）-“人们出生，生活，学习，工作，玩耍，崇拜和年龄的环境条件”，3,4如教育，收入和获得健康食品，对影响广泛的健康结果起着至关重要的作用，包括T2D5,6,7的发展和预后。

Moreover, health disparities in T2D have been widely documented over the past decades8,9,10. Racial and ethnic minority groups and individuals experiencing social disadvantages—often rooted in their SDoH—bear a disproportionate burden of T2D and its complications11,12,13. As such, diabetes is a public crisis that must be managed with sensitivity to patients’ unmet social needs to improve T2D outcomes and health equity.The US healthcare system has begun embracing the need to address patients’ social needs, including screening for SDoH at the point of care.

。种族和少数民族群体以及经常根植于SDoH的社会弱势群体承担着T2D及其并发症的不成比例的负担11,12,13。因此，糖尿病是一种公共危机，必须对患者未满足的社会需求进行敏感管理，以改善T2D结果和健康公平。美国医疗保健系统已经开始接受满足患者社会需求的需求，包括在护理点筛查SDoH。

For example, the Centers for Medicare & Medicaid Services (CMS) have made proposals to require SDoH screening (e.g., housing stability, food insecurity, and access to transportation) in annual beneficiary health risk assessments. Despite this push, only 16–24% of clinics and hospitals provide SDoH screening14, and the actual utilization rate is very low15.

例如，医疗保险和医疗补助服务中心（CMS）提出建议，要求在年度受益人健康风险评估中进行SDoH筛查（例如，住房稳定性，粮食不安全和交通便利）。尽管如此，只有16-24%的诊所和医院提供SDoH筛查14，实际利用率非常低15。

In a national network of community health centers, only 2% of patients were screened for SDoH, and most had only one SDoH documented16. The reasons for the low rate of SDoH screening are multiple17. First, existing screening tools are not automated, making them difficult to adapt to clinical workflows18,19.

在全国社区卫生中心网络中，只有2%的患者接受了SDoH筛查，大多数患者只有一个SDoH记录16。SDoH筛查率低的原因是多方面的17。首先，现有的筛查工具不是自动化的，使得它们难以适应临床工作流程18,19。

In .

在。

Development of ML pipeline for iPsRS

Figure 8 shows our overall analytics pipeline. First, we imputed missing data and then adopted balance processing techniques (Step 1. Preprocessing). After that, we trained a set of machine learning models by using grid search cross-validation to identify the best hyperparameters (Step 2. ML Modeling).

图8显示了我们的整体分析流程。首先，我们估算缺失数据，然后采用平衡处理技术（步骤1）。预处理）。之后，我们通过使用网格搜索交叉验证来训练一组机器学习模型，以识别最佳超参数（步骤2）。ML建模）。

Next, we evaluated the model prediction performance (Step 3. Performance Assessment) and utilized XAI and causal structure learning techniques to identify important causal SDoH contributing to the hospitalization outcome (Step 4. Explanation). Finally, we assessed the algorithmic fairness (Step 5. Fairness Assessment) and implemented a range of fairness issue mitigation algorithms to address the identified bias (Step 6.

接下来，我们评估了模型预测性能（步骤3）。性能评估），并利用XAI和因果结构学习技术来确定有助于住院结果的重要因果SDoH（步骤4）。解释）。最后，我们评估了算法的公平性（步骤5）。。

Potential Bias Mitigation).Fig. 8: Data analytics pipeline for iPsRS.This pipeline contains six steps: preprocessing, machine learning modeling, performance assessment, explanation, fairness assessment, and potential bias mitigation. Attribution: the icons for gear, graph, and brain were originally designed by Freepik (www.freepik.com).

潜在的偏见缓解）。图8:IPSR的数据分析管道。该管道包含六个步骤：预处理，机器学习建模，性能评估，解释，公平性评估和潜在的偏见缓解。属性：齿轮、图形和大脑的图标最初是由Freepik（www.Freepik.com）设计的。

The other icons were designed by Vecteezy, including: <a href=https://www.vecteezy.com/free-vector/magnifying-glass>Magnifying Glass Vectors by Vecteezy </a>, <a href = “https://www.vecteezy.com/vector-art/45358325-a-set-of-icons-that-include-books-law-and-other-items”>a set of icons that include books, law, and other items Vectors by Vecteezy </a>, <a href = “https://www.vecteezy.com/vector-art/680841-set-of-health-checkup-thin-line-and-pixel-perfect-icons-for-any-web-and-app-project”>Set of Health Checkup thin line and pixel perfect icons for any web and app project.

其他图标由Vecteezy设计，包括：=https://www.vecteezy.com/free-vector/magnifying-glass>通过Vecteezy放大玻璃向量，a href=“a”https://www.vecteezy.com/vector-art/45358325-a-set-of-icons-that-include-books-law-and-other-items“>一组图标，包括书籍、法律和Vecteezy的其他项目向量，a href=”https://www.vecteezy.com/vector-art/680841-set-of-health-checkup-thin-line-and-pixel-perfect-icons-for-any-web-and-app-project“>一套适用于任何web和应用程序项目的健康检查细线条和像素完美图标。

Vectors by Vecteezy </a>, <a href=https://www.vecteezy.com/free-vector/heart-rate>Heart Rate Vectors by Vecteezy </a>.Full size image.

Vectors by Vecteezy </a>, <a href=https://www.vecteezy.com/free-vector/heart-rate>Heart Rate Vectors by Vecteezy </a>.Full size image.

Data preprocessingWe imputed missing values using the “unknown” label for categorical variables and the mean for continuous variables to ensure the ML models can work smoothly. Next, we proceeded to create dummy variables for the categorical variables for the models to understand and applied min-max normalization to the continuous variables for improving the performance of regularization models (e.g., Lasso).

数据预处理我们使用分类变量的“未知”标签和连续变量的平均值来估算缺失值，以确保ML模型能够顺利工作。接下来，我们继续为模型的分类变量创建虚拟变量，以理解并将最小-最大归一化应用于连续变量，以提高正则化模型（例如Lasso）的性能。

Then, we employed random over-sampling (ROS), random under-sampling (RUS), and under-sampling by matching on Charlson Comorbidity Index (CCI)57 to address data imbalance before model training. ROS randomly duplicates the minority samples and RUS aims to randomly remove samples in the majority class.

然后，我们采用随机过采样（ROS），随机欠采样（RUS）和通过匹配Charlson合并症指数（CCI）57进行欠采样，以解决模型训练前的数据不平衡问题。。

CCI is a method of classifying the comorbidities of patients and can be a clinical factor for predicting hospitalization and mortality58. We used CCI to match a pair of majority and minority samples and created a balanced dataset for modeling training.Machine learning model development for iPsRSWe developed the iPsRS model for predicting hospitalizations in patients with T2D using three sets of input features: (1) individual-level SDoH only, (2) contextual-level SDoH only, and (3) individual- and contextual-level SDoH combined.

CCI是一种对患者合并症进行分类的方法，可以作为预测住院和死亡率的临床因素58。我们使用CCI匹配一对多数和少数样本，并创建了一个用于建模训练的平衡数据集。iPsRS的机器学习模型开发我们使用三组输入特征开发了iPsRS模型，用于预测T2D患者的住院情况：（1）仅个人层面的SDoH，（2）仅上下文层面的SDoH，以及（3）个人层面和上下文层面的SDoH相结合。

Two classes of commonly used ML approaches, linear and tree-based models, were employed. For the linear models, we included a range of hyperparameters and penalty functions that can be utilized in constructing different models, including logistic regression59, lasso regression60, ridge regression61, and ElasticNet62.

采用了两类常用的ML方法，线性和基于树的模型。对于线性模型，我们包括一系列超参数和惩罚函数，可用于构建不同的模型，包括逻辑回归59，套索回归60，岭回归61和ElasticNet62。

For the tree-based models, we selected Extreme Gradient Boosting (XGBoost), which is widely recognized as one of the best-in-class algorithms for decision-tree-based models and has shown remarkable prediction performance in a wide ra.

对于基于树的模型，我们选择了极端梯度提升（XGBoost），它被广泛认为是基于决策树的模型的同类最佳算法之一，并且在广泛的ra中显示出显着的预测性能。

Data availability

数据可用性

Data from UF Health IDR can be requested through https://idr.ufhealth.org/research-services/data-request-form/. Since the UF Health data is a HIPAA-limited data set, a data use agreement needs to be established with the UF Health IDR research team. The relevant data for each figure is provided in the Source Data file. Source data are provided with this paper..

可以通过以下方式请求UF Health IDR的数据https://idr.ufhealth.org/research-services/data-request-form/.由于UF Health数据是HIPAA有限的数据集，因此需要与UF Health IDR研究团队建立数据使用协议。。本文提供了源数据。。

Code availability

代码可用性

We have created a GitHub repository for the current study (https://github.com/uf-hobi-informatics-lab/iPsRS_Public) where we have uploaded our Python code. The repository is publicly available for access.

我们为当前的研究创建了一个GitHub存储库(https://github.com/uf-hobi-informatics-lab/iPsRS_Public)我们在那里上传了Python代码。该存储库可公开访问。

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Shenkman & Jiang BianDepartment of Pharmaceutical Outcomes and Policy, University of Florida, Gainesville, FL, USAJingchuan Guo, Yao An Lee, Ying Lu, Wei-Han Chen & Huilin TangDivision of Endocrinology, Diabetes and Metabolism, College of Medicine, University of Florida, Gainesville, FL, USAWilliam T.

Shenkman和Jiang BianDepartment of Pharmaceutical Outcomes and Policy，佛罗里达大学，盖恩斯维尔，佛罗里达州，USAJingchuan Guo，Yao An Lee，Ying Lu，Wei Han Chen＆Huilin Tang佛罗里达大学医学院内分泌，糖尿病和代谢系，盖恩斯维尔，佛罗里达州，USAWilliam T。

DonahooDepartment of Surgery, College of Medicine— Jacksonville, University of Florida, Jacksonville, FL, USALori BilelloDepartment of Community Health and Family Medicine, College of Medicine, University of Florida, Jacksonville, FL, USAAaron A. SaguilDivision of General Internal Medicine, Department of Medicine, College of Medicine, University of Florida, Gainesville, FL, USAEric RosenbergAuthorsYu HuangView author publicationsYou can also search for this author in.

佛罗里达大学杰克逊维尔医学院外科，佛罗里达州杰克逊维尔，美国佛罗里达州杰克逊维尔，佛罗里达大学医学院社区健康与家庭医学系，佛罗里达大学杰克逊维尔，佛罗里达州，美国亚伦A.Sagildivision of General Internal Medicine，Department of Medicine，College of Medicine，佛罗里达大学医学院，盖恩斯维尔，佛罗里达州，美国埃里克·罗森伯·高索西（Eric RosenbergAuthorsYu HuangView）作者出版物你也可以在中搜索这位作者。

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PubMed Google ScholarJiang BianView author publicationsYou can also search for this author in

PubMed Google ScholarJiang BianView作者出版物您也可以在

PubMed Google ScholarContributionsConceptualization, J.G., J.B., and W.T.D.; methodology, Y.H., J.G., and J.B.; formal analysis, Y.H.; data curation, Z.F., Y. Lee., W.H.C., and H.T.; resources, J.G. and J.B.; writing—initial draft, Y.H., and J.G.; critical review and editing, J.G., J.B., W.T.D., Z.F., Y.

PubMed谷歌学术贡献概念化，J.G.，J.B。和W.T.D。；方法论，Y.H.，J.G。和J.B。；形式分析，Y.H。；数据管理，Z.F.，Y.Lee。，W、 H.C.和H.T。；资源，J.G.和J.B。；。；批判性评论和编辑，J.G.，J.B.，W.T.D.，Z.F.，Y。

Lu., W.H.C., H.T., L.B., A.A.S., E.R., and E.A.S.; supervision: J.B. All authors have read and agreed to the published version of the manuscript.Corresponding authorCorrespondence to.

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Jiang Bian.Ethics declarations

江边。道德宣言

Competing interests

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The authors declare no competing interests.

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Peer review

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Nature Communications thanks Emanuele Frontoni, Ayis Pyrros, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. A peer review file is available.

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Reprints and permissionsAbout this articleCite this articleHuang, Y., Guo, J., Donahoo, W.T. et al. A fair individualized polysocial risk score for identifying increased social risk in type 2 diabetes.

转载和许可本文引用本文Huang，Y.，Guo，J.，Donahoo，W.T.等人，一个公平的个体化多社会风险评分，用于识别2型糖尿病增加的社会风险。

Nat Commun 15, 8653 (2024). https://doi.org/10.1038/s41467-024-52960-9Download citationReceived: 29 November 2023Accepted: 27 September 2024Published: 05 October 2024DOI: https://doi.org/10.1038/s41467-024-52960-9Share 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.

《国家公社》158653（2024）。https://doi.org/10.1038/s41467-024-52960-9Download引文接收日期：2023年11月29日接收日期：2024年9月27日发布日期：2024年10月5日OI：https://doi.org/10.1038/s41467-024-52960-9Share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。

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