Innovations in early detection of chronic non-communicable diseases among adolescents through an easy-to-Use AutoML paradigm.
IF 2
3区 医学
Q2 HEALTH POLICY & SERVICES
引用次数: 0
Abstract
In this research, we present an interpretable AutoML approach for the early diagnosis of hypertension and hyperinsulinemia among adolescents, conditions that are critical to identify during these formative years due to their requirement for lifelong care and monitoring. The dataset, collected from 2019 to 2022 by Serbia's Healthcare Center through an observational cross-sectional study, posed challenges common to medical datasets, including imbalances, data scarcity, and a need for transparent, explainable predictive models. To counter these issues, we utilized three AutoML frameworks - AutoGluon, H2O, and MLJAR - in conjunction with a Tabular Variational Autoencoder (TVAE) to synthetically augment the data points, Prinicipal Component Analysis (PCA) for dimensionality reduction, and SHapley Additive exPlanations (SHAP) and Permutation feature importance analyses to extract insights from the results. AutoGluon outperformed the others on the original dataset, delivering better results with weighted ensemble models for both conditions under a 12-minute budget-time constraint and maintaining all evaluation metrics below a 4% threshold, all without the need for further scaling or calibration in the experimental setup. Our research underscores the broad applicability of the current AutoML paradigm, highlighting its particular benefits for the healthcare domain and diagnostics, where such advanced tools can enhance patient care.
通过易于使用的AutoML模式在青少年慢性非传染性疾病的早期检测方面进行创新。
在这项研究中，我们提出了一种可解释的AutoML方法，用于青少年高血压和高胰岛素血症的早期诊断，由于他们需要终身护理和监测，在这些形成时期识别这些疾病至关重要。该数据集由塞尔维亚医疗保健中心通过一项观察性横断面研究从2019年至2022年收集，提出了医疗数据集常见的挑战，包括不平衡、数据稀缺以及对透明、可解释的预测模型的需求。为了解决这些问题，我们使用了三个AutoML框架——AutoGluon、H2O和MLJAR——结合一个表变分自编码器（TVAE）来综合增加数据点，主成分分析（PCA）用于降维，SHapley加性解释（SHAP）和排列特征重要性分析来从结果中提取见解。AutoGluon在原始数据集上的表现优于其他工具，在12分钟的预算时间限制下，通过加权集成模型在两种条件下提供了更好的结果，并将所有评估指标保持在4%以下的阈值，所有这些都不需要在实验设置中进一步缩放或校准。我们的研究强调了当前AutoML范式的广泛适用性，强调了其对医疗保健领域和诊断的特殊好处，这些先进的工具可以增强患者护理。
本文章由计算机程序翻译，如有差异，请以英文原文为准。
求助全文
约1分钟内获得全文
求助全文
来源期刊
Health Care Management Science
HEALTH POLICY & SERVICES-
CiteScore
7.20
自引率
5.60%
发文量
40
期刊介绍：
Health Care Management Science publishes papers dealing with health care delivery, health care management, and health care policy. Papers should have a decision focus and make use of quantitative methods including management science, operations research, analytics, machine learning, and other emerging areas. Articles must clearly articulate the relevance and the realized or potential impact of the work. Applied research will be considered and is of particular interest if there is evidence that it was implemented or informed a decision-making process. Papers describing routine applications of known methods are discouraged.
Authors are encouraged to disclose all data and analyses thereof, and to provide computational code when appropriate.
Editorial statements for the individual departments are provided below.
Health Care Analytics
Departmental Editors:
Margrét Bjarnadóttir, University of Maryland
Nan Kong, Purdue University
With the explosion in computing power and available data, we have seen fast changes in the analytics applied in the healthcare space. The Health Care Analytics department welcomes papers applying a broad range of analytical approaches, including those rooted in machine learning, survival analysis, and complex event analysis, that allow healthcare professionals to find opportunities for improvement in health system management, patient engagement, spending, and diagnosis. We especially encourage papers that combine predictive and prescriptive analytics to improve decision making and health care outcomes.
The contribution of papers can be across multiple dimensions including new methodology, novel modeling techniques and health care through real-world cohort studies. Papers that are methodologically focused need in addition to show practical relevance. Similarly papers that are application focused should clearly demonstrate improvements over the status quo and available approaches by applying rigorous analytics.
Health Care Operations Management
Departmental Editors:
Nilay Tanik Argon, University of North Carolina at Chapel Hill
Bob Batt, University of Wisconsin
The department invites high-quality papers on the design, control, and analysis of operations at healthcare systems. We seek papers on classical operations management issues (such as scheduling, routing, queuing, transportation, patient flow, and quality) as well as non-traditional problems driven by everchanging healthcare practice. Empirical, experimental, and analytical (model based) methodologies are all welcome. Papers may draw theory from across disciplines, and should provide insight into improving operations from the perspective of patients, service providers, organizations (municipal/government/industry), and/or society.
Health Care Management Science Practice
Departmental Editor:
Vikram Tiwari, Vanderbilt University Medical Center
The department seeks research from academicians and practitioners that highlights Management Science based solutions directly relevant to the practice of healthcare. Relevance is judged by the impact on practice, as well as the degree to which researchers engaged with practitioners in understanding the problem context and in developing the solution. Validity, that is, the extent to which the results presented do or would apply in practice is a key evaluation criterion. In addition to meeting the journal’s standards of originality and substantial contribution to knowledge creation, research that can be replicated in other organizations is encouraged. Papers describing unsuccessful applied research projects may be considered if there are generalizable learning points addressing why the project was unsuccessful.
Health Care Productivity Analysis
Departmental Editor:
Jonas Schreyögg, University of Hamburg
The department invites papers with rigorous methods and significant impact for policy and practice. Papers typically apply theory and techniques to measuring productivity in health care organizations and systems. The journal welcomes state-of-the-art parametric as well as non-parametric techniques such as data envelopment analysis, stochastic frontier analysis or partial frontier analysis. The contribution of papers can be manifold including new methodology, novel combination of existing methods or application of existing methods to new contexts. Empirical papers should produce results generalizable beyond a selected set of health care organizations. All papers should include a section on implications for management or policy to enhance productivity.
Public Health Policy and Medical Decision Making
Departmental Editors:
Ebru Bish, University of Alabama
Julie L. Higle, University of Southern California
The department invites high quality papers that use data-driven methods to address important problems that arise in public health policy and medical decision-making domains. We welcome submissions that develop and apply mathematical and computational models in support of data-driven and model-based analyses for these problems.
The Public Health Policy and Medical Decision-Making Department is particularly interested in papers that:
Study high-impact problems involving health policy, treatment planning and design, and clinical applications;
Develop original data-driven models, including those that integrate disease modeling with screening and/or treatment guidelines;
Use model-based analyses as decision making-tools to identify optimal solutions, insights, recommendations.
Articles must clearly articulate the relevance of the work to decision and/or policy makers and the potential impact on patients and/or society. Papers will include articulated contributions within the methodological domain, which may include modeling, analytical, or computational methodologies.
Emerging Topics
Departmental Editor:
Alec Morton, University of Strathclyde
Emerging Topics will handle papers which use innovative quantitative methods to shed light on frontier issues in healthcare management and policy. Such papers may deal with analytic challenges arising from novel health technologies or new organizational forms. Papers falling under this department may also deal with the analysis of new forms of data which are increasingly captured as health systems become more and more digitized.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
