A machine learning framework for interpretable predictions in patient pathways: The case of predicting ICU admission for patients with symptoms of sepsis.

IF 2.3 3区医学 Q2 HEALTH POLICY & SERVICES

Health Care Management Science Pub Date : 2024-06-01 Epub Date: 2024-05-21 DOI:10.1007/s10729-024-09673-8

Sandra Zilker, Sven Weinzierl, Mathias Kraus, Patrick Zschech, Martin Matzner

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引用次数: 0

Abstract

Proactive analysis of patient pathways helps healthcare providers anticipate treatment-related risks, identify outcomes, and allocate resources. Machine learning (ML) can leverage a patient's complete health history to make informed decisions about future events. However, previous work has mostly relied on so-called black-box models, which are unintelligible to humans, making it difficult for clinicians to apply such models. Our work introduces PatWay-Net, an ML framework designed for interpretable predictions of admission to the intensive care unit (ICU) for patients with symptoms of sepsis. We propose a novel type of recurrent neural network and combine it with multi-layer perceptrons to process the patient pathways and produce predictive yet interpretable results. We demonstrate its utility through a comprehensive dashboard that visualizes patient health trajectories, predictive outcomes, and associated risks. Our evaluation includes both predictive performance - where PatWay-Net outperforms standard models such as decision trees, random forests, and gradient-boosted decision trees - and clinical utility, validated through structured interviews with clinicians. By providing improved predictive accuracy along with interpretable and actionable insights, PatWay-Net serves as a valuable tool for healthcare decision support in the critical case of patients with symptoms of sepsis.

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病人路径中可解释预测的机器学习框架：预测有败血症症状的患者入住重症监护室的案例。

对患者路径的主动分析有助于医疗服务提供者预测治疗相关风险、确定治疗结果并分配资源。机器学习（ML）可以利用患者的完整健康史，对未来事件做出明智的决策。然而，以前的工作大多依赖于所谓的黑盒模型，人类无法理解这些模型，因此临床医生很难应用这些模型。我们的工作引入了 PatWay-Net，这是一个 ML 框架，旨在对有败血症症状的患者入住重症监护室（ICU）进行可解释的预测。我们提出了一种新型的递归神经网络，并将其与多层感知器相结合，以处理病人的路径并产生可解释的预测结果。我们通过一个全面的仪表盘展示了它的实用性，该仪表盘可直观显示患者的健康轨迹、预测结果和相关风险。我们的评估包括预测性能（PatWay-Net 的性能优于决策树、随机森林和梯度提升决策树等标准模型）和临床实用性（通过对临床医生的结构化访谈进行验证）。PatWay-Net 不仅提高了预测的准确性，还提供了可解释和可操作的见解，是对有败血症症状的危重病人提供医疗决策支持的重要工具。

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来源期刊

Health Care Management Science HEALTH POLICY & SERVICES-

CiteScore

7.20

自引率

5.60%

发文量

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.