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Balancing continuity of care and home care schedule costs using blueprint routes 利用蓝图路线平衡持续护理和家庭护理计划成本
IF 1.5 Q3 HEALTH CARE SCIENCES & SERVICES Pub Date : 2024-09-01 DOI: 10.1016/j.orhc.2024.100441

In a home care setting, high-quality care is typically associated with continuity of care. In addition, the increasing pressure due to labor shortages calls for cost-efficient operations. This paper focuses on obtaining cost-efficient daily schedules over a longer time horizon, with balanced shift lengths, while ensuring continuity of care (using the continuity of care index). To address this challenge, we propose a novel method based on blueprint routes. This method generates daily schedules by constructing optimized shifts and routes with regard to travel time, (time window) waiting time, and shift costs based on hourly wages. To ensure continuity of care, the daily scheduling decisions are strategically guided using the concept named blueprint routes. The blueprint routes are pre-optimized (partial) routes that help to align the daily schedules to achieve continuity of care in the subsequent nurse-to-shift assignment. Model-based evolutionary algorithms are employed to overcome the NP-hardness of the routing problem and nurse-to-shift assignment. Real-life-based numerical experiments demonstrate that continuity of care does not have to compromise home care schedule costs significantly.

在家庭护理环境中,高质量的护理通常与护理的连续性有关。此外,由于劳动力短缺造成的压力越来越大,因此需要具有成本效益的运营。本文的重点是在较长的时间跨度内,通过平衡轮班长度,获得具有成本效益的每日计划,同时确保护理的连续性(使用护理连续性指数)。为应对这一挑战,我们提出了一种基于蓝图路线的新方法。这种方法通过构建优化的班次和路线来生成每日班次表,同时考虑到旅行时间、(时间窗)等待时间和基于小时工资的班次成本。为确保护理工作的连续性,每天的排班决策都以名为 "蓝图路线 "的概念为战略指导。蓝图路线是预先优化的(部分)路线,有助于调整每日排班,从而在后续的护士轮班分配中实现护理的连续性。我们采用基于模型的进化算法来克服路由问题和护士轮班分配的 NP 难度。基于实际生活的数值实验证明,护理的连续性并不一定要大幅降低家庭护理计划的成本。
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引用次数: 0
Preference-based allocation of patients to nursing homes 根据偏好将病人分配到疗养院
IF 1.5 Q3 HEALTH CARE SCIENCES & SERVICES Pub Date : 2024-09-01 DOI: 10.1016/j.orhc.2024.100442

In many countries, the rapid aging of the population leads to an additional burden on already stretched long-term care systems. This often manifests itself in excessive waiting times for long-term care centers, and in abandonments (i.e., patients passing away while they are waiting). Interestingly, in practice, long waiting times are not caused by a lack of available total capacity in the system, but by systematic inefficiencies in the allocation of patients, each with their personal preferences and (in)flexibility, to geographically distributed care centers.

Motivated by this, we propose a new and easy-to-implement method for the optimal allocation of patients-in-need to nursing homes, balancing the trade-off between the waiting time performance and the individual patients’ preferences and levels of flexibility. The optimal placement policy found by solving a Markov Decision Process demonstrates that for small instances, the mean optimality gap of the allocation model is equal to 1. 3%. We validate a simulation model for a real-life use case of allocating somatic patients to nursing homes in the Amsterdam area. The results show that if more patient replacements are approved, the allocation model can reduce the abandonment fraction under the current policy from 32.2% to 7.4% and waiting times at the same time. Moreover, with the allocation model individual preferences can be served better, which thus provides a powerful means to face the increasing need for patient-centered and sustainable long-term care solutions.

在许多国家,人口的快速老龄化导致本已捉襟见肘的长期护理系统负担加重。这往往表现为长期护理中心的等候时间过长,以及病人被遗弃(即病人在等候期间去世)。有趣的是,在实践中,漫长的等待时间并不是由于系统中可用的总容量不足造成的,而是由于在将病人(每个人都有其个人偏好和(不)灵活性)分配到地理上分散的护理中心时存在系统性的低效率。受此启发,我们提出了一种新的、易于实施的方法,用于将有需要的病人优化分配到护理院,在等待时间表现与病人个人偏好和灵活性水平之间进行权衡。通过求解马尔可夫决策过程找到的最优安置策略表明,对于小实例,分配模型的平均最优性差距等于 1.3%。我们在阿姆斯特丹地区将躯体病人分配到疗养院的真实案例中验证了仿真模型。结果表明,如果批准更多的病人替换,分配模式就能将现行政策下的放弃率从 32.2% 降至 7.4%,同时减少等待时间。此外,分配模式还能更好地满足个人偏好,从而为满足日益增长的以患者为中心和可持续的长期护理解决方案需求提供了有力手段。
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引用次数: 0
Outpatient appointment systems: A new heuristic with patient classification 门诊预约系统:病人分类的新启发式
IF 1.5 Q3 HEALTH CARE SCIENCES & SERVICES Pub Date : 2024-08-30 DOI: 10.1016/j.orhc.2024.100443

Purpose

This study aims to develop a heuristic for an outpatient appointment system considering patient classification.

Design/methodology/approach

The proposed heuristic was applied in simulations with eighteen scenarios, combining different environmental factors. Total cost was adopted as a performance metric, composed of the patient's wait time and the service provider's idleness and overtime. The patients were divided into two classes according to their no-show probability, in an arrivals sequence with a binomial distribution. As a significance test of the results, Bonferroni-adjusted repeated measures analysis was applied.

Findings

Having Dome rule as baseline, an increase in performance in terms of total cost (TC) was observed, which varied between 0.46 % and 5.94 % among the means of the simulated environments, validated using the proposed significance test. The greatest benefits were obtained in the scenarios with lower ratios between service provider costs and patient costs (CR), as well as lower coefficients of variation for service times (Cv). It was also found that the heuristic is more efficient when patients from the class with the highest no-show rate predominate in the session.

Originality

The single study identified in the literature that contemplates recalculations adopts deterministic service times to make its model viable. The present research, in turn, makes more realistic assumptions for the simulated environments, considering the variables and probability distributions most commonly observed in practical contexts

Practical implications

The proposed heuristic provided a significant increase in performance for some combinations of environmental factors analyzed, preserving flexibility in the choice of appointment slots and covering a wide range of healthcare services found in practice.

设计/方法/途径将所提出的启发式应用于结合不同环境因素的 18 种情景模拟中。总成本作为性能指标,由病人的等待时间和服务提供者的闲置时间及加班时间组成。在二项分布的到达序列中,病人根据其不出现的概率被分为两类。结果以 Dome 规则为基线,观察到总成本(TC)方面的性能有所提高,模拟环境的平均值在 0.46 % 和 5.94 % 之间变化,并使用建议的显著性检验进行了验证。在服务提供商成本与患者成本(CR)比率较低以及服务时间变异系数(Cv)较低的情况下,收益最大。研究还发现,当缺席率最高的班级的病人在疗程中占多数时,启发式方法的效率更高。 原创性在文献中发现的唯一一项考虑重新计算的研究采用了确定性服务时间,以使其模型可行。而本研究则对模拟环境做出了更切合实际的假设,考虑到了实际环境中最常见的变量和概率分布。
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引用次数: 0
A modeling framework for evaluating proactive and reactive nurse rostering strategies — A case study from a Neonatal Intensive Care Unit 评估主动和被动护士名册编制策略的建模框架--来自新生儿重症监护室的案例研究
IF 2.1 Q1 Health Professions Pub Date : 2024-06-03 DOI: 10.1016/j.orhc.2024.100432
Kjartan Kastet Klyve, Isabel Nordli Løyning, Line Maria Haugen Melby, Henrik Andersson, Anders Nordby Gullhav

We develop a modeling framework for rostering, absence and demand uncertainty realization, and rerostering to perform detailed quantitative analyses of the robustness of nurse rosters. The framework reflects a real-life problem observed at the Department of Neonatal Intensive Care (DNIC) at St. Olavs Hospital in Trondheim, Norway, but is general and has a high transfer value with respect to using it to analyze roster robustness at other departments. We present multiple proactive strategies to enhance the stability of a roster and a reactive rerostering problem used to improve the flexibility. An extensive case study is performed using historical data from the department. The results show that there is a great potential to improve the stability and flexibility of the rosters using the best combination of strategies. We show that allowing nurses to trade extra weekend work for extra days off, assign surplus work hours evenly over all work shifts, and consider the absence profile of nurses when making the rosters are key strategies to create robust rosters.

我们开发了一个用于名册编制、缺勤和需求不确定性实现以及重新名册编制的建模框架,以便对护士名册的稳健性进行详细的定量分析。该框架反映了在挪威特隆赫姆圣奥拉夫斯医院新生儿重症监护部(DNIC)观察到的一个实际问题,但具有通用性和较高的移植价值,可用于分析其他部门名册的稳健性。我们介绍了增强名册稳定性的多种主动策略,以及用于提高灵活性的被动重定向问题。我们利用该部门的历史数据进行了广泛的案例研究。研究结果表明,采用最佳策略组合提高花名册的稳定性和灵活性大有可为。我们发现,允许护士以额外的周末工作换取额外的休息日、将多余的工作时间平均分配给所有班次以及在制定轮值表时考虑护士的缺勤情况是创建稳健轮值表的关键策略。
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引用次数: 0
Joint determination of nurse and patient bed positions in an inpatient unit considering equity in visibility 考虑到能见度的公平性,联合确定住院部的护士和病人床位
IF 2.1 Q1 Health Professions Pub Date : 2024-06-01 DOI: 10.1016/j.orhc.2024.100431
Uttam Karki, Pratik J. Parikh

Layout design is considered a crucial aspect of healthcare architecture and its goal is to allow easy access to essential hospital services and effective patient care. Literature suggests that modifying or redesigning the inpatient unit layout is one of the ways to maximize patient visibility in an inpatient layout. However, prior work has been descriptive in nature and limited in their ability to derive optimal layouts. To fill this gap, we propose a non-linear optimization model that optimizes both equity and effectiveness in visibility by jointly determining the optimal location of two nurses and patient bed positions in multiple rooms. The bi-objective model is then converted into a single objective model utilizing the ε-constrained method, with equity in the objective function and effectiveness as a constraint. Patient visibility is estimated using a ray-casting algorithm that also considers nurses’ line of sight, door positions, and obstruction levels. A progressive refinement algorithm embedded in the Particle Swarm Optimization framework is proposed to efficiently solve this model. Our results suggest that optimizing bed position in conjunction with nurse position can enhance equity by over 45.2% compared to just optimizing the nurse position. Similarly, angular layouts are superior to linear layout by up to 53% in patient equity. We also notice that increasing spatial distance between nurses in angular layouts can further increase equity. Our approach provides valuable insights and can serve as a benchmark tool for hospitals looking to improve the design of their inpatient units that promote patient safety and high-quality care.

布局设计被认为是医疗建筑的一个重要方面,其目标是方便患者获得医院的基本服务和有效的病人护理。文献表明,修改或重新设计住院部布局是在住院布局中最大限度提高病人能见度的方法之一。然而,以往的研究都是描述性的,在推导最佳布局方面能力有限。为了填补这一空白,我们提出了一个非线性优化模型,通过共同确定多个房间中两个护士和病人床位的最佳位置,来优化能见度的公平性和有效性。然后利用ε约束法将双目标模型转换为单目标模型,目标函数为公平性,约束条件为有效性。病人能见度是通过射线投射算法估算的,该算法还考虑了护士的视线、门的位置和障碍物水平。我们提出了一种嵌入粒子群优化框架的渐进细化算法,以高效解决该模型。我们的研究结果表明,与仅优化护士位置相比,结合优化床位和护士位置可将公平性提高 45.2%。同样,在病人公平性方面,角度布局比线性布局优越多达 53%。我们还注意到,在角度布局中增加护士之间的空间距离可以进一步提高公平性。我们的方法提供了宝贵的见解,可作为医院的基准工具,帮助医院改善住院部的设计,促进患者安全和高质量护理。
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引用次数: 0
Multi-objective mammography unit location–allocation problem: A case study 多目标乳腺 X 射线照相单元位置分配问题:案例研究
IF 2.1 Q1 Health Professions Pub Date : 2024-03-30 DOI: 10.1016/j.orhc.2024.100430
Marcos Vinícius Andrade de Campos , Romário dos Santos Lopes de Assis , Marcone Jamilson Freitas Souza , Eduardo Camargo de Siqueira , Maria Amélia Lopes Silva , Sérgio Ricardo de Souza

This work addresses the Multi-Objective Mammography Unit Location–allocation Problem (MOMULAP), aiming to meet three objectives: maximize mammography screening coverage, minimize the total distance traveled weighted by the number of users, and maximize equity in access to mammography screening. We introduce a mixed-integer nonlinear programming (MINLP) formulation to represent the MOMULAP and algorithms based on the Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Strength Pareto Evolutionary Algorithm (SPEA2) for treating it. The algorithms were tested with data from seven Brazilian states. In these states, the number of cities ranges from 139 to 853, equipment from 23 to 347 units, and estimated annual demand for screenings from 96,592 to 1,739,085. The solutions provided by this work allow health managers to choose the appropriate location and allocation of the mammography units, considering different objectives.

本研究针对多目标乳腺放射摄影单位位置分配问题(MOMULAP),旨在实现三个目标:最大化乳腺放射摄影筛查覆盖率、最小化用户数量加权的总路程,以及最大化乳腺放射摄影筛查的公平性。我们引入了一个混合整数非线性编程(MINLP)公式来表示 MOMULAP,并引入了基于非支配排序遗传算法 II(NSGA-II)和强度帕累托进化算法(SPEA2)的算法来处理该问题。这些算法使用巴西七个州的数据进行了测试。在这些州中,城市数量从 139 个到 853 个不等,设备从 23 台到 347 台不等,估计每年的筛查需求从 96,592 到 1,739,085 不等。这项工作提供的解决方案使卫生管理人员能够在考虑不同目标的情况下,选择合适的地点和乳腺 X 射线照相设备的分配。
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引用次数: 0
Resilience of critical supply chains in pandemics: A model proposal for health personal protective equipment socially optimal distribution 大流行病关键供应链的复原力:个人防护设备社会最佳分配模型建议
IF 2.1 Q1 Health Professions Pub Date : 2024-02-16 DOI: 10.1016/j.orhc.2024.100420
Thaís Campos Lucas, Rafael Duarte Guimarães, Marcela Silva Guimarães Vasconcellos, Isis Didier Lins, Márcio José das Chagas Moura, Paulo Gabriel Santos Campos de Siqueira

The COVID-19 pandemic has tested the resilience of Supply Chains (SCs), which has faced many restrictions and affected their global response. Worldwide stockouts were witnessed due to SCs disruptions, which may endanger lives since some products are critical to responding to this global threat, such as ventilators and Personal Protective Equipment (PPE). Thus, this work aims to help deal with the pandemic impacts on critical SCs, addressing the distribution of materials that are used to cope with the pandemic and considering the resilience of its SCs, dealing with a gap of few studies combining simulation and optimization approaches to tackle this situation. We propose a dynamic framework based on a stochastic population model to address pandemic behavior and an optimization model to support decision-making in a PPE supply chain subject to a pandemic-driven disruption that can be updated anytime necessary. We develop a social objective function that aims to deliver PPE where they are most needed. The proposed approach is illustrated by an example involving real data from a Brazilian company that distributes PPE during the COVID-19 pandemic. We find that profit was inversely correlated with social gain, suggesting that optimizing profits is a poor strategy for addressing public health or social crisis. Still, our model furnishes results with an acceptable profit while prioritizing its effect on coping with the pandemic. As implications, our framework can be applied to support decision makers to improve SCs’ resilience and better allocate resources during disruptive circumstances in which the uncertainty is high, such as future pandemics.

COVID-19 大流行考验了供应链 (SC) 的应变能力,供应链面临许多限制,影响了其全球响应。由于供应链中断,出现了全球范围的缺货现象,这可能会危及生命,因为有些产品对应对这一全球性威胁至关重要,如呼吸机和个人防护设备(PPE)。因此,这项工作旨在帮助应对大流行病对关键 SC 的影响,解决用于应对大流行病的材料的分配问题,并考虑其 SC 的复原力,解决很少有研究结合模拟和优化方法来应对这种情况的空白。我们提出了一个基于随机人口模型的动态框架,以解决大流行行为问题,并提出了一个优化模型,以支持个人防护设备供应链在大流行导致的中断情况下的决策,该模型可随时进行必要的更新。我们开发了一个社会目标函数,旨在向最需要的地方提供个人防护设备。我们以巴西一家在 COVID-19 大流行期间分发个人防护设备的公司的真实数据为例,说明了所提出的方法。我们发现,利润与社会收益成反比,这表明优化利润并不是解决公共卫生或社会危机的良策。尽管如此,我们的模型还是提供了一个可接受的利润结果,同时优先考虑其对应对大流行病的影响。因此,我们的框架可用于支持决策者在不确定性较高的破坏性环境(如未来的大流行病)中提高自然科学部门的复原力并更好地分配资源。
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引用次数: 0
Surgical scheduling to smooth demand for resources 手术调度,以满足资源需求
IF 2.1 Q1 Health Professions Pub Date : 2023-11-21 DOI: 10.1016/j.orhc.2023.100411
Michael W. Carter , Saeedeh Ketabi

With the growing demand for healthcare resources, pressure on efficient usage of available bed capacity is increasing. Peaks in bed demand corresponds to overcrowding in upstream units such as emergency department or operating rooms. With a balanced schedule in elective surgeries integrated into the master surgical schedule, peak traffic can be leveled across the week without changing resource capacity. Hence, overcrowding is reduced without turning away any patients or increasing bed capacity.

This study formulates the integration of master surgical and elective surgery scheduling problems as an Integer Programming model to minimize the fluctuation in the required ward beds for elective inpatients admitted for surgery to the hospital, by changing the day of surgery. This demonstrates the opportunities for smoothing the expected patient demand for beds by adjusting the operating room schedule. This decision is made at the tactical level. The model has been examined using data on the elective patient demand for beds in the hospital during typical weeks driven from Hamilton Health Sciences in Ontario, Canada. The integer programming model has been solved using GAMS/CoinCBC MIP Solver. The model enhances bed management by not only smoothing but also reducing the peak demand. The optimal schedule reduces the peak patient demand for bed by about 3–19% for the test samples. The model can be extended to cover the demand for other resources such as ICU beds.

随着对医疗资源的需求不断增长,有效利用现有病床容量的压力越来越大。床位需求高峰对应于上游单位如急诊科或手术室的过度拥挤。将选择性手术的平衡时间表整合到主手术时间表中,高峰流量可以在不改变资源容量的情况下在一周内保持平衡。因此,在没有拒绝任何病人或增加床位容量的情况下,减少了过度拥挤。本研究将主手术与择期手术的调度问题整合为整数规划模型,通过改变择期手术的日期,使住院择期手术患者所需病床的波动最小化。这证明了通过调整手术室时间表来平滑预期患者对床位的需求的机会。这个决定是在战术层面上做出的。该模型使用了加拿大安大略省汉密尔顿健康科学公司在典型周内对医院病床的选择性患者需求数据进行了检验。利用GAMS/ cobcmip求解器对整数规划模型进行了求解。该模型不仅平滑,而且降低了高峰需求,从而提高了床位管理水平。最优的时间表使患者对测试样本的床位需求高峰减少了约3-19%。该模型可以扩展到其他资源,如ICU床位的需求。
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引用次数: 0
Reorganization of a medical service network to manage pandemic waves: A real case study 重组医疗服务网络以管理流行病浪潮:一个真实案例研究
IF 2.1 Q1 Health Professions Pub Date : 2023-10-05 DOI: 10.1016/j.orhc.2023.100410
Sajjad Ahadian , Mir Saman Pishvaee , Hamed Jahani

During Covid-19, medical service networks (MSNs) faced new challenges, such as an impressive increase in hospital visits, a shortage of hospital beds and staff, and insufficient information to estimate the number of mild and critical cases. In addition, governments were encountered to implement appropriate quarantine policies. Dealing with these problems became more complex and challenging when a new wave of disease occurred. This study develops a mixed-integer linear programming model for reorganizing an MSN to manage future pandemic waves. The model aims at reallocation medical staff to prevent a shortage of hospital beds. A fuzzy approach is employed to estimate the uncertain number of patients in each period. As a result, direct hospital visits are decreased by 60% on average, and shortages of beds are avoided by adding the fewest beds possible in each period. The model can also optimize several performance ratios, e.g., the ratio of hospitalized patients to the specialized personnel assigned to each hospital, which is decreased by approximately 40% in our case.

在2019冠状病毒病期间,医疗服务网络(msn)面临着新的挑战,例如医院就诊人数大幅增加,医院床位和工作人员短缺,以及估计轻危病例数量的信息不足。此外,还要求各国政府执行适当的检疫政策。当新一波疾病发生时,处理这些问题变得更加复杂和具有挑战性。本研究开发了一个混合整数线性规划模型,用于重组MSN以管理未来的流行病浪潮。该模式旨在重新分配医务人员,以防止医院床位短缺。采用模糊方法对每个时间段的不确定患者数量进行估计。因此,直接到医院就诊的人数平均减少了60%,并且通过在每个时期尽可能少地增加床位,避免了床位短缺。该模型还可以优化几个性能比率,例如,住院患者与分配到每家医院的专业人员的比率,在我们的案例中减少了大约40%。
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引用次数: 0
Health outcome predictive modelling in intensive care units 重症监护病房的健康结局预测模型
IF 2.1 Q1 Health Professions Pub Date : 2023-10-04 DOI: 10.1016/j.orhc.2023.100409
Chengqian Xian , Camila P.E. de Souza , Felipe F. Rodrigues

The literature in Intensive Care Units (ICUs) data analysis focuses on predictions of length-of-stay (LOS) and mortality based on patient acuity scores such as Acute Physiology and Chronic Health Evaluation (APACHE), Sequential Organ Failure Assessment (SOFA), to name a few. Unlike ICUs in other areas around the world, ICUs in Ontario, Canada, collect two primary intensive care scoring scales, a therapeutic acuity score called the “Multiple Organs Dysfunctional Score” (MODS) and a nursing workload score called the “Nine Equivalents Nursing Manpower Use Score” (NEMS). The dataset analyzed in this study contains patients’ NEMS and MODS scores measured upon patient admission into the ICU and other characteristics commonly found in the literature. Data were collected between January 1st, 2015 and May 31st, 2021, at two teaching hospital ICUs in Ontario, Canada. In this work, we developed logistic regression, random forests (RF) and neural networks (NN) models for mortality (discharged or deceased) and LOS (short or long stay) predictions. Considering the effect of mortality outcome on LOS, we also combined mortality and LOS to create a new categorical health outcome called LMClass (short stay & discharged, short stay & deceased, or long stay without specifying mortality outcomes), and then applied multinomial regression, RF and NN for its prediction. Among the models evaluated, logistic regression for mortality prediction results in the highest area under the curve (AUC) of 0.795 and also for LMClass prediction the highest accuracy of 0.630. In contrast, in LOS prediction, RF outperforms the other methods with the highest AUC of 0.689. This study also demonstrates that MODS and NEMS, as well as their components measured upon patient arrival, significantly contribute to health outcome prediction in ICUs.

重症监护病房(icu)数据分析的文献侧重于基于患者急性生理和慢性健康评估(APACHE)、顺序器官衰竭评估(SOFA)等患者视力评分来预测住院时间(LOS)和死亡率。与世界其他地区的icu不同,加拿大安大略省的icu收集两种主要的重症监护评分量表,一种是称为“多器官功能障碍评分”(MODS)的治疗灵敏度评分,另一种是称为“九等量护理人力使用评分”(NEMS)的护理工作量评分。本研究分析的数据集包含患者入ICU时的NEMS和MODS评分以及文献中常见的其他特征。数据于2015年1月1日至2021年5月31日在加拿大安大略省的两家教学医院icu收集。在这项工作中,我们开发了逻辑回归、随机森林(RF)和神经网络(NN)模型,用于死亡率(出院或死亡)和LOS(短期或长期住院)预测。考虑到死亡率结局对LOS的影响,我们还将死亡率和LOS结合起来创建了一个新的分类健康结局,称为LMClass(短期停留&出院,短暂停留;死亡,或长期停留,但未指定死亡结果),然后应用多项回归,RF和NN进行预测。在评估的模型中,logistic回归预测死亡率的曲线下面积(AUC)最高,为0.795,LMClass预测准确率最高,为0.630。相比之下,在LOS预测中,RF优于其他方法,AUC最高,为0.689。本研究还表明,MODS和NEMS及其在患者到达时测量的成分对icu的健康结局预测有重要作用。
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