Equity-promoting integer programming approaches for medical resident rotation scheduling [0.03%]
促进公平的医嘱轮转排班整数规划方法研究
Shutian Li,Karmel S Shehadeh,Frank E Curtis et al.
Shutian Li et al.
Motivated by our collaboration with a residency program at an academic health system, we propose new integer programming (IP) approaches for the resident-to-rotation assignment problem (RRAP). Given sets of residents, resident classes, and ...
A decision support tool for the location, districting and dimensioning of Community Health Houses [0.03%]
社区卫生机构的位置、分区和规模的决策支持工具
Martina Doneda,Ettore Lanzarone,Carlotta Franchi et al.
Martina Doneda et al.
Community Health Houses (CHHs) are new entities in the Italian National Health Service that have been envisaged to provide proximity care to an increasingly aging population, and bear some similarities to other facilities in countries that ...
Surgery scheduling problem considering the affinity and preferences in the surgical team [0.03%]
考虑手术团队亲和力和偏好的手术排程问题
Francisco Ríos-Fierro,Guillermo Latorre-Núñez,Carlos Contreras-Bolton
Francisco Ríos-Fierro
Surgery scheduling is crucial in healthcare management, particularly in hospitals and clinics. This study tackles the elective surgery scheduling problem by integrating affinity and preferences among the surgical team's members. Although th...
Diagnosis decoded: a taxonomy and natural language processing analysis of the diagnosis section in German hospital discharge summaries [0.03%]
诊断释义：德国医院出院总结中诊断部分的分类学及自然语言处理分析
Julian Frings,Paul Rust,Felix Jede et al.
Julian Frings et al.
The diagnosis section in hospital discharge summaries plays a critical role in ensuring continuity of care by providing essential diagnostic information and a succinct summary of a patient's condition to subsequent caregivers. However, the ...
Enhancing clinical and non-clinical risk management: A case study using ELECTRE Tri-nC [0.03%]
改进临床和非临床风险管理：使用ELECTRE Tri-nC的案例研究
Joana Lemos Alves,Miguel Alves Pereira
Joana Lemos Alves
Adverse events in healthcare continue to challenge hospital management practices, often resulting in avoidable patient harm and substantial financial costs. Despite technological progress and the availability of risk management tools, healt...
Positive and unlabeled learning from hospital administrative data: a novel approach to identify sepsis cases [0.03%]
基于医院管理数据的积极与未标记学习：识别脓毒症病例的新方法
Justus Vogel,Johannes Cordier
Justus Vogel
In positive and unlabeled (PU) learning problems, only positive examples are labeled. Unlabeled data contain both positive and negative examples. Studies show that positive examples of (secondary) diagnoses, and clinical conditions, such as...
Expanding modeling boundaries to design more resilient vaccine supply networks [0.03%]
拓展建模边界以设计更具韧性的疫苗供应链
Donovan Guttieres,Carla Van Riet,Nico Vandaele et al.
Donovan Guttieres et al.
The COVID-19 pandemic shed light on the fragility of today's public health systems and failure to sufficiently invest in preparedness. These shortcomings are observed in delays achieving timely, equitable, and sufficient access to life-savi...
Multi-objective dynamic prioritized routing and scheduling for home healthcare services with cooperating service providers [0.03%]
考虑合作服务提供商的居家医护服务多目标动态优先级调度算法研究
Mert Parçaoğlu,F Sibel Salman,Ozgur M Araz
Mert Parçaoğlu
In home healthcare service systems, each healthcare service provider (HSP) is assigned a list of patients to be visited at their homes. We focus on generating a daily patient visit plan that selects the patients to be visited according to t...
Can past variants of SARS-CoV-2 predict the impact of future variants? Machine learning for early warning of US counties at risk [0.03%]
过去的新冠病毒变异株能预测未来变异株的影响吗？美国风险县的早期预警机器学习模型
Kevin B Smith,Siqian Shen,Brian T Denton
Kevin B Smith
In this paper, we determine whether machine learning (ML) models created using data from the novel SARS-CoV-2 Alpha variant can prospectively predict county-level incidence of emerging variants, validated using data of the Omicron variant. ...
Causal networks guiding large language models: application to COVID-19 [0.03%]
因果网络引导大型语言模型在COVID-19中的应用
Farrokh Alemi,Kevin James Lybarger,Jee Vang et al.
Farrokh Alemi et al.
In the context of diagnosis of COVID-19, this paper shows how to convert a Causal Network to a Large Language Model (LLM). The Causal Network was converted to the language model using prompts and completions. Prompts were composed from the ...