Emergency Department Online Patient-Caregiver Scheduling

Rosemarin, Hanan; Rosenfeld, Ariel; Kraus, Sarit

doi:10.1609/aaai.v33i01.330110013

Cited by 5 publications

(7 citation statements)

References 5 publications

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“…Acknowledgments: This article extends a previous report from the Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19) [62] in two major aspects: First, our previous report focused entirely on the patient-physician assignment problem in EDs. As such, this article generalizes our approach and further investigates the challenging setting of medical imaging which strengthens the benefits and applicability of our approach.…”

Section: Conflicts Of Interestmentioning

confidence: 74%

LBA: Online Learning-Based Assignment of Patients to Medical Professionals

Rosemarin

Rosenfeld

Lapp

et al. 2021

Sensors

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Central to any medical domain is the challenging patient to medical professional assignment task, aimed at getting the right patient to the right medical professional at the right time. This task is highly complex and involves partially conflicting objectives such as minimizing patient wait-time while providing maximal level of care. To tackle this challenge, medical institutions apply common scheduling heuristics to guide their decisions. These generic heuristics often do not align with the expectations of each specific medical institution. In this article, we propose a novel learning-based online optimization approach we term Learning-Based Assignment (LBA), which provides decision makers with a tailored, data-centered decision support algorithm that facilitates dynamic, institution-specific multi-variate decisions, without altering existing medical workflows. We adapt our generic approach to two medical settings: (1) the assignment of patients to caregivers in an emergency department; and (2) the assignment of medical scans to radiologists. In an extensive empirical evaluation, using real-world data and medical experts’ input from two distinctive medical domains, we show that our proposed approach provides a dynamic, robust and configurable data-driven solution which can significantly improve upon existing medical practices.

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Section: Conflicts Of Interestmentioning

confidence: 74%

LBA: Online Learning-Based Assignment of Patients to Medical Professionals

Rosemarin

Rosenfeld

Lapp

et al. 2021

Sensors

Self Cite

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“…Rosemarin et al ( 2019 ) define the ED scheduling problem as needing to satisfy the following constraints: the schedule must minimise the risk of adverse consequences, minimise patient waiting time, minimise patient length-of-stay, minimise ED crowding and minimise interruption to caregivers. They use a mixture of health record data of the patients and data on the status the ED to reconstruct the state of the ED when the patients were there.…”

Section: Machine Learning For Patient Admissionsmentioning

confidence: 99%

Machine learning in patient flow: a review

et al. 2021

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This work is a review of the ways in which machine learning has been used in order to plan, improve or aid the problem of moving patients through healthcare services. We decompose the patient flow problem into four subcategories: prediction of demand on a healthcare institution, prediction of the demand and resource required to transfer patients from the emergency department to the hospital, prediction of potential resource required for the treatment and movement of inpatients and prediction of length-of-stay and discharge timing. We argue that there are benefits to both approaches of considering the healthcare institution as a whole as well as the patient by patient case and that ideally a combination of these would be best for improving patient flow through hospitals. We also argue that it is essential for there to be a shared dataset that will allow researchers to benchmark their algorithms on and thereby allow future researchers to build on that which has already been done. We conclude that machine learning for the improvement of patient flow is still a young field with very few papers tailor-making machine learning methods for the problem being considered. Future works should consider the need to transfer algorithms trained on a dataset to multiple hospitals and allowing for dynamic algorithms which will allow real-time decision-making to help clinical staff on the shop floor.

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“…Collection of all vector is done in a single matrix is also done here. At the same time, some talent researchers design a neural network-based scheduling optimization plan when studying the optimization strategy for the hospital emergency department [6]. The program's primary purpose is to automatically allocate and dispatch medical staff to shorten the gap in patient care and improve the emergency room's performance indicators without changing the existing emergency room arrangements.…”

Section: Steps Of Mcnnmentioning

confidence: 99%

“…The program's primary purpose is to automatically allocate and dispatch medical staff to shorten the gap in patient care and improve the emergency room's performance indicators without changing the existing emergency room arrangements. In addition, referring to the research on neural network sorting methods by Rigutini et al [7], these researchers finally propose a new type of neural network shown in Figure 4 [6] to realize the forecast. D-specific objective function g constraints: Eq.…”

Section: Steps Of Mcnnmentioning

confidence: 99%

See 1 more Smart Citation

Dynamic Task Scheduling using Trained Neural Network and Genetic Algorithm

Kumari¹,

Yadav²,

Agrawal³

2019

International Journal of Advanced Research in Computer and Comm

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This paper focus on allocation of cloud resources where two models were developed for this work. First was TLBO (Teacher Learning Based Optimization) genetic algorithms which find the correct position for the process to execute. Here some information used for analysis are total number of machines, memory, execution time, etc. So, outcome of the selected training process sequence were used as training input to the Convolutional Neural Network for learning. Here training was done in such a way that all set of features were utilized in pair with their process requirement and current position. For increasing the reliability of the work whole experiment was done on real dataset. Result shows that proposed GNNLB model has overcome various evaluation parameters on different scale as compared to previous approaches adopt by researchers.

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Emergency Department Online Patient-Caregiver Scheduling

Cited by 5 publications

References 5 publications

LBA: Online Learning-Based Assignment of Patients to Medical Professionals

LBA: Online Learning-Based Assignment of Patients to Medical Professionals

Machine learning in patient flow: a review

Dynamic Task Scheduling using Trained Neural Network and Genetic Algorithm

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