An Efficient Clustering Algorithm for Mixed Dataset of Postoperative Surgical Records

Petwal, Hemant; Rani, Rinkle

doi:10.2991/ijcis.d.200601.001

Cited by 3 publications

(2 citation statements)

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“…Experiments were performed using a real case study of a multispecialty hospital in India. The proposed framework was implemented using a postoperative surgical dataset (POS) [33], which was obtained from the orthopedic surgery department of the hospital. Sub-section 5.1 discusses the performance of the SHM module.…”

Section: Resultsmentioning

confidence: 99%

“…Arranging existing surgical patients in disjoint clusters based on their characteristics can help hospitals to find a suitable subgroup for a newly referred surgical patient. In this study, an efficient data clustering algorithm for mixed datasets [33] is proposed to cluster surgical patients. The proposed clustering algorithm (Algorithm 1) based on the AEFA focuses on finding optimal clusters automatically.…”

Section: Clustering Of the Existing Surgical Patientsmentioning

confidence: 99%

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An Optimized Framework for Surgical Team Selection

Petwal¹,

Rani²

2021

Computers, Materials &Amp; Continua

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In the healthcare system, a surgical team is a unit of experienced personnel who provide medical care to surgical patients during surgery. Selecting a surgical team is challenging for a multispecialty hospital as the performance of its members affects the efficiency and reliability of the hospital's patient care. The effectiveness of a surgical team depends not only on its individual members but also on the coordination among them. In this paper, we addressed the challenges of surgical team selection faced by a multispecialty hospital and proposed a decision-making framework for selecting the optimal list of surgical teams for a given patient. The proposed framework focused on improving the existing surgical history management system by arranging surgery-bound patients into optimal subgroups based on similar characteristics and selecting an optimal list of surgical teams for a new surgical patient based on the patient's subgroups. For this end, two population-based meta-heuristic algorithms for clustering of mixed datasets and multi-objective optimization were proposed. The proposed algorithms were tested using different datasets and benchmark functions. Furthermore, the proposed framework was validated through a case study of a real postoperative surgical dataset obtained from the orthopedic surgery department of a multispecialty hospital in India. The results revealed that the proposed framework was efficient in arranging patients in optimal groups as well as selecting optimal surgical teams for a given patient.

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Section: Resultsmentioning

confidence: 99%

Section: Clustering Of the Existing Surgical Patientsmentioning

confidence: 99%