An intelligent decision support system for acute postoperative endophthalmitis: design, development and evaluation of a smartphone application

BMC Medical Informatics and Decision Making

Research

Razieh Farrahi¹  Mahdi Shaeri²  Seyedeh Maryam Hosseini³  Nasser Shoeibi³  Fatemeh Rangraze Jeddi⁴  Azam Salehzadeh⁴  Ehsan Nabovati⁴ 

[1] Department of Health Information Technology, Ferdows Faculty of Medical Sciences, Birjand University of Medical Sciences, Birjand, Iran;Department of Ophthalmology, Kashan University of Medical Sciences, Kashan, Iran;Eye Research Center, Mashhad University of Medical Sciences, Mashhad, Iran;Health Information Management Research Center, School of Allied Health Professions, Kashan University of Medical Sciences, Pezeshk Blvd, 5Th of Qotbe Ravandi Blvd - Pardis Daneshgah, 8715973449, Kashan, Iran;

关键词: Eye disease;    Endophthalmitis;    Clinical decision support;    Mobile application;    Artificial intelligence;    User-centered design;   

DOI  :  10.1186/s12911-023-02214-3

 received in 2023-01-08, accepted in 2023-06-21,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundToday, clinical decision support systems based on artificial intelligence can significantly help physicians in the correct diagnosis and quick rapid treatment of endophthalmitis as the most important cause of blindness in emergency diseases. This study aimed to design, develop, and evaluate an intelligent decision support system for acute postoperative endophthalmitis.MethodsThis study was conducted in 2020–2021 in three phases: analysis, design and development, and evaluation. The user needs and the features of the system were identified through interviews with end users. Data were analyzed using thematic analysis. The list of clinical signs of acute postoperative endophthalmitis was provided to ophthalmologists for prioritization. 4 algorithms support vector machine, decision tree classifier, k-nearest neighbors, and random forest were used in the design of the computing core of the system for disease diagnosis. The acute postoperative endophthalmitis diagnosis application was developed for using by physicians and patients. Based on the data of 60 acute postoperative endophthalmitis patients, 143 acute postoperative endophthalmitis records and 12 non-acute postoperative endophthalmitis records were identified. The learning process of the algorithm was performed on 70% of the data and 30% of the data was used for evaluation.ResultsThe most important features of the application for physicians were selecting clinical signs and symptoms, predicting diagnosis based on artificial intelligence, physician–patient communication, selecting the appropriate treatment, and easy access to scientific resources. The results of the usability evaluation showed that the application was good with a mean (± SD) score of 7.73 ± 0.53 out of 10.ConclusionA decision support system with accuracy, precision, sensitivity and specificity, negative predictive values, F-measure and area under precision-recall curve 100% was created thanks to widespread participation, the use of clinical specialists' experiences and their awareness of patients' needs, as well as the availability of a comprehensive acute postoperative endophthalmitis clinical dataset.

【 授权许可】

CC BY   
© The Author(s) 2023

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