{"title":"Cross-checked screening application for reliable categorisation of familial hypercholesterolaemia: design and development of the prototype","authors":"M. M. Rosli, M. Annamalai, N. A. Mohd Kasim, C. Yung-An, H. Nawawi","doi":"10.11591/ijai.v12.i2.pp704-713","DOIUrl":null,"url":null,"abstract":"The paper describes the development of a computer-based familial hypercholesterolemia (FH) screening application (FH CatScreen©). The application facilitates automatic scoring and categorisation of patients by medical practitioners based on four well-known FH diagnostic criteria. In the absence of a FH diagnostic criterion for Malaysian population, these four diagnostic criteria are commonly used criteria to classify patients FH severity levels to manage early interventions. We applied an adaptive software development approach comprising planning, development and validation phases to develop FH CatScreen©. A user study involving thirty medical practitioners was conducted to evaluate the effectiveness and usability of FH CatScreen©. The study showed that FH CatScreen© was able to provide a more correct, faster and better-informed assessment compared to the traditional paper-based method. The study further showed that FH CatScreen© has a good degree of performance and acceptance by the participants. The participants indicated that the simultaneous use of the four diagnostic criteria in FH CatScreen© has assisted them to compare the outcomes of each of the criterion side-by-side. It allowed them to decide on the severity of patient condition with high confidence. FH CatScreen© has demonstrated its expediency and efficacy in collecting the data on FH incidence and prevalence in Malaysia.","PeriodicalId":52221,"journal":{"name":"IAES International Journal of Artificial Intelligence","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IAES International Journal of Artificial Intelligence","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.11591/ijai.v12.i2.pp704-713","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"Decision Sciences","Score":null,"Total":0}
引用次数: 0
用于家族性高胆固醇血症可靠分类的交叉检查筛查应用:原型的设计和开发
本文介绍了基于计算机的家族性高胆固醇血症(FH)筛查应用程序(FH CatScreen©)的开发。该应用程序便于医生根据四个众所周知的FH诊断标准对患者进行自动评分和分类。在马来西亚人口缺乏FH诊断标准的情况下,这四个诊断标准通常用于对患者FH严重程度进行分类,以便进行早期干预。我们采用自适应软件开发方法,包括计划、开发和验证阶段来开发FH CatScreen©。对30名医生进行了一项用户研究,以评估FH CatScreen的有效性和可用性©。研究表明,与传统的基于纸张的方法相比,FH CatScreen©能够提供更正确、更快速、更明智的评估。研究进一步表明,FH猫屏©具有良好的表现程度和参与者的接受程度。参与者表示,在FH CatScreen©中同时使用四个诊断标准有助于他们并排比较每个标准的结果。它使他们能够高度自信地决定病人病情的严重程度。FH CatScreen©已证明其在收集马来西亚FH发病率和流行率数据方面的便利性和有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。