Brauner Lea Eileen, Yao Yao, Grigull Lorenz, Klawonn Frank
Department of Computer Science, Ostfalia University of Applied Sciences, 38302 Wolfenbuettel, Germany.
Center for Rare Diseases Bonn (ZSEB), University Hospital of Bonn, 53127 Bonn, Germany.
J Clin Med. 2024 Aug 29;13(17):5132. doi: 10.3390/jcm13175132.
A major challenge faced by patients with rare diseases (RDs) often stems from delays in diagnosis, typically due to nonspecific clinical symptoms or doctors' limited experience in connecting symptoms to the underlying RD. Using patient-oriented questionnaires (POQs) as a data source for machine learning (ML) techniques can serve as a potential solution. These questionnaires enable patients to portray their day-to-day experiences living with their condition, irrespective of clinical symptoms. This systematic review-registered at PROSPERO with the Registration-ID: CRD42023490838-aims to present the current state of research in this domain by conducting a systematic literature search and identifying the potentials and limitations of this methodology. The review adheres to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and was primarily funded by the German Federal Ministry of Education and Research under grant no. 16DHBKI056 (ki4all). The methodology involved a systematic search across the databases PubMed, Semantic Scholar and Google Scholar, covering articles published until June 2023. The inclusion criteria encompass examining the use of POQs in diagnosing rare and common diseases. Additionally, studies that focused on applying ML techniques to the resulting datasets were considered for inclusion. The primary objective was to include English as well as German research that involved the generation of predictions regarding the underlying disease based on the information gathered from POQs. Furthermore, studies exploring identifying predictive indicators associated with the underlying disease were also included in the literature review. The following data were extracted from the selected studies: year of publication, number of questions in the POQs, answer scale in the questionnaires, the ML algorithms used, the input data for the ML algorithms, the performance of these algorithms and how the performance was measured. In addition, information on the development of the questionnaires was recorded. This search retrieved 421 results in total. After one superficial and two comprehensive screening runs performed by two authors independently, we ended up with 26 studies for further consideration. Sixteen of these studies deal with diseases and ML algorithms to analyse data; the other ten studies provide contributing research in this field. We discuss several potentials and limitations of the evaluated approach. Overall, the results show that the full potential has not yet been exploited and that further research in this direction is worthwhile, because the study results show that ML algorithms can achieve promising results on POQ data; however, their use in everyday medical practice has not yet been investigated.
罕见病患者面临的一个主要挑战通常源于诊断延迟，这通常是由于临床症状不具特异性，或医生将症状与潜在罕见病联系起来的经验有限。将以患者为导向的问卷（POQ）用作机器学习（ML）技术的数据源可能是一种潜在的解决方案。这些问卷使患者能够描述他们在患病情况下的日常生活经历，而不论临床症状如何。这项在PROSPERO注册的系统评价（注册号：CRD42023490838）旨在通过进行系统的文献检索，呈现该领域的研究现状，并确定这种方法的潜力和局限性。该评价遵循系统评价和Meta分析的首选报告项目（PRISMA）指南，主要由德国联邦教育与研究部资助，资助编号为16DHBKI056（ki4all）。该方法包括在PubMed、语义学者和谷歌学术等数据库中进行系统搜索，涵盖截至2023年6月发表的文章。纳入标准包括研究POQ在诊断罕见病和常见疾病中的应用。此外，关注将ML技术应用于所得数据集的研究也被考虑纳入。主要目标是纳入涉及基于从POQ收集的信息对潜在疾病进行预测生成的英文和德文研究。此外，探索识别与潜在疾病相关的预测指标的研究也纳入了文献综述。从选定的研究中提取了以下数据：发表年份、POQ中的问题数量、问卷中的答案量表、使用的ML算法、ML算法的输入数据、这些算法的性能以及性能的测量方式。此外，还记录了问卷的编制信息。此次搜索总共检索到421条结果。在两位作者独立进行一次初步筛选和两次全面筛选后，我们最终确定了26项研究以供进一步考虑。其中16项研究涉及疾病和用于分析数据的ML算法；其他10项研究为该领域提供了有价值的研究。我们讨论了所评估方法的几个潜力和局限性。总体而言，结果表明该方法的全部潜力尚未得到充分利用，值得在这个方向上进一步研究，因为研究结果表明ML算法在POQ数据上可以取得有前景的结果；然而，它们在日常医疗实践中的应用尚未得到研究。
