Department of Clinical Science, Intervention and Technology, Karolinska Institutet, 17177 Stockholm, Sweden; School of Engineering Sciences in Chemistry, Biotechnology and Health, Department of Biomedical Engineering and Health Systems, Division of Ergonomics, KTH Royal Institute of Technology, 14157 Stockholm, Sweden.
Department of Clinical Science, Intervention and Technology, Karolinska Institutet, 17177 Stockholm, Sweden; School of Engineering Sciences in Chemistry, Biotechnology and Health, Department of Biomedical Engineering and Health Systems, Division of Ergonomics, KTH Royal Institute of Technology, 14157 Stockholm, Sweden; Department of Clinical Physiology, Karolinska University Hospital, 17176 Stockholm, Sweden.
Artif Intell Med. 2023 Oct;144:102645. doi: 10.1016/j.artmed.2023.102645. Epub 2023 Aug 29.
The widespread use of information technology in healthcare leads to extensive data collection, which can be utilised to enhance patient care and manage chronic illnesses. Our objective is to summarise previous studies that have used data mining or process mining methods in the context of chronic diseases in order to identify research trends and future opportunities. The review covers articles that pertain to the application of data mining or process mining methods on chronic diseases that were published between 2000 and 2022. Articles were sourced from PubMed, Web of Science, EMBASE, and Google Scholar based on predetermined inclusion and exclusion criteria. A total of 71 articles met the inclusion criteria and were included in the review. Based on the literature review results, we detected a growing trend in the application of data mining methods in diabetes research. Additionally, a distinct increase in the use of process mining methods to model clinical pathways in cancer research was observed. Frequently, this takes the form of a collaborative integration of process mining, data mining, and traditional statistical methods. In light of this collaborative approach, the meticulous selection of statistical methods based on their underlying assumptions is essential when integrating these traditional methods with process mining and data mining methods. Another notable challenge is the lack of standardised guidelines for reporting process mining studies in the medical field. Furthermore, there is a pressing need to enhance the clinical interpretation of data mining and process mining results.
信息技术在医疗保健中的广泛应用导致了大量数据的收集，这些数据可以用于改善患者护理和管理慢性病。我们的目标是总结以前使用数据挖掘或流程挖掘方法在慢性病背景下的研究，以确定研究趋势和未来的机会。本综述涵盖了 2000 年至 2022 年期间发表的关于将数据挖掘或流程挖掘方法应用于慢性病的文章。根据预先确定的纳入和排除标准，从 PubMed、Web of Science、EMBASE 和 Google Scholar 中获取了这些文章。共有 71 篇文章符合纳入标准，并被纳入综述。根据文献综述的结果，我们发现数据挖掘方法在糖尿病研究中的应用呈增长趋势。此外，在癌症研究中使用流程挖掘方法来对临床路径进行建模的情况也明显增加。通常，这是流程挖掘、数据挖掘和传统统计方法的协作集成。鉴于这种协作方法，在将这些传统方法与流程挖掘和数据挖掘方法集成时，根据其基本假设仔细选择统计方法是至关重要的。另一个值得注意的挑战是缺乏医学领域流程挖掘研究报告的标准化指南。此外，迫切需要增强对数据挖掘和流程挖掘结果的临床解释。
