Computer Science and Information Engineering, National Taipei University of Technology, Taipei, 106344, Taiwan.
Clayton Faculty of Information Technology, Monash University, Melbourne, VIC, 3800, Australia.
BMC Cardiovasc Disord. 2023 Aug 10;23(1):394. doi: 10.1186/s12872-023-03393-7.
Myocardial infarction (MI) is one of the significant cardiovascular diseases (CVDs). According to Taiwanese health record analysis, the hazard rate reaches a peak in the initial year after diagnosis of MI, drops to a relatively low value, and maintains stable for the following years. Therefore, identifying suspicious comorbidity patterns of short-term death before the diagnosis may help achieve prolonged survival for MI patients.
Interval sequential pattern mining was applied with odds ratio to the hospitalization records from the Taiwan National Health Insurance Research Database to evaluate the disease progression and identify potential subjects at the earliest possible stage.
Our analysis resulted in five disease pathways, including "diabetes mellitus," "other disorders of the urethra and urinary tract," "essential hypertension," "hypertensive heart disease," and "other forms of chronic ischemic heart disease" that led to short-term death after MI diagnosis, and these pathways covered half of the cohort.
We explored the possibility of establishing trajectory patterns to identify the high-risk population of early mortality after MI.
心肌梗死（MI）是一种严重的心血管疾病（CVD）。根据台湾地区的健康记录分析，MI 诊断后最初的一年，危险率达到峰值，随后降至相对较低的值，并在随后几年保持稳定。因此，在诊断前识别短期死亡的可疑合并症模式可能有助于 MI 患者延长生存时间。
本研究使用间隔顺序模式挖掘和优势比，对来自台湾全民健康保险研究数据库的住院记录进行分析，以评估疾病进展情况并尽早识别潜在的患者。
我们的分析得到了五条疾病路径，包括“糖尿病”、“尿道和尿路其他疾病”、“原发性高血压”、“高血压性心脏病”和“其他形式的慢性缺血性心脏病”，这些疾病路径导致 MI 诊断后短期死亡，这些路径涵盖了队列的一半。
我们探索了建立轨迹模式的可能性，以识别 MI 后早期死亡的高危人群。
