Progression to myocardial infarction short-term death based on interval sequential pattern mining

Abstract

Background 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. Methods 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. Results 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. Conclusion We explored the possibility of establishing trajectory patterns to identify the high-risk population of early mortality after MI.

Detection of Amyotrophic Lateral Sclerosis (ALS) Comorbidity Trajectories Based on Principal Tree Model Analytics

“…Such methodologies fall short of capturing the dynamic narrative of disease progression and interactions with comorbidities. Furthermore, traditional analysis methods [15][16][17][18][19][20] often interpreted disease trajectories as a chain of disease events, potentially overlooking the simultaneous presence of comorbidities in ALS patients.…”

Applying Sequential Pattern Mining to Investigate the Temporal Relationships between Commonly Occurring Internal Medicine Diseases and Intervals for the Risk of Concurrent Disease in Canine Patients

“…Beyond retail, SPM was applied as a methodology to investigate relationships among medical events, and develop predictive models for extensive healthcare data in human medicine [1,8]. SPM-derived studies applied to medicine have demonstrated promising outcomes, including disease susceptibility prediction [9,10], improved understanding of disease progression patterns [11,12], identification of revisit patterns [13,14], enhanced pharmacovigilance for medication safety [15,16], and the exploration of relationships between medical conditions [17,18]. Despite several limitations, including variations in data quality, privacy concerns, the complexity of developing predictive models, and ethical considerations regarding data usage and transparency, medical research employing SPM has been actively reported to date [19,20].…”