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

SIMPLE SUMMARY: This study used a technique called sequential pattern mining to uncover connections between common internal medicine diseases in dogs. The goal was to understand how these diseases relate to each other over time. Researchers collected medical records from dogs treated at the Konkuk University Veterinary Medicine Teaching Hospital, focusing on their diseases and the time intervals between diagnoses. They also calculated the 3-year risk of developing another disease after the initial diagnosis. This study identified 547 dogs with at least one internal medicine disease. The sequential pattern mining analysis revealed strong associations and time intervals for five of the most common diseases in dogs, including hyperadrenocorticism, myxomatous mitral valve disease, canine atopic dermatitis, chronic kidney disease, and chronic pancreatitis. This research suggests that sequential pattern mining is a useful tool for understanding disease connections and predicting future health issues in dogs. Veterinarians can use these findings to recommend preventive measures and treatments for dogs at risk of developing additional medical conditions, ultimately improving the care and health of canine patients. ABSTRACT: Sequential pattern mining (SPM) is a data mining technique used for identifying common association rules in multiple sequential datasets and patterns in ordered events. In this study, we aimed to identify the relationships between commonly occurring internal medicine diseases in canine patients. We obtained medical records of dogs referred to the Konkuk University Veterinary Medicine Teaching Hospital. The data used for SPM included comorbidities and intervals between the diagnoses of internal medicine diseases. Additionally, we estimated the 3-year risk of developing an additional disease after the initial diagnosis of a commonly occurring veterinary internal medicine disease using logistic regression. We identified 547 canine patients diagnosed with ≥ 1 internal medicine disease. The SPM-based analysis assessed comorbidities and intervals for each of the five most common internal medical diseases, including hyperadrenocorticism, myxomatous mitral valve disease, canine atopic dermatitis, chronic kidney disease, and chronic pancreatitis. The highest values of the association rule were 3.01%, 6.02%, 3.9%, 4.1%, and 4.84%, and the shortest intervals were 1.64, 13.14, 5.37, 17.02, and 1.7 days, respectively. This study proposes that SPM is an effective technique for identifying common associations and temporal relationships between internal medicine diseases, and can be used to assess the probability of additional admission due to the development of the subsequent disease that may be diagnosed in canine patients. The results of this study will help veterinarians suggest appropriate preventive measures or other medical treatments for canine patients with medical conditions that have not yet been diagnosed, but are likely to develop in the short term.