A Novel Pattern-Based Comorbidity Mining Algorithm in Large-Scale Medical Database
Date Issued
2011
Date
2011
Author(s)
Hsu, Yu-Jen
Abstract
In medical study, comorbidities refer to the additional diseases a patient may suffer other than the primary disease of concern. In recent years, many medical studies have concluded that focusing on one single disease is not the most effective strategy for disease treatment and diagnosis of comobidities can provide a more comprehensive picture of the health condition of the patient. Accordingly, identifying possible comorbidities of the primary disease of concern is an issue of great significance and is essential for development of preventive medicine.
Clinical trial and observational study are the two principal methods for analysis of comorbidity. A clinical trial, which is conducted with a well-designed procedure, generally can provide compelling evidences. However, the cost and time involved in conducting a clinical trial may become a major concern for a research team to carry out such a study. Furthermore, a clinical trial typically involves randomly assigning the patients to the treated group or the control group and therefore may lead to an ethical controversy. On the other hand, an observational study is normally conducted with an existing dataset and therefore is substantially less costly and time consuming in comparison with a clinical trial. However, as the dataset employed in an observational study typically has been collected without the involvement of the research teams that use the dataset, the reliability of the results may be questioned, especially when the samples have not been selected with a rigorous procedure. The discussions above show that the clinical trial and the observational study complement each other in terms of their merits and deficiencies. Accordingly, an observational study can be conducted to collect some clues for the design of clinical trial.
This thesis proposes a pattern mining algorithm for identifying the possible comobidities of the primary disease of concern in population-based mediccal databases. This thesis also discusses the effects of applying the proposed algorithm to identify the comobidities of senile dementia. Experimental results show that the comorbidity patterns identified by the proposed pattern mining algorithm provide medical personnel with valuable clues for designing follow-up clinical trials.
Subjects
Comorbidity
Prevention medicine
Clinical trial
Observational study
Pattern mining
Large-scale medical database
Type
thesis
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