The evaluation and application of multidisciplinary cancer conference system
Date Issued
2016
Date
2016
Author(s)
Chou, Shih-Yu
Abstract
Liver cancer is a disease caused by multi-factors and abnormal cell growth, so its treatment process is very cumbersome and complicated. The previous study indicated that the clinical decision about cancer care is the key determinant of prognostic outcome of patient. Besides, since 21th century, the research over the world suggested that doctors and other medical professional form a multidisciplinary team (MDT) to hold the multidisciplinary case conference (MCC) regularly to review, discuss the cases and establish the appropriate treatment guideline. These processes not only cut the medical cost, but also improve the quality of cancer care. In 2012, Cancer Multidisciplinary Conference System (CMDTC) system was implemented in Taipei Veterans General Hospital to assist in managing the multidisciplinary cancer conference. With this system, it not only save time for medical professionals to hold and manage the system but also the recording and follow-up of patients. In addition, the connection between the system to other clinical system also help the information join and search for clinical purpose. With the merge of data collected from CMDTC system and cancer registry, we can have survival analysis on the all the aspect of patients including diagnosis, treatment and prognosis. On the basis of CMDTC system, this research is divided into two part: First part is using Cox proportional hazard model to evaluate the synergic effect of combination of the system and MCC on improvement of prognosis outcome of patients. And the significant result shows that liver cancer patients with MCC got better prognostic outcome (Hazard ratio 0.48; 95% CI 0.26-0.87). And second part of this article proposed a random forest model with AUC 0.82 and accuracy 0.85. It can give prediction about the potential patients with liver cancer requiring the MCC discussion. Also, the importance of clinical factor was given to standardize the patient selection process before MCC.
Subjects
machine learning
random forest
multidisciplinary cancer care
survival analysis
SDGs
Type
thesis
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