Abu Saleh Mohammad MosaDate:
2206A Student Center
Decision Tree Induction for the Screening of Patients at Risk of Moderately Emetogenic Chemotherapy-Induced Nausea and Vomiting During Delayed Phase
Chemotherapy-Induced Nausea and Vomiting (CINV) are the most feared and common side-effects of chemotherapy for cancer patients. The main care plan for CINV consists of preventative care using antiemetics before chemotherapy. CINV considerably impairs the life quality of cancer patients and increases the healthcare cost due to extended hospitalization or re-hospitalization. Thus, it is imperative to identify the patients at high-risk of CINV and provide sufficient antiemetic prophylaxis before chemotherapy. Several recent studies demonstrated that patient-related factors also significantly affect the risk of CINV but how those factors altogether affect the risk of CINV is an unknown fact. This is more applicable for the moderately emetogenic chemotherapy (MEC) risk-group since it has been classified as a broader risk-group (i.e., chances of emetogenecity ranges from 30% to 90%). In this study, we built a decision tree model to classify the patients at high-risk vs. low-risk for the MEC during delayed phase. This tree model can be implemented as a standalone-software or integrated with a clinical decision support system.
2206A Student Center
The MDID project: A study of dermatological image archive, search and use behaviors
A novel, web-based dermatology image management application (MDID) has been developed to facilitate the practices in the Department of Dermatology at the University of Missouri School of Medicine. Since its launch in early January, there are over 1700 images of over 500 patients have been uploaded to the MDID. What are the conventional practices? Are there any differences of preference and activities among professional groups? Are there any changes in image use after adoption of MDID? To answer these questions, we are conducting a mix-method research including surveys, field observations, interviews, and user activity log analysis. A pre-launch survey has been completed with a response rate of 82%. Field observation, user interviews and feedback surveys are currently in progress. Another assessment tool is to analyze user activity logs which are being populated every day. The log data provide us rich information about database use behaviors and preferences without interruption of user’s normal use of MDID. From the preliminary analysis, we discovered noticeable differences of site access and functionality usage among user groups. With a progressing adoption of the MDID into clinical activities, we are expecting to see a potential culture change in the context of medical image management in clinics, research and education.
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