Title page for etd-0716118-201801


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URN etd-0716118-201801
Author Ya-Jie Huang
Author's Email Address No Public.
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Department Information Management
Year 2017
Semester 2
Degree Master
Type of Document
Language English
Title Detection of Drug-Disease Interactions for Acute Kidney Injury using Deep Rule Forests
Date of Defense 2018-07-20
Page Count 35
Keyword
  • Deep rule forests
  • Random forest
  • Drug-drug interactions
  • Acute kidney injury
  • Machine learning
  • Abstract Patients with kidney diseases are often diagnosed with Acute Kidney Injury (AKI). The mortality rate of critically ill patients with AKI is 60%. As a result, if AKI is diagnosed earlier, patients may have greater chances to recover renal function, which will ultimately improve the patients’ survival rate. The risk factors to AKI include drug-drug interactions and drug-disease interactions. According to previous researches, researchers used statistical analysis to measure the correlations between one disease and one drug. However, realistically, the correlations can be various when the patients usually have many prescriptions and complications. In this thesis, we propose a machine learning algorithm, Deep Rule Forests (DRF), which helps discover and extract rules from tree models as the combinations of drug and diseases usages to help identify aforementioned interactions. We also found that several drug and diseases usages that may be considered having significant impact on (re)occurrence of AKI. After that, the results show that DRF model performs better than typical tree-based and linear method in terms of the prediction accuracy. Moreover, we can obtain a series of situations that may cause AKI. If the layer of DRF model is higher, the extracted rules are more precise.
    Advisory Committee
  • Keng-Pei Lin - chair
  • Pei-Ju Li - co-chair
  • Yihuang Kang - advisor
  • Files
  • etd-0716118-201801.pdf
  • indicate access worldwide
    Date of Submission 2018-08-16

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