Indiana University-Purdue University Indianapolis (IUPUI) / The aim of this research is to design a new method to identify the opioid use
problems (OUP) among long-term opioid therapy patients in Indiana University
Health using text mining and machine learning approaches. First, a systematic review
was conducted to investigate the current variables, methods, and opioid problem
definitions used in the literature. We identified 75 distinct variables in 9 models that
majorly used ICD codes to identify the opioid problem (OUP). The review concluded
that using ICD codes alone may not be enough to determine the real size of the opioid
problem and more effort is needed to adopt other methods to understand the issue.
Next, we developed a text mining approach to identify OUP and compared the results
with the current conventional method of identifying OUP using ICD-9 codes.
Following the institutional review board and an approval from the Regenstrief
Institute, structured and unstructured data of 14,298 IUH patients were collected
from the Indiana Network for Patient Care. Our text mining approach identified 127
opioid cases compared to 45 cases identified by ICD codes. We concluded that the text
mining approach may be used successfully to identify OUP from patients clinical
notes. Moreover, we developed a machine learning approach to identify OUP by
analyzing patients’ clinical notes. Our model was able to classify positive OUP from
clinical notes with a sensitivity of 88% on unseen data. We concluded that the
machine learning approach may be used successfully to identify the opioid use
problem from patients’ clinical notes. / 2019-06-21
| Identifer | oai:union.ndltd.org:IUPUI/oai:scholarworks.iupui.edu:1805/18079 |
| Date | 12 1900 |
| Creators | Alzeer, Abdullah Hamad |
| Contributors | Jones, Josette, Dixon, Brian, Bair, Matthew, Liu, Xiaowen |
| Source Sets | Indiana University-Purdue University Indianapolis |
| Language | en_US |
| Detected Language | English |
| Type | Dissertation |
Page generated in 0.0023 seconds