Indiana University-Purdue University Indianapolis (IUPUI) / Psychiatric patients require continuous monitoring on par with their severity status.
Unfortunately, current assessment instruments are often time-consuming. The present thesis introduces several passive digital markers (PDMs) that can help reduce this burden
by automating the assessment using medical notes. The methodology leverages medical
notes already annotated according to the General Assessment of Functioning (GAF) scale
to develop a disease severity PDM for schizophrenia, bipolar type I or mixed bipolar and
non-psychotic patients. Topic words that are representative of three disease severity levels
(severe impairment, serious impairment, moderate to no impairment) are identified and the
top 50 words from each severity level are used to summarize the raw text of the medical
notes. The summary of the text is processed by a classifier that generates a disease severity
level. Two classifiers are considered: BERT PDM and Clinical BERT PDM. The evaluation of these classifiers showed that the BERT PDM delivered the best performance. The
PDMs developed using the BERT PDM can assign medical notes from each encounter to a
severe impairment level with a positive predictive value higher than 0.84. These PDMs are
generalizable and their development was facilitated by the availability of a substantial number of medical notes from multiple institutions that were annotated by several health care
providers. The methodology introduced in the present thesis can support the automated
monitoring of the progression of the disease severity for psychiatric patients by digitally
processing the medical note produced at each encounter without additional burden on the
health care system. Applying the same methodology to other diseases is possible subject to
availability of the necessary data.
| Identifer | oai:union.ndltd.org:IUPUI/oai:scholarworks.iupui.edu:1805/31152 |
| Date | 12 1900 |
| Creators | Wang, Shuo |
| Contributors | Miled, Zina Ben, King, Brain, Lee, John |
| Source Sets | Indiana University-Purdue University Indianapolis |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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