Browsing by Subject "topic modeling"

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    Automated Assessment of Psychiatric Patients Using Medical Notes
    (2022-12) Wang, Shuo; Miled, Zina Ben; King, Brain; Lee, John
    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.
  • Thumbnail Image
    Item
    Data-To-Question Generation Using Deep Learning
    (IEEE, 2023) Koshy, Nicole; Dixit, Anshuman; Jadhav, Siddhi Shrikant; Penmatsa, Arun V.; Samanthapudi, Sagar V.; Kumar, Mothi Gowtham Asok; Anuyah, Sydney Oghenetega; Vemula, Gourav; Herzog, Patricia Snell; Bolchini, Davide
    Many publicly available datasets exist that can provide factual answers to a wide range of questions that benefit the public. Indeed, datasets created by governmental and non- governmental organizations often have a mandate to share data with the public. However, these datasets are often underutilized by knowledge workers due to the cumbersome amount of expertise and embedded implicit information needed for everyday users to access, analyze, and utilize their information. To seek solutions to this problem, this paper discusses the design of an automated process for generating questions that provide insight into a dataset. Given a relational dataset, our prototype system architecture follows a five-step process from data extraction, cleaning, pre-processing, entity recognition using deep learning, and questions formulation. Through examples of our results, we show that the questions generated by our approach are similar and, in some cases, more accurate than the ones generated by an AI engine like ChatGPT, whose question outputs while more fluent, are often not true to the facts represented in the original data. We discuss key limitations of our approach and the work to be done to bring to life a fully generalized pipeline that can take any data set and automatically provide the user with factual questions that the data can answer.