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You searched for +publisher:"University of Texas – Austin" +contributor:("Sun, Jimeng"). One record found.

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University of Texas – Austin

1. -3478-7289. Clinically interpretable models for healthcare data.

Degree: Electrical and Computer Engineering, 2015, University of Texas – Austin

The increasing availability of electronic health records (EHRs) has spurred the adoption of data-driven approaches to provide additional insights for diagnoses, prognoses, and cost-effective patient treatment and management. The records are composed of a diverse array of data that includes both structured information (e.g., diagnoses, medications, and lab results) and unstructured clinical narratives notes (e.g., physician's observations, progress notes, etc). Thus, EHRs are a rich source of patient information. However, there are several formidable challenges with using EHRs that have limited their utility for clinical research so far. Problems include data quality; high-dimensional heterogenous information from various sources; privacy; and interoperability across institutions. Further hampering the acceptance of data-driven models is the lack of interpretability of their results. Physicians are accustomed to reasoning based on concise clinical concepts (or phenotypes) rather than directly on high-dimensional EHR data. Unfortunately, these records do not readily map to simple phenotypes, let alone more sophisticated and multifaceted ones. This dissertation investigates the development of clinically interpretable models for EHR data using dimensionality reduction techniques. We posit that clinical concepts are representations in lower dimensional latent spaces. Yet, standard dimensionality reduction techniques alone are insufficient to derive concise and relevant medical concepts from EHR data. We explore two approaches: (1) state space models to dynamically track a patient's cardiac arrest risk, and (2) non – negative matrix and tensor factorization models to generate concise and clinically relevant phenotypes. Our approaches yield clinically interpretable models with minimal human intervention and provides a powerful, and data-driven framework for transforming high-dimensional EHR data into medical concepts. Advisors/Committee Members: Ghosh, Joydeep (advisor), Vishwanath, Sriram (advisor), Vikalo, Haris (committee member), Sanghavi, Sujay (committee member), Sun, Jimeng (committee member).

Subjects/Keywords: Data mining; Healthcare data

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APA (6th Edition):

-3478-7289. (2015). Clinically interpretable models for healthcare data. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/33383

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

-3478-7289. “Clinically interpretable models for healthcare data.” 2015. Thesis, University of Texas – Austin. Accessed May 22, 2019. http://hdl.handle.net/2152/33383.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

-3478-7289. “Clinically interpretable models for healthcare data.” 2015. Web. 22 May 2019.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Vancouver:

-3478-7289. Clinically interpretable models for healthcare data. [Internet] [Thesis]. University of Texas – Austin; 2015. [cited 2019 May 22]. Available from: http://hdl.handle.net/2152/33383.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

-3478-7289. Clinically interpretable models for healthcare data. [Thesis]. University of Texas – Austin; 2015. Available from: http://hdl.handle.net/2152/33383

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Not specified: Masters Thesis or Doctoral Dissertation

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