Electronic health records (EHRs) are rich data sources that can be analyzed to discover new, clinically relevant patterns of disease manifestations. However, sparsity, irregularity, and asynchrony in health records pose challenges for their use in such discovery tasks, as standard statistical and machine learning techniques possess limited ability to handle these complications. Abstracting the clinical data into models and then using elements of those models as input to statistical and machine learning algorithms is one approach to overcoming these challenges. This dissertation provides insight into the use of different models for this purpose.
First, I examine the effect of model complexity on algorithm performance. Specifically, I examine how well different models capture the low-specificity information distributed throughout electronic health data. For several predictive algorithms, low-complexity models turn out to be nearly as powerful and much less costly as high-complexity models.
I then explore the use of continuous longitudinal models of laboratory results and diagnosis billing codes to discover clinically relevant patterns between and among these data. I look for associations between clusters of specific laboratory values and single billing codes, and identify known associations as well as others that are consistent with current medical knowledge but not expected a priori.
Finally, I use the same longitudinal abstraction models as inputs into more complex probabilistic models that adjust for indirect associations, and find that diagnosis codes can be used to predict the laboratory status of a patient.
| Identifer | oai:union.ndltd.org:VANDERBILT/oai:VANDERBILTETD:etd-07252016-100314 |
| Date | 29 July 2016 |
| Creators | VanHouten, Jacob Paul |
| Contributors | Katherine E. Hartmann, Nancy M. Lorenzi, Michael E. Matheny, Thomas A. Lasko, Christopher J. Fonnesbeck |
| Publisher | VANDERBILT |
| Source Sets | Vanderbilt University Theses |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | http://etd.library.vanderbilt.edu/available/etd-07252016-100314/ |
| Rights | restricted, I hereby certify that, if appropriate, I have obtained and attached hereto a written permission statement from the owner(s) of each third party copyrighted matter to be included in my thesis, dissertation, or project report, allowing distribution as specified below. I certify that the version I submitted is the same as that approved by my advisory committee. I hereby grant to Vanderbilt University or its agents the non-exclusive license to archive and make accessible, under the conditions specified below, my thesis, dissertation, or project report in whole or in part in all forms of media, now or hereafter known. I retain all other ownership rights to the copyright of the thesis, dissertation or project report. I also retain the right to use in future works (such as articles or books) all or part of this thesis, dissertation, or project report. |
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