Using machine learning methods to improve chronic disease case definitions in primary care electronic medical records

Lethebe, Brendan Cord

Using machine learning methods to improve chronic disease case definitions in primary care electronic medical records

dc.contributor.advisor	Williamson, Tyler S.
dc.contributor.advisor	Sajobi, Tolulope T.
dc.contributor.author	Lethebe, Brendan Cord
dc.contributor.committeemember	Quan, Hude
dc.contributor.committeemember	Ronksley, Paul Everett
dc.date	2018-06
dc.date.accessioned	2018-04-25T14:19:24Z
dc.date.available	2018-04-25T14:19:24Z
dc.date.issued	2018-04-23
dc.description.abstract	Background: Chronic disease surveillance at the primary care level is becoming more feasible with the increased use of electronic medical records (EMRs). However, the quality of surveillance information is directly dependent on the quality of the case definitions that identify the conditions of interest. Purpose: To determine whether machine learning algorithms can produce chronic disease case definitions comparable to committee created case definitions in a primary care EMR setting. Methods: A chart review was conducted for the presence of hypertension, diabetes, osteoarthritis, and depression in a cohort of 1920 patients from the Canadian Primary Care Sentinel Surveillance Network database. The results of this chart review were used as training data. The C5.0, Classification and Regression Tree, Chi-Squared Automated Interaction Detection decision trees, Forward Stepwise logistic regression, Least Absolute Shrinkage and Selection Operator penalized logistic regression were compared using 10-fold cross validation. Sensitivity, specificity, positive predictive value and negative predictive value were estimated and compared for the four chronic conditions of interest. Results: Validity measures were similar across algorithms. For hypertension, sensitivity ranged between 93.1-96.7%, while specificity ranged from 88.8-93.2%. For diabetes, sensitivities ranged from 93.5-96.3% with specificities between 97.1-99.0%. For osteoarthritis, sensitivities ranged from 82.0-84.4% with specificities between 92.7-94.0%. For depression, sensitivities went from 81.4-88.3%, and specificities ranged from 93.4-94.9%. Compared with the committee-created case definitions, these metrics were equivalent or better using the machine learning method. Conclusions: Machine learning algorithms produced accurate case definitions comparable to committee-created case definitions. It is possible to use machine learning techniques to develop high quality case definitions from EMR data.	en_US
dc.identifier.citation	Lethebe, B. C. (2018). Using machine learning methods to improve chronic disease case definitions in primary care electronic medical records (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. doi:10.11575/PRISM/31824	en_US
dc.identifier.doi	http://dx.doi.org/10.11575/PRISM/31824
dc.identifier.uri	http://hdl.handle.net/1880/106538
dc.language.iso	eng
dc.publisher.faculty	Cumming School of Medicine
dc.publisher.faculty	Graduate Studies
dc.publisher.institution	University of Calgary	en
dc.publisher.place	Calgary	en
dc.rights	University of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.
dc.subject	Machine Learning
dc.subject	Prediction
dc.subject	Statistics
dc.subject	Case Definition
dc.subject	Surveillance
dc.subject	Chronic Disease
dc.subject.classification	Biostatistics	en_US
dc.subject.classification	Public Health	en_US
dc.subject.classification	Statistics	en_US
dc.subject.classification	Computer Science	en_US
dc.title	Using machine learning methods to improve chronic disease case definitions in primary care electronic medical records
dc.type	master thesis
thesis.degree.discipline	Community Health Sciences
thesis.degree.grantor	University of Calgary
thesis.degree.name	Master of Science (MSc)
ucalgary.item.requestcopy	true