Extracting Information from Electronic Medical Records to Identify the Obesity Status of a Patient Based on Comorbidities and Bodyweight Measures
| Autor: | Rosa L. Figueroa, Christopher A. Flores |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Support Vector Machine
020205 medical informatics Bigram Population Medicine (miscellaneous) Health Informatics 02 engineering and technology Comorbidity Overweight Machine learning computer.software_genre Health informatics Cross-validation 03 medical and health sciences Naive Bayes classifier 0302 clinical medicine Health Information Management Artificial Intelligence 0202 electrical engineering electronic engineering information engineering medicine Data Mining Electronic Health Records Humans 030212 general & internal medicine Obesity education Natural Language Processing education.field_of_study business.industry Bayes Theorem Support vector machine Bag-of-words model Artificial intelligence medicine.symptom business computer Information Systems |
| Zdroj: | Journal of medical systems. 40(8) |
| ISSN: | 1573-689X |
| Popis: | Obesity is a chronic disease with an increasing impact on the world's population. In this work, we present a method of identifying obesity automatically using text mining techniques and information related to body weight measures and obesity comorbidities. We used a dataset of 3015 de-identified medical records that contain labels for two classification problems. The first classification problem distinguishes between obesity, overweight, normal weight, and underweight. The second classification problem differentiates between obesity types: super obesity, morbid obesity, severe obesity and moderate obesity. We used a Bag of Words approach to represent the records together with unigram and bigram representations of the features. We implemented two approaches: a hierarchical method and a nonhierarchical one. We used Support Vector Machine and Naive Bayes together with ten-fold cross validation to evaluate and compare performances. Our results indicate that the hierarchical approach does not work as well as the nonhierarchical one. In general, our results show that Support Vector Machine obtains better performances than Naive Bayes for both classification problems. We also observed that bigram representation improves performance compared with unigram representation. |
| Databáze: | OpenAIRE |
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