Improving the Accuracy of Predictive Models for Outcomes of Antidepressants by Using an Ontological Adjustment Approach
Abstract
:Featured Application
Abstract
1. Introduction
2. Materials and Methods
2.1. Source of Data
2.2. Sample
2.3. Measure of Antidepressant Effectiveness
2.4. Methods of Ontological Adjustment
2.5. Measure of Accuracy of Estimated Treatment Effectiveness
3. Results
3.1. Threshold for Adjustments
3.2. Comparison of AROC Gaps between Models with and without OA
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
ICD | International Classification of Disease |
OLDW | OptumLabs® Data Warehouse |
AROC | Area under the receiver operating curves |
OA | Ontological adjustment |
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Antidepressant | Threshold 1 (No Adjustment) | Threshold 100 (Adjusted Data) | ||||
---|---|---|---|---|---|---|
Training | Test | Gap | Training | Test | Gap | |
AMITRIPTYLINE | 80.2% | 68.4% | 11.9% | 72.3% | 68.0% | 4.4% |
BUPROPION | 73.9% | 67.3% | 6.6% | 69.8% | 67.3% | 2.5% |
CITALOPRAM | 65.4% | 63.2% | 2.2% | 63.9% | 63.3% | 0.7% |
DESVENLAFAXINE | 76.5% | 68.9% | 7.5% | 70.2% | 69.2% | 1.0% |
DOXEPIN | 79.9% | 64.6% | 15.4% | 67.4% | 64.2% | 3.2% |
DULOXETINE | 67.3% | 61.9% | 5.3% | 64.0% | 62.3% | 1.6% |
ESCITALOPRAM | 63.7% | 60.5% | 3.2% | 61.5% | 60.3% | 1.2% |
FLUOXETINE | 66.2% | 64.8% | 1.4% | 65.3% | 64.8% | 0.5% |
FLUVOXAMINE | 89.2% | 63.5% | 25.7% | 69.3% | 62.1% | 7.2% |
IMIPRAMINE | 94.7% | 64.9% | 29.8% | 77.9% | 67.8% | 10.2% |
MIRTAZAPINE | 72.0% | 59.6% | 12.4% | 63.6% | 59.6% | 4.0% |
NEFAZODONE | 88.7% | 58.4% | 30.3% | 68.1% | 59.9% | 8.2% |
NORTRIPTYLINE | 85.1% | 63.9% | 21.2% | 70.9% | 65.4% | 5.5% |
PAROXETINE | 65.0% | 62.1% | 2.9% | 62.9% | 62.1% | 0.8% |
PRAMIPEXOLE | 76.0% | 58.6% | 17.4% | 64.0% | 58.9% | 5.0% |
ROPINIROLE | 77.1% | 58.4% | 18.7% | 64.4% | 59.0% | 5.4% |
SERTRALINE | 65.6% | 64.2% | 1.4% | 64.7% | 64.2% | 0.5% |
TRAZODONE | 84.3% | 72.5% | 11.8% | 78.1% | 73.7% | 4.4% |
VENLAFAXINE | 67.8% | 64.2% | 3.6% | 65.4% | 64.4% | 1.1% |
VORTIOXETINE | 85.6% | 66.2% | 19.4% | 70.3% | 68.9% | 1.3% |
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Min, H.; Alemi, F.; Hane, C.A.; Nori, V.S. Improving the Accuracy of Predictive Models for Outcomes of Antidepressants by Using an Ontological Adjustment Approach. Appl. Sci. 2022, 12, 1479. https://doi.org/10.3390/app12031479
Min H, Alemi F, Hane CA, Nori VS. Improving the Accuracy of Predictive Models for Outcomes of Antidepressants by Using an Ontological Adjustment Approach. Applied Sciences. 2022; 12(3):1479. https://doi.org/10.3390/app12031479
Chicago/Turabian StyleMin, Hua, Farrokh Alemi, Christopher A. Hane, and Vijay S. Nori. 2022. "Improving the Accuracy of Predictive Models for Outcomes of Antidepressants by Using an Ontological Adjustment Approach" Applied Sciences 12, no. 3: 1479. https://doi.org/10.3390/app12031479
APA StyleMin, H., Alemi, F., Hane, C. A., & Nori, V. S. (2022). Improving the Accuracy of Predictive Models for Outcomes of Antidepressants by Using an Ontological Adjustment Approach. Applied Sciences, 12(3), 1479. https://doi.org/10.3390/app12031479