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Prediction of treatment dosage and duration from free-text prescriptions : an application to ADHD medications in the Swedish prescribed drug register

Zhang, Le (författare)
Karolinska Institutet
Lagerberg, Tyra (författare)
Karolinska Institutet
Chen, Qi (författare)
Karolinska Institutet
visa fler...
Ghirardi, Laura (författare)
Karolinska Institutet
D'Onofrio, Brian M. (författare)
Karolinska Institutet
Larsson, Henrik, 1975- (författare)
Örebro universitet,Institutionen för medicinska vetenskaper,Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden
Viktorin, Alexander (författare)
Karolinska Institutet
Chang, Zheng (författare)
Karolinska Institutet
visa färre...
 (creator_code:org_t)
2021-04-01
2021
Engelska.
Ingår i: Evidence-Based Mental Health. - : BMJ Publishing Group Ltd. - 1362-0347 .- 1468-960X. ; 24:4, s. 146-152
  • Tidskriftsartikel (refereegranskat)
Abstract Ämnesord
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  • BACKGROUND: Accurate estimation of daily dosage and duration of medication use is essential to pharmacoepidemiological studies using electronic healthcare databases. However, such information is not directly available in many prescription databases, including the Swedish Prescribed Drug Register.OBJECTIVE: To develop and validate an algorithm for predicting prescribed daily dosage and treatment duration from free-text prescriptions, and apply the algorithm to ADHD medication prescriptions.METHODS: We developed an algorithm to predict daily dosage from free-text prescriptions using 8000 ADHD medication prescriptions as the training sample, and estimated treatment periods while taking into account several features including titration, stockpiling and non-perfect adherence. The algorithm was implemented to all ADHD medication prescriptions from the Swedish Prescribed Drug Register in 2013. A validation sample of 1000 ADHD medication prescriptions, independent of the training sample, was used to assess the accuracy for predicted daily dosage.FINDINGS: In the validation sample, the overall accuracy for predicting daily dosage was 96.8%. Specifically, the natural language processing model (NLP1 and NLP2) have an accuracy of 99.2% and 96.3%, respectively. In an application to ADHD medication prescriptions in 2013, young adult ADHD medication users had the highest probability of discontinuing treatments as compared with other age groups. The daily dose of methylphenidate use increased with age substantially.CONCLUSIONS: The algorithm provides a flexible approach to estimate prescribed daily dosage and treatment duration from free-text prescriptions using register data. The algorithm showed a good performance for predicting daily dosage in external validation.CLINICAL IMPLICATIONS: The structured output of the algorithm could serve as basis for future pharmacoepidemiological studies evaluating utilization, effectiveness, and safety of medication use, which would facilitate evidence-based treatment decision-making.

Ämnesord

MEDICIN OCH HÄLSOVETENSKAP  -- Medicinska och farmaceutiska grundvetenskaper -- Samhällsfarmaci och klinisk farmaci (hsv//swe)
MEDICAL AND HEALTH SCIENCES  -- Basic Medicine -- Social and Clinical Pharmacy (hsv//eng)

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