Methylphenidate use among students living in junior on-campus residences of the University of the Free State

Zyl, P M Van; Joubert, G; Fechter, L; Griesel, J; Nel, Malan; Honiball, A; Serfontein, L; Diedericks, M

doi:10.4102/safp.v59i4.4736

Cited by 1 publication

(1 citation statement)

References 2 publications

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“…Positive cohort drugs were expected to have off-label drug usage, whereas the negative cohort drugs were not expected to have major off-label drug usage. After a review of the literature, the following drugs were selected as the positive cohort, as they were known to have off-label uses: pregabalin, 11 gabapentin, 11 , 12 lorazepam, 13 methylphenidate, 14 amitriptyline, 15 and quetiapine. 16 The negative cohort drugs considered were as follows: levonorgestrel, levofloxacin, clopidogrel, celecoxib, buprenorphine-naloxone, and natalizumab.…”

Section: Methodsmentioning

confidence: 99%

Application of natural language processing techniques to identify off-label drug usage from various online health communities

Dreyfus

Chaudhary²,

Bhardwaj³

et al. 2021

Journal of the American Medical Informatics Association

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Objective Outcomes mentioned on online health communities (OHCs) by patients can serve as a source of evidence for off-label drug usage evaluation, but identifying these outcomes manually is tedious work. We have built a natural language processing model to identify off-label usage of drugs mentioned in these patient posts. Materials and Methods Single patient posts from 4 major OHCs were considered for this study. A text classification model was built to classify the posts as either relevant or not relevant based on patient experience. The relevant posts were passed through a spelling correction tool, CSpell, and then medications and indications from these posts were identified using cTAKES (clinical Text Analysis and Knowledge Extraction System), a named entity recognition tool. Drug and indication pairs were identified using a dependency parser. Finally, if the paired indication was not mentioned on the label of the drug approved by U.S. Food and Drug Administration, it was tagged as off-label use of that drug. Results Using this algorithm, we identified 289 off-label indications, achieving a recall of 76%. Conclusions The method designed in this study identifies and extracts the semantic relationship between drugs and indications from demotic posts in OHCs. The results demonstrate the feasibility of using natural language processing techniques in identifying off-label drug usage across online health forums for a variety of drugs. Understanding patients’ off-label use of drugs may be able to help manufacturers innovate to better address patients’ needs and assist doctors’ prescribing decisions.

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