Dataset Information

Performance evaluation of a prescription medication image classification model: an observational cohort.

ABSTRACT: Technology assistance of pharmacist verification tasks through the use of machine intelligence has the potential to detect dangerous and costly pharmacy dispensing errors. National Drug Codes (NDC) are unique numeric identifiers of prescription drug products for the United States Food and Drug Administration. The physical form of the medication, often tablets and capsules, captures the unique features of the NDC product to help ensure patients receive the same medication product inside their prescription bottle as is found on the label from a pharmacy. We report and evaluate using an automated check to predict the shape, color, and NDC for images showing a pile of pills inside a prescription bottle. In a test set containing 65,274 images of 345 NDC classes, overall macro-average precision was 98.5%. Patterns of incorrect NDC predictions based on similar colors, shapes, and imprints of pills were identified and recommendations to improve the model are provided.

SUBMITTER: Lester CA

PROVIDER: S-EPMC8316316 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature

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Performance evaluation of a prescription medication image classification model: an observational cohort.

Lester Corey A CA Li Jiazhao J Ding Yuting Y Rowell Brigid B Yang Jessie 'Xi' J' Kontar Raed Al RA

NPJ digital medicine 20210727 1

Technology assistance of pharmacist verification tasks through the use of machine intelligence has the potential to detect dangerous and costly pharmacy dispensing errors. National Drug Codes (NDC) are unique numeric identifiers of prescription drug products for the United States Food and Drug Administration. The physical form of the medication, often tablets and capsules, captures the unique features of the NDC product to help ensure patients receive the same medication product inside their pre ...[more]

PMID: 34315995

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Project description:The aim of this study was to examine the prescribing patterns of medications quantified by the performance measures for acute myocardial infarction (AMI).Current performance measures for AMI are designed to improve quality by quantifying the use of evidence-based treatments. However, these measures only assess medication prescription. Whether patients receive optimal dosing of secondary prevention medications at the time of and after discharge after AMI is unknown.We assessed treatment doses of beta-blockers, statins, and angiotensin-converting enzyme inhibitors (ACEI)/angiotensin II receptor blockers (ARBs) at discharge and 12 months after AMI among 6,748 patients from 31 hospitals enrolled in 2 U.S. registries (2003 to 2008). Prescribed doses were categorized as none, low (<50% target [defined from seminal clinical trials]), moderate (50% to 74% target), or goal (? 75% target). Patients with contraindications were excluded from analyses for that medication.Most eligible patients (>87%) were prescribed some dose of each medication at discharge, although only 1 in 3 patients were prescribed these medications at goal doses. Of patients not discharged on goal doses, up-titration during follow-up occurred infrequently (approximately 25% of patients for each medication). At 12 months, goal doses of beta-blockers, statins, and ACEI/ARBs were achieved in only 12%, 26%, and 32% of eligible patients, respectively. After multivariable adjustment, prescription of goal dose at discharge was strongly associated with being at goal dose at follow-up: beta-blockers, adjusted odds ratio (OR): 6.08 (95% confidence interval [CI]: 3.70 to 10.01); statins, adjusted OR: 8.22 (95% CI: 6.20 to 10.90); ACEI/ARBs, adjusted OR: 5.80 (95% CI: 2.56 to 13.16); p < 0.001 for each.Although nearly all patients after an AMI are discharged on appropriate secondary prevention medications, dose increases occur infrequently, and most patients are prescribed doses below those with proven efficacy in clinical trials. Integration of dose intensity into performance measures might help improve the use of optimal medical therapy after AMI.

Project description:BackgroundPrescription medication (PM) misuse/abuse has emerged as a national crisis in the United States, and social media has been suggested as a potential resource for performing active monitoring. However, automating a social media-based monitoring system is challenging-requiring advanced natural language processing (NLP) and machine learning methods. In this paper, we describe the development and evaluation of automatic text classification models for detecting self-reports of PM abuse from Twitter.MethodsWe experimented with state-of-the-art bi-directional transformer-based language models, which utilize tweet-level representations that enable transfer learning (e.g., BERT, RoBERTa, XLNet, AlBERT, and DistilBERT), proposed fusion-based approaches, and compared the developed models with several traditional machine learning, including deep learning, approaches. Using a public dataset, we evaluated the performances of the classifiers on their abilities to classify the non-majority "abuse/misuse" class.ResultsOur proposed fusion-based model performs significantly better than the best traditional model (F1-score [95% CI]: 0.67 [0.64-0.69] vs. 0.45 [0.42-0.48]). We illustrate, via experimentation using varying training set sizes, that the transformer-based models are more stable and require less annotated data compared to the other models. The significant improvements achieved by our best-performing classification model over past approaches makes it suitable for automated continuous monitoring of nonmedical PM use from Twitter.ConclusionsBERT, BERT-like and fusion-based models outperform traditional machine learning and deep learning models, achieving substantial improvements over many years of past research on the topic of prescription medication misuse/abuse classification from social media, which had been shown to be a complex task due to the unique ways in which information about nonmedical use is presented. Several challenges associated with the lack of context and the nature of social media language need to be overcome to further improve BERT and BERT-like models. These experimental driven challenges are represented as potential future research directions.

Dataset Information

Performance evaluation of a prescription medication image classification model: an observational cohort.

Publications

Performance evaluation of a prescription medication image classification model: an observational cohort.

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