A Drug Identification Model developed using Deep Learning Technologies: Experience of a Medical Center in Taiwan

Abstract

Background: Issuing of correct prescriptions is a foundation of patient safety. Medication errors represent one of the most important problems in health care, with ‘look-alike and sound-alike’ (LASA) being the lead error. Existing solutions to prevent LASA still have their limitations. Deep learning techniques have revolutionized identification classifiers in many fields. In search of better image-based solutions for blister package identification problem, this study using a baseline deep learning drug identification (DLDI) aims to understand how identification confusion of look-alike images by human occurs through the cognitive counterpart of deep learning solutions and thereof to suggest further solutions to approach them.Methods: We collected images of 250 types of blister-packaged drug from the Out-Patient Department (OPD) of a medical center for identification. The deep learning framework of You Only Look Once (YOLO) was adopted for implementation of the proposed deep learning. The commonly-used F1 score, defined by precision and recall for large numbers of identification tests, was used as the performance criterion. This study trained and compared the proposed models based on images of either the front-side or back-side of blister-packaged drugs. Results: Our results showed that the total training time for the front-side model and back-side model was 5 hours 34 minutes and 7 hours 42 minutes, respectively. The F1 score of the back-side model (95.99%) was better than that of the front-side model (93.72%). Conclusions: In conclusion, this study constructed a deep learning-based model for blister-packaged drug identification, with an accuracy greater than 90%. This model outperformed identification using conventional computer vision solutions, and could assist pharmacists in identifying drugs while preventing medication errors caused by look-alike blister packages. By integration into existing prescription systems in hospitals, the results of this study indicated that using this model, drugs dispensed could be verified in order to achieve automated prescription and dispensing.

Pharmaceutical Blister Package Identification Based on Induced Deep Learning

“…Visually, it is very easy to confuse them. This is also one of the main reasons that pharmacists choose the wrong drug [5].…”

A Clinical Decision Support System for Diabetes Patients with Deep Learning: Experience of a Taiwan Medical Center

“…[19] Recently, deep learning methods have dramatically improved different elds of medical care and research [20]. They have also been used as the core methods to build the CDSS [21]. For example, convolutional neural networks (CNNs) are used to process image data and recurrent neural networks (RNNs) are used for sequential pattern problems [21,22].…”

“…They have also been used as the core methods to build the CDSS [21]. For example, convolutional neural networks (CNNs) are used to process image data and recurrent neural networks (RNNs) are used for sequential pattern problems [21,22]. Sun et al proposed a method to predict blood sugar levels at four intervals, namely 15, 30, 45, and 60 minutes, using the long short-term memory (LSTM) model and the bidirectional-LSTM (Bi-LSTM) model [23].…”