fieldid E-Thesis & Research สถาบันเทคโนโลยีไทย-ญี่ปุ่น
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บทคัดย่อ
A MACHINE LEARNING ALGORITHMS FOR DRUG IDENTIFICATION: ENHANCING MEDICATION SAFTEY IN THAILAND : Medication errors are among the leading causes of patient harm within the healthcare system. To address this critical issue, a variety of tools and solutions have emerged to minimize the occurrence of these errors, particularly those stemming from Look-Alike, Sound-Alike (LASA) drugs. Notable among these solutions are automated dispensing systems that utilize barcode scanning and RFID technology to enhance accuracy in medication administration. This study investigates the challenge of drug identification through a machine learning approach. Initially, a dataset comprising commonly used medications in Thailand was manually curated. Subsequently, various versions of the YOLO (You Only Look Once) object detection models were trained and evaluated to assess their efficacy in drug identification. The primary goal of this research is to develop and compare the performance of these different YOLO model variants. Evaluation metrics for this study include precision, recall, F1-score, and mean average precision (mAP). The results demonstrate that the YOLOv8-nano model achieves the highest accuracy in drug identification, recording a mAP score of 99.5%, a recall of 99.7%, and an F1 score of 99.4%, outperforming other versions. In conclusion, this research highlights the significant potential of machine learning as a critical component in efforts to mitigate medication errors in the future. By improving drug identification processes, these advanced technologies can enhance patient saf
ผู้แต่ง
Sorayus Kittisorayut
ประเภทสิ่งพิมพ์
Thesis (MIT) 2024
เลขหน้า
48
หัวเรื่อง
Information Technology--Thesis
หัวเรื่อง
Thai-Nichi Institute of Technology.Information Technology--Thesis
เอกสารฉบับเต็ม
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