An AI-Empowered Home-Infrastructure to Minimize Medication Errors
This article presents an Artificial Intelligence (AI)-based infrastructure to reduce medication errors while following a treatment plan at home. The system, in particular, assists patients who have some cognitive disability. The AI-based system first learns the skills of a patient using the Actor–Cr...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-02-01
|
| Series: | Journal of Sensor and Actuator Networks |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2224-2708/11/1/13 |
| _version_ | 1797446278379995136 |
|---|---|
| author | Muddasar Naeem Antonio Coronato |
| author_facet | Muddasar Naeem Antonio Coronato |
| author_sort | Muddasar Naeem |
| collection | DOAJ |
| description | This article presents an Artificial Intelligence (AI)-based infrastructure to reduce medication errors while following a treatment plan at home. The system, in particular, assists patients who have some cognitive disability. The AI-based system first learns the skills of a patient using the Actor–Critic method. After assessing patients’ disabilities, the system adopts an appropriate method for the monitoring process. Available methods for monitoring the medication process are a Deep Learning (DL)-based classifier, Optical Character Recognition, and the barcode technique. The DL model is a Convolutional Neural Network (CNN) classifier that is able to detect a drug even when shown in different orientations. The second technique is an OCR based on Tesseract library that reads the name of the drug from the box. The third method is a barcode based on Zbar library that identifies the drug from the barcode available on the box. The GUI demonstrates that the system can assist patients in taking the correct drug and prevent medication errors. This integration of three different tools to monitor the medication process shows advantages as it decreases the chance of medication errors and increases the chance of correct detection. This methodology is more useful when a patient has mild cognitive impairment. |
| first_indexed | 2024-03-09T13:38:01Z |
| format | Article |
| id | doaj.art-51eb23370871454c9a3682b0609cceb1 |
| institution | Directory Open Access Journal |
| issn | 2224-2708 |
| language | English |
| last_indexed | 2024-03-09T13:38:01Z |
| publishDate | 2022-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Sensor and Actuator Networks |
| spelling | doaj.art-51eb23370871454c9a3682b0609cceb12023-11-30T21:09:37ZengMDPI AGJournal of Sensor and Actuator Networks2224-27082022-02-011111310.3390/jsan11010013An AI-Empowered Home-Infrastructure to Minimize Medication ErrorsMuddasar Naeem0Antonio Coronato1Institute of High Performance Computing and Networking, National Research Council of Italy, 80131 Napoli, ItalyInstitute of High Performance Computing and Networking, National Research Council of Italy, 80131 Napoli, ItalyThis article presents an Artificial Intelligence (AI)-based infrastructure to reduce medication errors while following a treatment plan at home. The system, in particular, assists patients who have some cognitive disability. The AI-based system first learns the skills of a patient using the Actor–Critic method. After assessing patients’ disabilities, the system adopts an appropriate method for the monitoring process. Available methods for monitoring the medication process are a Deep Learning (DL)-based classifier, Optical Character Recognition, and the barcode technique. The DL model is a Convolutional Neural Network (CNN) classifier that is able to detect a drug even when shown in different orientations. The second technique is an OCR based on Tesseract library that reads the name of the drug from the box. The third method is a barcode based on Zbar library that identifies the drug from the barcode available on the box. The GUI demonstrates that the system can assist patients in taking the correct drug and prevent medication errors. This integration of three different tools to monitor the medication process shows advantages as it decreases the chance of medication errors and increases the chance of correct detection. This methodology is more useful when a patient has mild cognitive impairment.https://www.mdpi.com/2224-2708/11/1/13artificial intelligencereinforcement learningdeep learningmedical treatmentmedication erroroptical character recognition |
| spellingShingle | Muddasar Naeem Antonio Coronato An AI-Empowered Home-Infrastructure to Minimize Medication Errors Journal of Sensor and Actuator Networks artificial intelligence reinforcement learning deep learning medical treatment medication error optical character recognition |
| title | An AI-Empowered Home-Infrastructure to Minimize Medication Errors |
| title_full | An AI-Empowered Home-Infrastructure to Minimize Medication Errors |
| title_fullStr | An AI-Empowered Home-Infrastructure to Minimize Medication Errors |
| title_full_unstemmed | An AI-Empowered Home-Infrastructure to Minimize Medication Errors |
| title_short | An AI-Empowered Home-Infrastructure to Minimize Medication Errors |
| title_sort | ai empowered home infrastructure to minimize medication errors |
| topic | artificial intelligence reinforcement learning deep learning medical treatment medication error optical character recognition |
| url | https://www.mdpi.com/2224-2708/11/1/13 |
| work_keys_str_mv | AT muddasarnaeem anaiempoweredhomeinfrastructuretominimizemedicationerrors AT antoniocoronato anaiempoweredhomeinfrastructuretominimizemedicationerrors AT muddasarnaeem aiempoweredhomeinfrastructuretominimizemedicationerrors AT antoniocoronato aiempoweredhomeinfrastructuretominimizemedicationerrors |