An intelligent environment for preventing medication errors in home treatment. (1st May 2022)

• Record Type: Journal Article
• Authors: Ciampi, Mario; Coronato, Antonio; Naeem, Muddasar; Silvestri, Stefano
• Is Part Of: Expert systems with applications. Volume 193(2022)
• Publication Date: 2022-05-01
• Subjects: Ambient intelligence -- Artificial Intelligence -- Reinforcement Learning -- Deep Learning -- Natural Language Processing -- Medication errors -- Drug–drug interactions; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2021.116434
• Languages: English
• ISSNs: 0957-4174

Abstract

Abstract: This article presents an assistance system based on home sensors, Ambient Intelligence, and Artificial Intelligence, which helps the elderly during their medical treatment at home to reduce medication errors. The sort of medication errors we address are those due to medication omission, wrong dosage or timing, drug–drug interactions. Since the patient may have some physical and/or cognitive disabilities, the proposed solution provides advanced features of self-adaptation and exploits the most cutting edge Artificial Intelligence technologies such as Reinforcement Learning, Deep Learning and Natural Language Processing (NLP) to remind and monitor adherence to the prescribed treatment. In particular, the system offers functions for (i) personalised reminds; i.e. an intelligent agent – called Tutor – self-learns (via Reinforcement Learning) the best way to communicate with the patient; (ii) feedback about the medication the patient is going to take; i.e., another intelligent agent – called Checker - identifies the pillbox that the patient is handling before taking the pill (via Deep Neural Network, Optical Character Recognition, and Barcode Reading); and, (iii) alerts in case of known drug–drug interactions; i.e., an intelligent service – called Advisor – searches for the active principles of the medication (via NLP and Unified Medical Language System (UMLS) RxNorm resources) identified by the Checker and known interactions with other medications of the treatment. The final objective is to remind effectively when and what medication is to be taken, to check that the patient is going to take the correct medication, and to alert if possible drug–drug interactions are identified, remotely reporting about the adherence to the therapy or anomalies to the caregivers and/or doctors. Experimental evaluations show encouraging results in terms of drug recognition and drug–drug interactions identification. Highlights: Medication error prevention via Artificial Intelligence based technologies. AI-based personalized reminders for medication omission. AI-based checking services for medication identification. AI-based advisory services for drug–drug interaction prevention.