Subtractive mountain clustering algorithm applied to a chatbot to assist elderly people in medication intake

TL;DR

This paper presents a novel application of a subtractive mountain clustering algorithm to develop a natural language processing chatbot aimed at assisting elderly individuals with medication management, addressing common errors due to memory loss.

Contribution

The study adapts the subtractive mountain clustering algorithm for natural language processing in chatbots, specifically tailored to support elderly medication adherence.

Findings

The algorithm successfully clusters related medication information.

The chatbot provides accurate answers to medication-related questions.

The method improves natural language understanding for elderly assistance.

Abstract

Errors in medication intake among elderly people are very common. One of the main causes for this is their loss of ability to retain information. The high amount of medicine intake required by the advanced age is another limiting factor. Thence, the design of an interactive aid system, preferably using natural language, to help the older population with medication is in demand. A chatbot based on a subtractive cluster algorithm, included in unsupervised learned models, is the chosen solution since the processing of natural languages is a necessary step in view to construct a chatbot able to answer questions that older people may pose upon themselves concerning a particular drug. In this work, the subtractive mountain clustering algorithm has been adapted to the problem of natural languages processing. This algorithm version allows for the association of a set of words into clusters. After finding the centre of every cluster -- the most relevant word, all the others are aggregated according to a defined metric adapted to the language processing realm. All the relevant stored information is processed, as well as the questions, by the algorithm. The correct processing of the text enables the chatbot to produce answers that relate to the posed queries. To validate the method, we use the package insert of a drug as the available information and formulate associated questions.