Upon Improving the Performance of Localized Healthcare Virtual Assistants

Virtual assistants are becoming popular in a variety of domains, responsible for automating repetitive tasks or allowing users to seamlessly access useful information. With the advances in Machine Learning and Natural Language Processing, there has been an increasing interest in applying such assist...

Full description

Saved in:
Bibliographic Details
Published in:Healthcare (Basel) 2022-01, Vol.10 (1), p.99
Main Authors: Malamas, Nikolaos, Papangelou, Konstantinos, Symeonidis, Andreas L
Format: Article
Language:English
Subjects:
chatbot
Customer services
ehealhtcare
Greek language
Localization
Machine learning
Natural language processing
Older people
Performance evaluation
Personal health
Rasa
virtual assistant
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Virtual assistants are becoming popular in a variety of domains, responsible for automating repetitive tasks or allowing users to seamlessly access useful information. With the advances in Machine Learning and Natural Language Processing, there has been an increasing interest in applying such assistants in new areas and with new capabilities. In particular, their application in e-healthcare is becoming attractive and is driven by the need to access medically-related knowledge, as well as providing first-level assistance in an efficient manner. In such types of virtual assistants, localization is of utmost importance, since the general population (especially the aging population) is not familiar with the needed "healthcare vocabulary" to communicate facts properly; and state-of-practice proves relatively poor in performance when it comes to specialized virtual assistants for less frequently spoken languages. In this context, we present a Greek ML-based virtual assistant specifically designed to address some commonly occurring tasks in the healthcare domain, such as doctor's appointments or distress (panic situations) management. We build on top of an existing open-source framework, discuss the necessary modifications needed to address the language-specific characteristics and evaluate various combinations of word embeddings and machine learning models to enhance the assistant's behaviour. Results show that we are able to build an efficient Greek-speaking virtual assistant to support e-healthcare, while the NLP pipeline proposed can be applied in other (less frequently spoken) languages, without loss of generality.
ISSN:2227-9032
2227-9032
DOI:10.3390/healthcare10010099