Machine Learning in Healthcare Communication

Siddique, Sarkar; Chow, James C. L.

doi:10.3390/encyclopedia1010021

Cited by 90 publications

(41 citation statements)

References 97 publications

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“… 15 Both AI and NLP are becoming more popular in the medical domain with the continuous advancement of information technology. 16 , 17 Several computing developers have developed effective and efficient systems by including these techniques to save users time and provide accurate results. 17 This study presents a designed chatbot application to manage all patient queries regarding different hospitals.…”

Section: Discussionmentioning

confidence: 99%

Web-based chatbot for Frequently Asked Queries (FAQ) in Hospitals

Mittal¹,

Battineni

Singh³

et al. 2021

Journal of Taibah University Medical Sciences

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Objectives Local hospitals are operated by the resigned association of patients as passive communication channels. The online hospital data related to the users’ queries are not transparent and reliable. Therefore, it is crucial to have an intelligent web chatbot that manages user requests and provides quick access to local hospital information. In this paper, we present a framework and functionality of a chatbot developed using web technologies. Methods The bot engine was integrated by several machine learning approaches like gradient descent (GD) and natural language processing (NLP) algorithms. The trained data entered into the bot were split into mini-word batches, and the GD algorithm was applied sequentially on each mini-batch. The NLP methods involved in converting a word to its stem with a text result less readable by humans. Results The employed ML algorithms were successfully incorporated to manage the alternative synchronisation of text and voice messages. Conclusions The proposed bot can be a better solution for data extraction from local hospital which functioning as a good communication channel for both users and hospital staff and helpful in reducing the crowd.

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Section: Discussionmentioning

confidence: 99%

Web-based chatbot for Frequently Asked Queries (FAQ) in Hospitals

Mittal¹,

Battineni

Singh³

et al. 2021

Journal of Taibah University Medical Sciences

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“…The advantage of machine learning, of course, is the opportunity to make informed clinical decisions by considering the complex preceding health data to provide an appropriate conclusion. The study focuses on different fields in machine learning such as NLP and DNN which can be applied in the healthcare sector [4] . It is rightly pointed out that the relative novelty of AI and deep learning have been capacitated by big data analytics and advancement in cloud storage modalities.…”

Section: Review Of Literaturementioning

confidence: 99%

Role of artificial intelligence in health communication

Pandey

Chakraborty

Kumar

2022

ijhs

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Artificial intelligence is a transformational technology of the digital age and increasingly capturing the world. Apart from robotics, this technology is also useful in the health sector. Smartwatches are a good example of artificial intelligence and health communication. This device provides you with some specific information like blood pressure, oxygen level, sleep monitoring, etc. which is very useful for a common man to track his/her health. This device communicates to you about your primary health issue and informs you to visit a doctor. So, in this study, researchers explored and examined the role and relevance of artificial intelligence in health communication, Research evaluates the level of awareness among users and non-users of smartwatches AI technology and finds out the effectiveness of using smartwatches. For data gathering, researchers will use a survey method. In this study, a quantitative approach was used and a random sampling method has been adopted for the selection of smartwatch users.

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“…Meanwhile, contrary to DNNs, scholars [9,14,16,19] have evidently demonstrated the practical effectiveness of modelling medical chatbots with rule-based NLP frameworks that are based on traditional hand-crafted machine-learning approaches. This approach is often based on pattern-matching [9,10,14,19] with a knowledge base for domain-specific tasks and requires low-volume data and less computational resources for model training in contrast to the DNN approach.…”

Section: Related Workmentioning

confidence: 99%

“…In medical practice, before diagnosis and subsequent treatment are administered to patients, bilateral interactions based on question-and-answer (Q&A) is the basic and most vital approach used by doctors to fathom the type and stage of patients' internal symptoms (such as early-stage FBC), which are usually invisible to the eyes by mere observation. Therefore, effective communication between doctors and patients is generally advantageous towards achieving successful medicare outcomes [8], and telemedicine via chatbots is the promising modern way to foster it [9,10]. Based on this predication, various healthcare establishments are beginning to adopt interactive text-based chatbot systems [11] to streamline communication between doctors and their remotely distributed patients; with artificial intelligence (AI) technologies, chatbots are quickly gaining the potential to facilitate interactive communication-dependent services and the capability to independently make adaptive judgment calls reliably in task-oriented closed domains.…”

Section: Introductionmentioning

confidence: 99%

Online Textual Symptomatic Assessment Chatbot Based on Q&A Weighted Scoring for Female Breast Cancer Prescreening

et al. 2021

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The increasing number of female breast cancer (FBC) incidences in the East predominated by Chinese language speakers has generated concerns over women’s medicare. To minimize the mortality rate associated with FBC in the region, governments and health experts are jointly encouraging women to undergo mammography screening at the earliest suspicion of FBC symptoms. However, studies show that a huge number of women affected by FBC tend to delay medical consultation at its early stage as a result of factors such as complacency due to unawareness of FBC symptoms, procrastination due to lifestyle, and the feeling of embarrassment in discussing private matters especially with medical personnel of the opposite gender. To address these issues, we propose a symptomatic assessment chatbot (SAC) based on artificial intelligence (AI) designed to prescreen women for FBC symptoms via a textual question-and-answer (Q&A) approach. The purpose of our chatbot is to assist women in engaging in communication regarding FBC symptoms, so as to subsequently initiate formal medical consultations for early FBC diagnosis and treatment. We implemented the SAC systematically with some of the latest natural language processing (NLP) techniques suitable for Chinese word segmentation (CWS) and trained the model with real-world FBC Q&A data obtained from a major hospital in Taiwan. The results from our experiments showed that the SAC achieved very high accuracy in FBC assessment scoring in comparison to FBC patients’ screening benchmark scores obtained from doctors.

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Machine Learning in Healthcare Communication

Cited by 90 publications

References 97 publications

Web-based chatbot for Frequently Asked Queries (FAQ) in Hospitals

Web-based chatbot for Frequently Asked Queries (FAQ) in Hospitals

Role of artificial intelligence in health communication

Online Textual Symptomatic Assessment Chatbot Based on Q&A Weighted Scoring for Female Breast Cancer Prescreening

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