A medical Chatbot using machine learning and natural language understanding

Hsu, I-Ching; Yu, Jiun-De

doi:10.1007/s11042-022-12820-4

Cited by 22 publications

(5 citation statements)

References 39 publications

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“…Hsu and Yu [8] suggested creating a machine learning and natural language processing medical chatbot. They used the Random Forest (RF) and decision tree methods.…”

Section: Related Workmentioning

confidence: 99%

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Accurate AI-Based Chatbot to Diagnose Heart Diseases Pre-Human Doctor Consultation

Majeed,

Hardan,

Hardan

et al. 2024

RIA

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Early diagnosis of heart disorders is an essential issue since they might be life-threatening conditions that call for urgent medical care. Chatbots as a user-friendly technology can be used to deliver general assistance and information anywhere anytime. However, the decision-making accuracy is one of the key challenges of Chatbots. In this paper, a medical Chatbot is developed for the purpose of early cardiac disease diagnosis based on given symptoms. An approved dataset known as the Cleveland Heart Disease dataset is utilized in this work. Hence, for accurate decision-making, three machine learning algorithms Support Vector Machine (SVM), and Logistic Regression (LR), and Extreme Gradient Boosting (XGBoost) are compared and tested to nominate the best algorithms for heart health disorders detection. Three simulation scenarios are applied to test the performance of each algorithm using first all features of the dataset, then the k-best feature selection method, and the application of Grid Search (GS) optimization algorithms. The performance evaluation shows that the prediction accuracy of the proposed XGBoost algorithm outweighs other algorithms.

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“…Hsu and Yu [8] suggested creating a machine learning and natural language processing medical chatbot. They used the Random Forest (RF) and decision tree methods.…”

Section: Related Workmentioning

confidence: 99%

“…ML Method Use of Chatbot [4] Rule based approach ✓ [5] Pattern matching and NLP ✓ [6] Decision Tree Algorithm ✓ [7] Decision Tree and RF ✓ [8] ML and natural language ✓ [9] SVM and KNN algorithms ✓ [10] NLP, machine learning…”

Section: Refmentioning

confidence: 99%

Accurate AI-Based Chatbot to Diagnose Heart Diseases Pre-Human Doctor Consultation

Majeed,

Hardan,

Hardan

et al. 2024

RIA

View full text Add to dashboard Cite

show abstract

“…The program simulates conversations with human users the same way they interact with human beings. This program can be used in real-time, 24 hours a day [16], [17]. Chatbots are divided into 2 main types including: i) rule-based chatbots, which is a chatbot developed to process and understand human conversations according to predefined rules and conditions [18]; however, when a question is outside the predefined rules or conditions, the program will not be able to answer it or provide a wrong answer [19], and ii) AI-based chatbot or intelligent bot, a chatbot developed with natural language processing that understands natural language or the language that humans use to communicate in everyday life without relying on any predefined answers [20].…”

Section: Chatbotmentioning

confidence: 99%

A Thai-language chatbot analyzing mosquito-borne diseases using Jaccard similarity

Chanakot,

Sanrach

2024

Bulletin EEI

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The objective of this study is to create a Thai-language chatbot analyzing mosquito-borne diseases using Jaccard similarity with an aim to develop an artificial intelligence (AI)-based chatbot used to analyze Aedes-borne diseases through natural language processing. The analysis occurred when the symptoms provided by users on the chatbot were assessed to select relevant words as text attributes using the term frequency-inverse document frequency (TF-IDF) before the Jaccard similarity was used to measure the similarity of the information on the mosquito-borne disease database. The Line Messaging API was applied to facilitate communication between users and the chatbot through the Line application. The chatbot applied PHP 7.2.34 and MySQL 5.7.32 for database management, with Apache 2.2.29 serving as the bot server. The performance evaluation of the chatbot revealed that the chatbot accurately understood user intentions with an intent accuracy of 85.00%. Likewise, the usability of the chatbot was assessed using the system usability scale (SUS), and it received a score of 89.75, indicating a high level of user-friendliness. Furthermore, it has been found that appropriate tokenization enables accurate feature selection. This leads to improved accuracy in measuring Jaccard similarity. Consequently, the chatbot is capable of providing precise responses that align with the user's intent.

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“…In healthcare chatbots can offer useful medical information based on user needs. Such AIbased systems, eliminate the cost and time in seeking medical help, particularly in rural areas, which consultation with qualified professionals is not easily available (Hsu & Yu, 2022). Moreover, mental health chatbot has shown to be an effective and engaging way to deliver mental health support and decreases depression and anxiety symptoms in students (Fitzpatrick et al, 2017;Wyllie et al, 2022).…”

Section: B Chatbotsmentioning

confidence: 99%

Applications of Artificial Intelligence and Voice Assistant in Healthcare

Ahanin¹,

Sade²,

Tat³

2022

IJARBSS

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The modern smart technology such as Artificial Intelligence (AI) is merging with humans' physical lives and is going to change the way we live, work, and interact. AI in the healthcare sector is gaining attention from researchers, health professionals, and life sciences companies. The new technology advancement has brought various opportunities in electronic health (e-health) that allows healthcare to be accessible regardless of distance using information and communication technologies (ICTs) such as use of blood pressure telemonitoring service and voice assistants. Voice Assistant (VA) as an emerging technology in healthcare helps to reduce expenses, build loyalty, drive revenue, and it is especially beneficial amidst COVID-19 outbreak as healthcare will need to move towards more touchfree technologies post-pandemic. In this paper, we summarize the latest developments of applications of AI and VA in healthcare, and some basic knowledge regarding the techniques, the current state of this technology in healthcare, and possible developments in future, which potentially can transform many aspects of patient care.

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A medical Chatbot using machine learning and natural language understanding

Cited by 22 publications

References 39 publications

Accurate AI-Based Chatbot to Diagnose Heart Diseases Pre-Human Doctor Consultation

Accurate AI-Based Chatbot to Diagnose Heart Diseases Pre-Human Doctor Consultation

A Thai-language chatbot analyzing mosquito-borne diseases using Jaccard similarity

Applications of Artificial Intelligence and Voice Assistant in Healthcare

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