Supervised Machine Learning Chatbots for Perinatal Mental Healthcare

Wang, Ruyi; Wang, Jiankun; Liao, Yuan Mei; Wang, Jinyu

doi:10.1109/ichci51889.2020.00086

Cited by 27 publications

(14 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This study recommends the Fuzzy Analytic Hierarchy Process as a solution because there are many things to consider while deciding between different Chatbot systems. Finally, it is advised that the company use the Carik platform for internal training [13].…”

Section: Literature Surveymentioning

confidence: 99%

Medibot: Intelligent Healthcare Assistant

2024

IRJMETS

View full text Add to dashboard Cite

A state of complete physical, mental, and social well-being is called health. Although chatbots have been used in this sector a lot and in many different contexts in the past, there is always an opportunity for additional creative applications. AI use cases for healthcare conversations can be customized and suited to specific industries. Patients may utilize them to learn more about their condition, possible treatments, or even the extent of their insurance coverage. Numerous healthcare organizations are thinking about implementing healthcare chatbots into their operations because studies have shown that they can greatly decrease wait times and increase patient satisfaction.There are several uses for chatbots in the healthcare industry, including monitoring, anonymity, customisation, in-person interaction, and more. In this case study, the probable illness type of the patient will be ascertained based on the user's input regarding the patient's symptoms. Precautions will be advised based on the nature of the illness, and the patient will be referred to a physician who specializes in that area. The symptoms in the text were extracted using a sequential model, and the patient's condition type was then predicted using the CNN method.

show abstract

Section: Literature Surveymentioning

confidence: 99%

Medibot: Intelligent Healthcare Assistant

2024

IRJMETS

View full text Add to dashboard Cite

show abstract

“…They have attempted to build personalized model for each user and have experimented with Naïve Bayes, ordinal Naives Bayes and decision tree (DT). Similarly Wang et al (2020) have tried to evaluate the stress among females during pregnancy and within 24 months after the birth of a child, which affects pregnant women, newborns and family relationships. Due to pregnancy, the female tends to undergo lots of physical and mental changes that takes a toll on mental health status.…”

Section: Related Workmentioning

confidence: 99%

“…The need of hour for MHC system is to have a highly accurate framework to recognize stress. The proposed framework for MHC system supports single or multimodal sensor input to the proposed end-to-end framework for stress recognition that has not been proposed in previous literature (Bobade & Vani, 2020;Wang et al, 2020). A simple and flexible end-to-end framework for stress recognition using wearable physiological sensors is proposed using DL framework that excludes manual feature extraction steps that was required in machine learning algorithm based studies (Garg et al, 2021;Schmidt et al, 2018b) and so forth.…”

Section: Motivation and Contributionsmentioning

confidence: 99%

Stress recognition with multi‐modal sensing using bootstrapped ensemble deep learning model

2023

View full text Add to dashboard Cite

The factors that influence a person's mental health are numerous, interconnected, and multi-dimensional. Recognition of stress is one of the facets in developing the Mental Healthcare (MHC) system framework. With the advent of technology, smart wearable devices have paved a way to collect data in real-time to provide the cutting-edge reports about the individual. Due to the physiological sensors present in the smart wearable devices, it is now possible to have a robust system to recognize the stress of the smart wearable devices user thus consecutively leading to recognition of factors in leading to stress. However, the current MHC system for recognition and identification of stress have several drawbacks. First, stress recognition is mostly designed for a particular group of individuals like occupational stress, perinatal maternal stress, or health worker stress and fails to propose a framework that would not be targeted for a particular group of individual. Second, most of the previous work done on stress recognition focuses on the extraction of handcrafted features thus requiring human intervention and expertise. To address these issues, this study proposes, a hybrid deep learning based ensemble approach for automated extraction of features and classification into various state of stress for MHC system. The proposed framework takes input from wearable physiological sensors and is provided to deep learning classifier of convolutional neural network (CNN) and CNN-long short term memory based ensemble model. The proposed framework has been experimented on the wearable stress and affect detection dataset and reports an accuracy of 91.52% that is 7.20% higher than earlier reported accuracies from other machine learning and deep learning models.

show abstract

“…They experimented with ordinal naive Bayes, naive Bayes, and decision trees with the goal of developing a special model for each user. Similarly to this, Wang et al 23 tried to measure the stress that women go through throughout pregnancy and the first 24 months after giving birth, which has an impact on expectant mothers. The multiple physical and psychological changes brought on by pregnancy have a detrimental effect on a woman's mental health.…”

Section: Related Workmentioning

confidence: 99%

Hybrid deep learning model for wearable sensor‐based stress recognition for Internet of Medical Things (IoMT) system

Singh,

Chetia Phukan,

Gupta

et al. 2023

Int J Communication

View full text Add to dashboard Cite

SummaryThere are numerous, interrelated, and multi‐dimensional aspects that influence a person's mental health, one of them being stress. Smart wearable technology having physiological and motion sensors has paved the way for real‐time data collection to deliver cutting‐edge information about the stress of individuals. It is now possible to build an Internet of Medical Things (IoMT) system that can recognise the user's stress, revealing the elements that cause stress. However, there are significant gaps in the existing system for stress recognition. To begin with, stress recognition is primarily studied for a specific set of people, such as occupational stress among office working persons or hospital staff during emergency duties, and so forth. Second, most past work on stress recognition has focused on extracting handcrafted traits, which necessitates human interaction and skill. To overcome above mentioned challenges, this work proposes a novel IoMT framework for continuous stress recognition for mental well‐being. This paper presents a hybrid deep learning (DL) approach for automatically retrieving features and classification into various stress states for the IoMT system to address these difficulties. The proposed expanded system gathers data from wearable physiological sensors and feeds it into a convolutional neural network–long short‐term memory (CNN‐LSTM), a hybrid DL classifier. The suggested approach has been tested on the wearable stress and affect detection (WESAD) dataset. It has a reported accuracy of 90.45%, which is higher than previously reported accuracies from existing machine learning (ML) and DL approaches.

show abstract

Supervised Machine Learning Chatbots for Perinatal Mental Healthcare

Cited by 27 publications

References 5 publications

Medibot: Intelligent Healthcare Assistant

Medibot: Intelligent Healthcare Assistant

Stress recognition with multi‐modal sensing using bootstrapped ensemble deep learning model

Hybrid deep learning model for wearable sensor‐based stress recognition for Internet of Medical Things (IoMT) system

Contact Info

Product

Resources

About