Mental Health Diagnostic System using Machine Learning ModelVaikole, ShubhangiUmmadishetty, SmaranVaidya, AnushkaKeerthi, ChaitanyaJournal of Algebraic Statistics
the high prevalence of mental health problems means that the manual review of complex patient records to make proactive care decisions is not feasible in practice. Therefore, we developed a machine learning model that uses
Personalized Early Stage Alzheimer's Disease Detection: A Case Study of President Reagan's Speeches Clinical research indicates that the onset and progression of Alzheimer`s disease lead to dementia and other mental health issues. As a result, the language capabilities of patient start to decline. ...
To do this, we used a two-step machine learning approach combining unsupervised and supervised machine learning. Results We demonstrate that there are three unique subgroups of university students who access mental health apps. Two of these, with either higher or lower mental well-being, were defi...
Mental health and Machine Learning Global Reach Collaborating with researchers in Mexico and Harvard University on a large-scale randomised controlled trial in Latin America to evaluate our culturally-adapted Spanish digital CBT program for depression and anxiety in university students. A key objective of...
Machine learningModel treesAssessmentMental healthChangeIn mental health, accurate symptom assessment and precise measurement of patient conditions are crucial for clinical decision-making and effective treatment planning. Traditional assessment methods can be burdensome, especially for vulnerable populations, ...
The proposed model is a chat-based forum that is designed to monitor the mental health of the user the model consists of two parts: (i) Web Development; (ii) Machine Learning. This application is built with a Machine Learning algorithm and integrated with web development. Web scraping and ...
The purpose of this study is to examine wearable medical sensor devices, machine and deep learning algorithms, and Internet of Things-based healthcare syst... T Jenkins - 《American Journal of Medical Research》 被引量: 0发表: 2022年 Explainable Misinformation Detection Across Multiple Social Media...
Logistic Regression, Parsimonious Bayes, Random Forest, and Support Vector Machine, and apply them to group mental health detection; Yingchao Shi6based on the relationship between failure mode networks (DMN) and human health conditions, analyzed functional magnetic resonance imaging data of seafarers, ...
machine learningdeep learningpatient health questionnaireImportance Use of asynchronous text-based counseling is rapidly growing as an easy-to-access approach to behavioral health care. Similar to in-person treatment, it is challenging to reliably assess as measures of process and content do not scale...