AIMC Topic: Hotlines
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Machine Learning Approach to Identifying Empathy Using the Vocals of Mental Health Helpline Counselors: Algorithm Development and Validation.
JMIR formative research
Apr 16, 2025
BACKGROUND: This research study aimed to detect the vocal features immersed in empathic counselor speech using samples of calls to a mental health helpline service.
Acoustic Features for Identifying Suicide Risk in Crisis Hotline Callers: Machine Learning Approach.
Journal of medical Internet research
Apr 14, 2025
BACKGROUND: Crisis hotlines serve as a crucial avenue for the early identification of suicide risk, which is of paramount importance for suicide prevention and intervention. However, assessing the risk of callers in the crisis hotline context is cons...
Comparison of an AI Chatbot With a Nurse Hotline in Reducing Anxiety and Depression Levels in the General Population: Pilot Randomized Controlled Trial.
JMIR human factors
Mar 6, 2025
BACKGROUND: Artificial intelligence (AI) chatbots have been customized to deliver on-demand support for people with mental health problems. However, the effectiveness of AI chatbots in tackling mental health problems among the general public in Hong ...
Real-time assistance in suicide prevention helplines using a deep learning-based recommender system: A randomized controlled trial.
International journal of medical informatics
Dec 17, 2024
OBJECTIVE: To evaluate the effectiveness and usability of an AI-assisted tool in providing real-time assistance to counselors during suicide prevention helpline conversations.
Speech based suicide risk recognition for crisis intervention hotlines using explainable multi-task learning.
Journal of affective disorders
Nov 9, 2024
BACKGROUND: Crisis Intervention Hotline can effectively reduce suicide risk, but suffer from low connectivity rates and untimely crisis response. By integrating speech signals and deep learning to assist in crisis assessment, it is expected to enhanc...
The Most Effective Interventions for Classification Model Development to Predict Chat Outcomes Based on the Conversation Content in Online Suicide Prevention Chats: Machine Learning Approach.
JMIR mental health
Sep 26, 2024
BACKGROUND: For the provision of optimal care in a suicide prevention helpline, it is important to know what contributes to positive or negative effects on help seekers. Helplines can often be contacted through text-based chat services, which produce...
Predicting suicide risk in real-time crisis hotline chats integrating machine learning with psychological factors: Exploring the black box.
Suicide & life-threatening behavior
Feb 12, 2024
BACKGROUND: This study addresses the suicide risk predicting challenge by exploring the predictive ability of machine learning (ML) models integrated with theory-driven psychological risk factors in real-time crisis hotline chats. More importantly, w...
Trends in reasons for emergency calls during the COVID-19 crisis in the department of Gironde, France using artificial neural network for natural language classification.
Scandinavian journal of trauma, resuscitation and emergency medicine
Mar 31, 2021
OBJECTIVES: During periods such as the COVID-19 crisis, there is a need for responsive public health surveillance indicators in order to monitor both the epidemic growth and potential public health consequences of preventative measures such as lockdo...
Digital triage: Novel strategies for population health management in response to the COVID-19 pandemic.
Healthcare (Amsterdam, Netherlands)
Oct 26, 2020
The COVID-19 pandemic has created unique challenges for the U.S. healthcare system due to the staggering mismatch between healthcare system capacity and patient demand. The healthcare industry has been a relatively slow adopter of digital innovation ...
Acceptability of artificial intelligence (AI)-enabled chatbots, video consultations and live webchats as online platforms for sexual health advice.
BMJ sexual & reproductive health
Jan 21, 2020
OBJECTIVES: Sexual and reproductive health (SRH) services are undergoing a digital transformation. This study explored the acceptability of three digital services, (i) video consultations via Skype, (ii) live webchats with a health advisor and (iii) ...
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