Related papers: Human-centered Perspectives on a Clinical Decision…
Adopting a good health information system (HIS) is essential for providing high-quality healthcare. With rapid advances in technology in the healthcare industry in recent years, healthcare providers seek effective options to deal with…
Obsessive-compulsive disorder (OCD) is a mental health condition that significantly impacts people's quality of life. While evidence-based therapies such as exposure and response prevention (ERP) can be effective, managing OCD symptoms in…
Reminiscence therapy is an inexpensive non-pharmacological therapy commonly used due to its therapeutic value for PwD, as it can be used to promote independence, positive moods and behavior, and improve their quality of life. Caregivers are…
The advancement of AI systems for mental health support is hindered by limited access to therapeutic conversation data, particularly for trauma treatment. We present Thousand Voices of Trauma, a synthetic benchmark dataset of 3,000 therapy…
Post-Traumatic Stress Disorder (PTSD) remains underdiagnosed in clinical settings, presenting opportunities for automated detection to identify patients. This study evaluates natural language processing approaches for detecting PTSD from…
Virtual reality exposure therapy (VRET) enables controlled exposure to trauma-related stimuli to facilitate memory access and emotional processing. However, the field remains underexplored for complex post-traumatic stress disorder…
Post-Traumatic Stress Disorder (PTSD) is a multifaceted mental health condition, particularly challenging for individuals with pre-existing medical conditions. This review critically examines the intersection of PTSD and chronic illnesses…
The lack of explainability using relevant clinical knowledge hinders the adoption of Artificial Intelligence-powered analysis of unstructured clinical dialogue. A wealth of relevant, untapped Mental Health (MH) data is available in online…
Clinical decision support systems combine knowledge and data from a variety of sources, represented by quantitative models based on stochastic methods, or qualitative based rather on expert heuristics and deductive reasoning. At the same…
As cannabis use has increased in recent years, researchers have come to rely on sophisticated machine learning models to predict cannabis use behavior and its impact on health. However, many artificial intelligence (AI) models lack…
Participatory Design (PD) methods are effective in understanding older adults' perspectives, concerns, and wishes and generating ideas for new intelligent aids. The aim of our study was first to map perceptions and explore the needs of…
Accurate models of clinical actions and their impacts on disease progression are critical for estimating personalized optimal dynamic treatment regimes (DTRs) in medical/health research, especially in managing chronic conditions.…
Children with chronic conditions face evolving challenges in daily activities, peer relationships, and clinical care. Younger children often rely on parental support, while older ones seek independence. Prior studies on chronic conditions…
Mental illness remains one of the most critical public health issues. Despite its importance, many mental health professionals highlight a disconnect between their training and actual real-world patient practice. To help bridge this gap, we…
This research paper outlines the development and implementation of a novel Clinical Decision Support System (CDSS) that integrates AI predictive modeling with medical knowledge bases. It utilizes the quantifiable information elements in lab…
A Clinical Decision Support System (CDSS) is designed to enhance clinician decision-making by combining system-generated recommendations with medical expertise. Given the high costs, intensive labor, and time-sensitive nature of medical…
Clinical Decision Support Systems (CDSS) utilize evidence-based knowledge and patient data to offer real-time recommendations, with Large Language Models (LLMs) emerging as a promising tool to generate plain-text explanations for medical…
Clinical guidance systems have been widely adopted to help medical staffs to avoid preventable medical errors such as delay in diagnosis, treatment or untended deviations from best practice guidelines. However, because patient condition…
Automated decision systems (ADS) are broadly deployed to inform and support human decision-making across a wide range of consequential settings. However, various context-specific details complicate the goal of establishing meaningful…
Deep learning, an area of machine learning, is set to revolutionize patient care. But it is not yet part of standard of care, especially when it comes to individual patient care. In fact, it is unclear to what extent data-driven techniques…