A5003353585- Intensive Care Unit Cognitive Disorders
- Digital Mental Health Interventions
- Mental Health via Writing
- Mental Health Research Topics
- Artificial Intelligence in Healthcare and Education
- Family and Patient Care in Intensive Care Units
- Reinforcement Learning in Robotics
- Non-Invasive Vital Sign Monitoring
- Machine Learning in Healthcare
- Anesthesia and Sedative Agents
- Topic Modeling
- Statistical Methods in Clinical Trials
- Mobile Crowdsensing and Crowdsourcing
- Respiratory Support and Mechanisms
- Functional Brain Connectivity Studies
- Healthcare Technology and Patient Monitoring
- Healthcare Decision-Making and Restraints
- EEG and Brain-Computer Interfaces
- Neural and Behavioral Psychology Studies
- Data Stream Mining Techniques
University of Florida
2018-2025
Currently, many critical care indices are not captured automatically at a granular level, rather repetitively assessed by overburdened nurses. In this pilot study, we examined the feasibility of using pervasive sensing technology and artificial intelligence for autonomous monitoring in Intensive Care Unit (ICU). As an exemplary prevalent condition, characterized delirious patients their environment. We used wearable sensors, light sound camera to collect data on analyzed collected detect...
Abstract Attention can be attracted reflexively by sensory signals, biased learning or reward, focused voluntarily based on momentary goals. When voluntary attention is purely internal decision processes (will), rather than instructions via external cues, we call this “willed attention.” In prior work, reported ERP and fMRI correlates of willed spatial in trial-by-trial cuing tasks. Here further investigated the oscillatory mechanisms contrasting event-related EEG spectrogram between...
In cognitive psychology, automatic and self-reinforcing irrational thought patterns are known as distortions. Left unchecked, patients exhibiting these types of thoughts can become stuck in negative feedback loops unhealthy thinking, leading to inaccurate perceptions reality commonly associated with anxiety depression. this paper, we present a machine learning framework for the detection classification 15 common distortions two novel mental health free datasets collected from both...
With Artificial Intelligence (AI) increasingly permeating various aspects of society, including healthcare, the adoption Transformers neural network architecture is rapidly changing many applications. Transformer a type deep learning initially developed to solve general-purpose Natural Language Processing (NLP) tasks and has subsequently been adapted in fields, healthcare. In this survey paper, we provide an overview how adopted analyze forms data, medical imaging, structured unstructured...
Patients staying in the Intensive Care Unit (ICU) have a severely disrupted circadian rhythm. Due to patients' critical medical condition, ICU physicians and nurses provide round-the-clock clinical care, further disrupting Mistimed family visits during rest-time can also disrupt Currently, such effects are only reported based on hospital visitation policies rather than actual number of visitors care providers room. To quantify disruptions, we used deep Mask R-CNN model, learning framework...
Introduction Reinforcement learning formalizes the concept of from interactions.1 Broadly, reinforcement focuses on a setting in which an agent (decision maker) sequentially interacts with environment that is partially unknown to them. At each stage, takes action and receives reward. The objective maximize rewards accumulated long run. There are many situations health care where decisions made for approaches could prove useful decision making. Throughout this article, we consider treatment...
Currently, many critical care indices are repetitively assessed and recorded by overburdened nurses, e.g. physical function or facial pain expressions of nonverbal patients. In addition, essential information on patients their environment not captured at all, in a non-granular manner, sleep disturbance factors such as bright light, loud background noise, excessive visitations. this pilot study, we examined the feasibility using pervasive sensing technology artificial intelligence for...