20 hasil untuk "Li-Chen Fu"
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Artikel Jurnal Teks lengkap
CMOS Image Sensor with a Built-in Lane Detector
This work develops a new current-mode mixed signal Complementary Metal-Oxide-Semiconductor (CMOS) imager, which can capture images and simultaneously produce vehicle lane maps. The adopted lane detection algorithm, which was modified to be compatible with hardware requirements, can achieve a high…
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The Effects of Social Presence and Familiarity on Children–Robot Interactions
In children–robot interactions, an impression of a robot’s “social presence” (i.e., an interactive agent that feels like a person) links positively to an improved relationship with the robot. However, building relationships takes many exposures, and there is an intellectual gap in how social…
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Interpretable Deep Learning System for Identifying Critical Patients Through the Prediction of Triage Level, Hospitalization, and Length of Stay: Prospective Study
BackgroundTriage is the process of accurately assessing patients’ symptoms and providing them with proper clinical treatment in the emergency department (ED). While many countries have developed their triage process to stratify patients’ clinical severity and thus distribute medical resources…
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Online Gait Detection with an Automatic Mobile Trainer Inspired by Neuro-Developmental Treatment
This paper demonstrates the development of an automatic mobile trainer employing inertial movement units (IMUs). The device is inspired by Neuro-Developmental Treatment (NDT), which is an effective rehabilitation method for stroke patients that promotes the relearning of motor skills by repeated…
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A Novel Deep Learning–Based System for Triage in the Emergency Department Using Electronic Medical Records: Retrospective Cohort Study
BackgroundEmergency department (ED) crowding has resulted in delayed patient treatment and has become a universal health care problem. Although a triage system, such as the 5-level emergency severity index, somewhat improves the process of ED treatment, it still heavily relies on the nurse’s…
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Multimodal Depression Detection Through Conversational Interactions with an Emotion-Aware Social Robot: Pilot Study
Abstract BackgroundDepression affects more than 300 million people worldwide and is a leading contributor to the global disease burden. Traditional diagnostic methods, such as structured clinical interviews, are reliable but impractical for frequent or large-scale screening. Self-report tools like…
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Mild Cognitive Impairment Detection System Based on Unstructured Spontaneous Speech: Longitudinal Dual-Modal Framework
Abstract BackgroundIn recent years, the incidence of cognitive diseases has also risen with the significant increase in population aging. Among these diseases, Alzheimer disease constitutes a substantial proportion, placing a high-cost burden on health care systems. To give early treatment and slow…
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Early Detection of Parkinson’s Disease by Neural Network Models
This paper develops neural network models that can recognize Parkinson’s disease (PD) at its early stage. PD is a common neurodegenerative disorder that presents with progressive slow movement, tremor, limb rigidity, and gait alterations, including stooped posture, shuffling steps, festination…
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Deep learning to predict emergency department revisit using static and dynamic features (Deep Revisit): development and validation study
Abstract Background Emergency Department (ED) revisits represent a critical issue in emergency medicine. Identifying high-risk revisit cases (revisits with intensive care unit admissions, cardiac arrest, or requiring emergency surgery) is particularly important. While prior studies have explored…
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Deep learning-based Emergency Department In-hospital Cardiac Arrest Score (Deep EDICAS) for early prediction of cardiac arrest and cardiopulmonary resuscitation in the emergency department
Abstract Background Timely identification of deteriorating patients is crucial to prevent the progression to cardiac arrest. However, current methods predicting emergency department cardiac arrest are primarily static, rule-based with limited precision and cannot accommodate time-series data. Deep…
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An efficient context-aware screening system for Alzheimer's disease based on neuropsychology test
Abstract Alzheimer's disease (AD) and other dementias have become the fifth leading cause of death worldwide. Accurate early detection of the disease and its precursor, Mild Cognitive Impairment (MCI), is crucial to alleviate the burden on the healthcare system. While most of the existing work in…
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Artikel Jurnal Teks lengkap
Social Robots for Evaluating Attention State in Older Adults
Sustained attention is essential for older adults to maintain an active lifestyle, and the deficiency of this function is often associated with health-related risks such as falling and frailty. The present study examined whether the well-established age-effect on reducing mind-wandering, the drift…
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Assessing Schizophrenia Patients Through Linguistic and Acoustic Features Using Deep Learning Techniques
Thought, language, and communication disorders are among the salient characteristics of schizophrenia. Such impairments are often exhibited in patients’ conversations. Researches have shown that assessments of thought disorder are crucial for tracking the clinical patients’ conditions and early…
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The effect of electroacupuncture merged with rehabilitation for frozen shoulder syndrome: A single-blind randomized sham-acupuncture controlled study
Purpose: Frozen shoulder syndrome (FSS) causes pain and reduces the range of motion in the shoulder joint. To investigate the short and medium-term effects of electroacupuncture in people with FSS, we evaluated the therapeutic effects of true and sham electroacupuncture on pain relief and…
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Multimodal Assessment of Schizophrenia Symptom Severity From Linguistic, Acoustic and Visual Cues
Assessing the condition of every schizophrenia patient correctly normally requires lengthy and frequent interviews with professionally trained doctors. To alleviate the time and manual burden on those mental health professionals, this paper proposes a multimodal assessment model that predicts the…
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A Digital Screening System for Alzheimer Disease Based on a Neuropsychological Test and a Convolutional Neural Network: System Development and Validation
BackgroundAlzheimer disease (AD) and other types of dementia are now considered one of the world’s most pressing health problems for aging people worldwide. It was the seventh-leading cause of death, globally, in 2019. With a growing number of patients with dementia and increasing costs for…
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Cardio-respiratory deep learning model to predict hospital admission from emergency department electrocardiogram images and respiration videos: a prospective study
Abstract Background Early prediction of hospital admission at the emergency department (ED) triage can improve patient flow and resource allocation. Most existing models rely solely on structured data. Incorporating multimodal physiologic information, such as cardiac and respiratory signals, may…
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Deep Learning–Based Electrocardiogram Model (EIANet) to Predict Emergency Department Cardiac Arrest: Development and External Validation Study
BackgroundIn-hospital cardiac arrest (IHCA) is a severe and sudden medical emergency that is characterized by the abrupt cessation of circulatory function, leading to death or irreversible organ damage if not addressed immediately. Emergency department (ED)–based IHCA (EDCA) accounts for 10% to 20%…
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Pain Assessment in the Emergency Department: A Prospective Videotaped Study
Introduction: Research suggests that pain assessment involves a complex interaction between patients and clinicians. We sought to assess the agreement between pain scores reported by the patients themselves and the clinician’s perception of a patient’s pain in the emergency department (ED). In…