Publications by authors named "Li-Chen Fu"

Background: In-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% of all IHCA cases. Early detection of EDCA is crucial, yet identifying subtle signs of cardiac deterioration is challenging.

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This paper introduces a dual-modal early cognitive impairment detection system based on autobiographical memory (AM) tests, and our approach is to automatically extract pre-defined acoustic features and self-designed embeddings to enhance linguistic representation of the spontaneous speech data. By integrating dual-modal data, we effectively enrich the features that aid in model learning, especially addressing the subtle symptoms exhibited by individuals with mild cognitive impairment (MCI), an intermediate stage between healthy individuals and those with Alzheimer's disease (AD). To account for spontaneous speech's unstructured and implicit nature, two additional embeddings, namely, speaker embedding and conversation embedding, are introduced to augment the information available for model learning, thus enriching the feature set for improving the model accuracy.

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Article Synopsis
  • Timely detection of deteriorating patients is essential to prevent cardiac arrest, but current methods lack precision and adaptability, making deep learning a promising alternative for continuous prediction in emergency departments.* -
  • The research developed the Deep EDICAS, a deep learning scoring system that integrates both tabular and time-series data, achieving high accuracy with AUPRC and AUROC scores significantly better than existing early warning scores.* -
  • This study highlights the potential of deep learning in predicting not just cardiac arrest but also the need for CPR, marking an initial step in improving detection methods in emergency care settings.*
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Anemia is a significant global health issue, affecting over a billion people worldwide, according to the World Health Organization. Generally, the gold standard for diagnosing anemia relies on laboratory measurements of hemoglobin. To meet the need in clinical practice, physicians often rely on visual examination of specific areas, such as conjunctiva, to assess pallor.

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Background: Triage 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, there are still some limitations of the current triage process. Since the triage level is mainly identified by experienced nurses based on a mix of subjective and objective criteria, mis-triage often occurs in the ED.

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In healthcare facilities, answering the questions from the patients and their companions about the health problems is regarded as an essential task. With the current shortage of medical personnel resources and an increase in the patient-to-clinician ratio, staff in the medical field have consequently devoted less time to answering questions for each patient. However, studies have shown that correct healthcare information can positively improve patients' knowledge, attitudes, and behaviors.

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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 severity level of each symptom defined in Scale for the Assessment of Thought, Language, and Communication (TLC) and Positive and Negative Syndrome Scale (PANSS) based on the patient's linguistic, acoustic, and visual behavior. The proposed deep-learning model consists of a multimodal fusion framework and four unimodal transformer-based backbone networks.

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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.

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Aims/hypothesis: To determine whether lower than currently accepted glycemic levels could lead to optimal risk reduction of incident diabetes among individuals with prediabetes.

Methods: We enrolled 9903 individuals with prediabetes and 16,902 individuals with normoglycemia from a prospective cohort participating health check-ups between 2006 and 2017. While classifying fasting glucose into <5.

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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 addition, we attempted to identify patient and physician factors that lead to greater discrepancies in pain assessment.

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Schizophrenia is a mental disorder that will progressively change a person's mental state and cause serious social problems. Symptoms of schizophrenia are highly correlated to emotional status, especially depression. We are thus motivated to design a mental status detection system for schizophrenia patients in order to provide an assessment tool for mental health professionals.

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Objective: Appropriate triage in patients presenting to the emergency department (ED) is often challenging. Little is known about the role of physician gestalt in ED triage. We aimed to compare the accuracy of emergency physician gestalt against the currently used computerized triage process.

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In this article, we propose a new 3-D maneuver controller for a class of nonlinear multiagent systems (MASs) with nonholonomic constraint and saturated control. The system is designed under a distributed communication topology and the controller is more flexible and efficient for general formation maneuver tasks. The saturation design generates control inputs within pregiven bounds, which makes the system more applicable in practice.

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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 detection of clinical high-risks.

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Background: Alzheimer 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 treatment and care, early detection of the disease at the stage of mild cognitive impairment (MCI) will prevent the rapid progression of dementia.

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Background: Emergency 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 subjective judgment and triages too many patients to emergency severity index level 3 in current practice. Hence, a system that can help clinicians accurately triage a patient's condition is imperative.

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Background: Mild cognitive impairment (MCI), which is common in older adults, is a risk factor for dementia. Rapidly growing health care demand associated with global population aging has spurred the development of new digital tools for the assessment of cognitive performance in older adults.

Objective: To overcome methodological drawbacks of previous studies (e.

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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 to internal thoughts that are seen to be detrimental to attentional control, could be replicated by using a robotic experimenter for older adults who are not as familiar with online technologies. A total of 28 younger and 22 older adults performed a Sustained Attention to Response Task (SART) by answering thought probes regarding their attention states and providing confidence ratings for their own task performances.

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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 the literature applied neural networks directly together with several data pre-processing techniques, we proposed in this paper a screening system that is to perform classification based on automatic processing of the transcripts of speeches from the subjects undertaking a neuropsychological test.

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Federated learning (FL) is a method used for training artificial intelligence models with data from multiple sources while maintaining data anonymity, thus removing many barriers to data sharing. Here we used data from 20 institutes across the globe to train a FL model, called EXAM (electronic medical record (EMR) chest X-ray AI model), that predicts the future oxygen requirements of symptomatic patients with COVID-19 using inputs of vital signs, laboratory data and chest X-rays. EXAM achieved an average area under the curve (AUC) >0.

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'Federated Learning' (FL) is a method to train Artificial Intelligence (AI) models with data from multiple sources while maintaining anonymity of the data thus removing many barriers to data sharing. During the SARS-COV-2 pandemic, 20 institutes collaborated on a healthcare FL study to predict future oxygen requirements of infected patients using inputs of vital signs, laboratory data, and chest x-rays, constituting the "EXAM" (EMR CXR AI Model) model. EXAM achieved an average Area Under the Curve (AUC) of over 0.

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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 training. However, traditional NDT training is very labor intensive and time consuming for therapists, thus, stroke patients usually cannot receive sufficient rehabilitation training.

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Alzheimer disease and other dementias have become the 7th cause of death worldwide. Still lacking a cure, an early detection of the disease in order to provide the best intervention is crucial. To develop an assessment system for the general public, speech analysis is the optimal solution since it reflects the speaker's cognitive skills abundantly and data collection is relatively inexpensive compared with brain imaging, blood testing, etc.

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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 improvement of shoulder function.

Methods: In this randomized, single-blind controlled clinical trial, 21 subjects with FSS were randomly assigned to two groups: a true electroacupuncture group (TEAG) and a sham electroacupuncture group (SEAG).

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This paper presents an assistive control system with a special kinematic structure of an upper limb rehabilitation robot embedded with force/torque sensors. A dynamic human model integrated with sensing torque is used to simulate human interaction under three rehabilitation modes: active mode, assistive mode, and passive mode. The hereby proposed rehabilitation robot, called NTUH-ARM, provides 7 degree-of- freedom (DOF) motion and runs subject to an inherent mapping between the 7 DOFs of the robot arm and the 4 DOFs of the human arm.

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