A5042076430- Mental Health Research Topics
- Digital Mental Health Interventions
- Dementia and Cognitive Impairment Research
- Psychosomatic Disorders and Their Treatments
- Schizophrenia research and treatment
- Anxiety, Depression, Psychometrics, Treatment, Cognitive Processes
- Treatment of Major Depression
- Bipolar Disorder and Treatment
- Fibromyalgia and Chronic Fatigue Syndrome Research
- Neurobiology of Language and Bilingualism
- Face recognition and analysis
- Machine Learning in Healthcare
- Interpreting and Communication in Healthcare
- Aging, Elder Care, and Social Issues
- Transcranial Magnetic Stimulation Studies
- Alzheimer's disease research and treatments
- Tryptophan and brain disorders
- Cholinesterase and Neurodegenerative Diseases
- Functional Brain Connectivity Studies
- Medication Adherence and Compliance
- Social Robot Interaction and HRI
- Stuttering Research and Treatment
- Neuroinflammation and Neurodegeneration Mechanisms
- Artificial Intelligence in Healthcare and Education
- AI in Service Interactions
Keio University
2017-2025
Keio University Hospital
2019-2023
Innovation Center of NanoMedicine
2022
ObjectiveWe aimed to develop a machine learning algorithm screen for depression and assess severity based on data from wearable devices.MethodsWe used device that calculates steps, energy expenditure, body movement, sleep time, heart rate, skin temperature, ultraviolet light exposure. Depressed patients healthy volunteers wore the continuously study period. The modalities were compared hourly between volunteers. XGBoost was build models 10-fold cross-validation applied...
The development of a cost-effective digital biomarker for detecting dementia is highly needed. While numerous studies have explored detection through speech and natural language analysis, only few focused on using face video recordings, more in-depth research In this paper, we propose method mild cognitive impairment (MCI), pre-dementia stage, by utilizing four types facial expression features extracted from recorded videos participants. These include Action Units, emotion categories,...
Abstract In recent years, studies on the use of natural language processing (NLP) approaches to identify dementia have been reported. Most these used picture description tasks or other similar encourage spontaneous speech, but free conversation without requiring a task might be easier perform in clinical setting. Moreover, is unlikely induce learning effect. Therefore, purpose this study was develop machine model discriminate subjects with and by extracting features from unstructured data...
Alzheimer's disease (AD) is a type of dementia that more likely to occur as people age. It currently has no known cure. As the world's population aging quickly, early screening for AD become increasingly important. Traditional methods such brain scans or psychiatric tests are stressful and costly. The patients feel reluctant screenings fail receive timely intervention. While researchers have been exploring use language in detection, less attention given face-related features. paper focuses...
Depressive and neurocognitive disorders are debilitating conditions that account for the leading causes of years lived with disability worldwide. However, there no biomarkers objective or easy-to-obtain in daily clinical practice, which leads to difficulties assessing treatment response developing new drugs. New technology allows quantification features clinicians perceive as reflective disorder severity, such facial expressions, phonic/speech information, body motion, activity, sleep. Major...
Mood disorders have long been known to affect motor function. While methods objectively assess such symptoms used in experiments, those same not yet applied clinical practice because the are time-consuming, labor-intensive, or invasive.
Few biomarkers can be used clinically to diagnose and assess the severity of depression. However, a decrease in activity sleep efficiency observed depressed patients, recent technological developments have made it possible measure these changes. In addition, physiological changes, such as heart rate variability, distinguish patients from normal persons; parameters improve diagnostic accuracy. The proposed research will explore construct machine learning models capable detecting depressive...
Psychiatric disorders are diagnosed through observations of psychiatrists according to diagnostic criteria such as the DSM-5. Such observations, however, mainly based on each psychiatrist's level experience and often lack objectivity, potentially leading disagreements among psychiatrists. In contrast, specific linguistic features can be observed in some psychiatric disorders, a loosening associations schizophrenia. Some studies explored biomarkers, but biomarkers have yet used clinical...
Abstract Background Depressive and neurocognitive disorders are debilitating conditions that account for the leading causes of years lived with disability worldwide. Overcoming these is an extremely important public health problem today. However, there no biomarkers objective or easy-to-obtain in daily clinical practice, which leads to difficulties assessing treatment response developing new drugs. Due advances technology, it has become possible quantify features clinicians perceive as...
The authors applied natural language processing and machine learning to explore the disease-related patterns that warrant objective measures for assessing ability in Japanese patients with Alzheimer disease (AD), while most previous studies have used large publicly available data sets Euro-American languages.The obtained 276 speech samples from 42 AD 52 healthy controls, aged 50 years or older. A library Python was used, spaCy, an add-on library, GiNZA, which is a parser based on Universal...
Exposure therapy is a mainstream of treatment for social anxiety disorder (SAD). However, effort and time are required to recreate interpersonal situations that produce moderate anxiety. On the other hand, virtual reality exposure can easily control anxiety-inducing conditions allow graduated exposure. artificial intelligence animations speak as naturally actual humans not yet practical, adding limitations these treatments. The authors propose use technology transform facial expressions into...
Some clinical subtypes of dementias with posterior lobar degeneration have been described as atypical Alzheimer's disease (AD). Patients cortical atrophy (PCA), logopenic progressive aphasia (LPA), or primary apraxia frequently AD pathologies. However, not every patient fall within the range these subtypes. Case report neuropsychological and neuroimaging data. A 73 years old, right handed female 12 education, having no previous medical psychiatric problems, began to experience agraphia Kanji...
ABSTRACT Introduction Few biomarkers can be clinically used to diagnose and assess the severity of depression. However, a decrease in activity sleep efficiency observed depressed patients, recent technological developments have made it possible measure these changes. In addition, physiological changes, such as heart rate variability, distinguish patients from normal persons; parameters improve diagnostic accuracy. The proposed research will explore construct machine learning models capable...
Abstract Introduction Psychiatric disorders are diagnosed according to diagnostic criteria such as the DSM-5 and ICD-11. Basically, psychiatrists extract symptoms make a diagnosis by conversing with patients. However, processes often lack objectivity. In contrast, specific linguistic features can be observed in some psychiatric disorders, loosening of associations schizophrenia. The purposes present study quantify language neurocognitive using natural processing identify that differentiate...