Explanatory Model of Mental Illness from the Perspective of Traditional Chinese Medicine (Preprint)

2021 ◽  
Author(s):  
Wan-Ling Lin ◽  
Yu-Chi Liang ◽  
Kuo-Hsuan Chung ◽  
Ping-Ho Chen ◽  
Yung-Chun Chang

UNSTRUCTURED The improvement of accurate management of mental illness has become an increasing concern in recent decades. Efforts to understand mental illness have progressed from treating the mind as an isolated system to involving both mind and body as an interactive response. This study attempts to express and ontologize the relationships between different mental illnesses and physical organ systems from the perspective of Traditional Chinese Medicine. In this paper, Natural Language Processing method was introduced to quantify the importance of different mental illness descriptions relative to the five Viscera and two bowels, Stomach and gallbladder through the classical medical text Huangdi Neijing and construct a mental illness network based on the TCM classic text. The results demonstrate that our proposed framework which integrates natural language processing and data visualization can enable clinicians to arrive at more comprehensive insights into mental health. According to the results of the correlation analysis for mental illnesses, viscera, and symptoms, the organs most affected by mental illness is the Heart, and the most two important factors to cause mental illness are Anger and Worry &Think. Moreover, the current findings promote the present comprehension of the association between the mind and body from the view of Traditional Chinese Medicine. We found the mental illness described in Traditional Chinese Medicine is always related to more than one organ.

2021 ◽  
Vol 33 (S1) ◽  
pp. 1-1
Author(s):  
Ellen Lee ◽  
Helmet Karim ◽  
Ipsit Vahia ◽  
Andrea Iaboni

SynopsisWith the rise of wearable sensors, advancement in comprehensible artificial intelligence (AI) algorithms, and growing acceptance of AI in medicine, AI has great potential to more reliably diagnose, prognose, and treat mental illnesses. The rapidly rising number of older adults worldwide presents a unique challenge for clinicians due to increased mental health needs in the setting of a dwindling clinical workforce. AI has enabled researchers to better understand mental illnesses by taking advantage of ‘big data.’This symposium will present an overview of novel research leveraging AI (machine learning, natural language processing) to better track, understand, and support mental health and cognitive functioning in older adults.Helmet Karim, PhD will present on prediction of treatment response in late-life major depressive disorder and the implications of those models.Ellen Lee, MD will present on using natural language processing to understand psychosocial functioning in older adults.Ipsit Vahia, MD will present on radio-based sensors to phenotype changes in behavior patterns that may correlate with a range of geropsychiatric symptoms.Andrea Iaboni, MD DPhil FRCPC will present on multimodal wearable and vision-based sensors for the detection and categorization of behavioural symptoms of dementia.The symposium includes three physician-scientists (Iaboni, Lee, Vahia), two women (Iaboni, Lee), and two early career faculty (Lee, Karim – co-chairs). The symposium represents four different institutions across the country (McLean/Harvard, Toronto Rehabilitation Institute/University of Toronto, UC San Diego, University of Pittsburgh) and four very different approaches using AI technology to improve understanding and outcomes in the field of geriatric mental health.The symposium seeks to address the underutilization of AI in psychiatric research, especially in the field of aging research. The increased individual-level heterogeneity associated with aging; complex trajectories of decline in cognitive, mental, and physical health; and lack and slow adoption of older adult-centered technologies present great challenges to advancing the field. However, advances in the field of explainable AI and transdisciplinary development of AI approaches can address the unique challenges of aging research.


2012 ◽  
Vol 2012 ◽  
pp. 1-17 ◽  
Author(s):  
Junghee Yoo ◽  
Euiju Lee ◽  
Chungmi Kim ◽  
Junhee Lee ◽  
Lao Lixing

Sasang constitutional medicine (SCM) is a holistic typological constitution medicine which balances psychological, social, and physical aspects of an individual to achieve wellness and increase longevity. SCM has the qualities of preventative medicine, as it emphasizes daily health management based on constitutionally differentiated regimens and self-cultivation of the mind and body. This review's goal is to establish a fundamental understanding of SCM and to provide a foundation for further study. It compares the similarities and differences of philosophical origins, perspectives on the mind (heart), typological systems, pathology, and therapeutics between SCM and traditional Chinese medicine (TCM). TCM is based on the Taoist view of the universe and humanity. The health and longevity of an individual depends on a harmonious relationship with the universe. On the other hand, SCM is based on the Confucian view of the universe and humanity. SCM focuses on the influence of human affairs on the psyche, physiology, and pathology.


1989 ◽  
Vol 33 (19) ◽  
pp. 1334-1338
Author(s):  
Joseph Psotka

Advanced technologies, including artificial intelligence (Al), hypertext, and natural language processing (NLP), are transforming the Mind/Machine Interface. This presentation focuses on two large development projects underway that use these technologies in unique ways. Their use is guided by the three natural means of communication between people: saying, coaching, and showing; as metaphors for using advanced technology interfaces. The two projects are aimed at developing job and training aids for the Army. The most complete example is the Maintenance Aid Computer for HAWK–Intelligent Institutional Instructor (MACH-III). This is the largest and most successful implementation of an ITS to date (Psotka, Massey, and Mutter, 1988). MACH-III was developed by Bolt, Beranek, and Newman (BBN), to provide training in organizational maintenance of the main radar of the HAWK air defense guided missile system. Its core is a huge qualitative simulation of the radar. The complexity of the simulation and the troubleshooting problem space demand a unique hypertext interface, whose structure and function are only beginning to be understood. Some preliminary evaluation results from the U.S. Army Air Defense Artillery School (USAADASCH), Ft. Bliss, Texas are beginning to show its effectiveness. The other project, Building Robust Dual Grammar Exercisers (BRIDGE), will begin to explore the architextual structure of hypertext systems within the context of advanced technologies for military machine translation and military foreign language training. From this perspective, hypertext is a bridging technology that links the existing strengths of qualitative simulations with the future power of natural language processing.


2022 ◽  
pp. 1-3
Author(s):  
Neguine Rezaii ◽  
Phillip Wolff ◽  
Bruce H. Price

A person's everyday language can indicate patterns of thought and emotion predictive of mental illness. Here, we discuss how natural language processing methods can be used to extract indicators of mental health from language to help address long-standing problems in psychiatry, along with the potential hazards of this new technology.


2020 ◽  
pp. 3-17
Author(s):  
Peter Nabende

Natural Language Processing for under-resourced languages is now a mainstream research area. However, there are limited studies on Natural Language Processing applications for many indigenous East African languages. As a contribution to covering the current gap of knowledge, this paper focuses on evaluating the application of well-established machine translation methods for one heavily under-resourced indigenous East African language called Lumasaaba. Specifically, we review the most common machine translation methods in the context of Lumasaaba including both rule-based and data-driven methods. Then we apply a state of the art data-driven machine translation method to learn models for automating translation between Lumasaaba and English using a very limited data set of parallel sentences. Automatic evaluation results show that a transformer-based Neural Machine Translation model architecture leads to consistently better BLEU scores than the recurrent neural network-based models. Moreover, the automatically generated translations can be comprehended to a reasonable extent and are usually associated with the source language input.


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