Sequential Condition Evolved Interaction Knowledge Graph for Traditional Chinese Medicine Recommendation

Traditional Chinese Medicine (TCM) has a rich history of utilizing natural herbs to treat a diversity of illnesses. In practice, TCM diagnosis and treatment are highly personalized and organically holistic, requiring comprehensive consideration of the patient's state and symptoms over time. However, existing TCM recommendation approaches overlook the changes in patient status and only explore potential patterns between symptoms and prescriptions. In this paper, we propose a novel Sequential Condition Evolved Interaction Knowledge Graph (SCEIKG), a framework that treats the model as a sequential prescription-making problem by considering the dynamics of the patient's condition across multiple visits. In addition, we incorporate an interaction knowledge graph to enhance the accuracy of recommendations by considering the interactions between different herbs and the patient's condition. Experimental results on a real-world dataset demonstrate that our approach outperforms existing TCM recommendation methods, achieving state-of-the-art performance

Advances in Patient Classification for Traditional Chinese Medicine: A Machine Learning Perspective

As a complementary and alternative medicine in medical field, traditional Chinese medicine (TCM) has drawn great attention in the domestic field and overseas. In practice, TCM provides a quite distinct methodology to patient diagnosis and treatment compared to western medicine (WM). Syndrome (ZHENG or pattern) is differentiated by a set of symptoms and signs examined from an individual by four main diagnostic methods: inspection, auscultation and olfaction, interrogation, and palpation which reflects the pathological and physiological changes of disease occurrence and development. Patient classification is to divide patients into several classes based on different criteria. In this paper, from the machine learning perspective, a survey on patient classification issue will be summarized on three major aspects of TCM: sign classification, syndrome differentiation, and disease classification. With the consideration of different diagnostic data analyzed by different computational methods, we present the overview for four subfields of TCM diagnosis, respectively. For each subfield, we design a rectangular reference list with applications in the horizontal direction and machine learning algorithms in the longitudinal direction. According to the current development of objective TCM diagnosis for patient classification, a discussion of the research issues around machine learning techniques with applications to TCM diagnosis is given to facilitate the further research for TCM patient classification

Teaching Undergraduate Students about Cultural and Linguistic Diversity: Assessment and Pedagogical Challenges

Purpose: Diverse undergraduate students can play a critical role in increasing the number of culturally competent clinicians in the future. However, exploring how these students develop cultural and linguistic awareness is crucial. This study examined the development and assessment of cultural and linguistic awareness among a diverse group of undergraduate students who completed a dedicated course on cultural and linguistic diversity in communication disorders. Method: We conducted quantitative and qualitative analyses to evaluate student growth. Ninety-seven undergraduate students from a public Hispanic-Serving Institution completed an adaptation of the ASHA\u27s Cultural Competence Checklist: Personal Reflection at the beginning and end of a 16-week dedicated course. We analyzed the item responses using a paired t-test, and factor analyses were run to explore the potential presence of underlying constructs. We also analyzed open-ended students\u27 reflections at the end of the semester. Results: Students exhibited significant gains in cultural awareness. The exploratory factor analyses of the Personal Reflection responses at the beginning and end of the semester resulted in similar percentages of explained variance but by different item groupings. Students\u27 reflections converged into two broad categories: (1) topics related to the course content and (2) student comments reflecting internal processes. Conclusions: A dedicated course with relevant content may positively influence growth in cultural awareness in diverse undergraduate students. We discuss pedagogical challenges and potential mitigating approaches to develop and evaluate cultural awareness. Overall, our study offers insights into the development of cultural understanding among diverse undergraduate students and provides actionable recommendations for promoting a more inclusive and culturally responsive learning environment

Do psychiatric diagnoses explain?:A philosophical investigation

This thesis is a philosophical examination of the explanatory roles of diagnoses in psychiatry. In medicine, diagnoses normally serve as causal explanations of patients’ symptoms. Given that psychiatry is a discipline whose practice is shaped by medical traditions, it is often implied that its diagnoses also serve such explanatory functions. This is evident in clinical texts that portray psychiatric diagnoses as referring to diseases that cause symptoms. However, there are problems which cast doubt on whether such portrayals are justified. I address these problems and examine whether psychiatric diagnoses provide explanations of symptoms. The first problem is conceptual. In diagnostic manuals, psychiatric diagnoses are defined by their symptoms. This suggests that invoking them as explanations of the symptoms amounts to circularity. I argue that this can be resolved with an appropriate conceptual framework that captures the complex semantic values of diagnostic terms and their different uses in clinical discourse. I put forward such a framework based on two-dimensional semantics. The second problem is ontological. Empirical research suggests that diagnostic categories in psychiatry do not correspond to invariant causal types, but are associated with variable combinations of diverse causes that interact across biological, psychological, and social levels. Given this heterogeneity, I argue that psychiatric diagnoses fall short of paradigmatic cases of causal explanation, but that some can still provide other sorts of useful causal explanatory information. The original contribution of this thesis is the illumination of the conceptual relations between diagnoses and symptoms. This philosophical work is important, because it can be brought to valuable application in modifying psychiatric practice

Themes in cultural psychiatry, an annotated bibliography, 1975-1980

Includes bibliographical references and index.While expanding on the previous compilation, Anthropological and Cross-Cultural Themes in Mental Health: An Annotated Bibliography, 1925-1974, Favazza anthologizes the next five years of literature on cultural psychiatry. The magnitude of material during this time period allowed Favazza to broaden the scope from cultural psychiatric themes in psychiatric and psychological journals to also include anthropological journals, non-English-language journals, and books as well.Introduction -- Journals cited in annotations -- Annotations -- Secondary author index -- Subject index.Digitized at the University of Missouri--Columbia MU Libraries Digitization Lab in 2012. Digitized at 600 dpi with Zeutschel, OS 15000 scanner. Access copy, available in MOspace, is 400 dpi, grayscale

Exploring the obesogenic environment: understanding the health impact of contemporary living.

Obesity exists in the complexity of everyday life and arises from individuals' interactions with the obesogenic environment, different behaviours/dispositions and biological factors. In order to develop better intervention strategies to attenuate obesity prevalence, this research applied an ontological approach to investigating some of the factors and/or underlying preconditions for obesity to occur. Previous research has taken an epistemological approach to the study of obesity and used siloed approaches, which may have assumed knowing what the cause of obesity was, or that its findings were the cause(s) for obesity. In contrast, an ontological approach asks the question of 'what the world or reality must be like for obesity to occur'. Therefore, the aim of this study was to explore the multiple, interrelated processes with respect to individuals' behaviour, attitudes and dispositions towards food, self and life. Due to the fact that obesity arises from complex origins, the study required a methodology that would account for complexity. Critical realism (CR) was used to explore causal or generative mechanisms (i.e. multiple and interrelated factors) that may be involved and/or contribute to obesity. A mixture of qualitative and quantitative methods - in the form of semi-structured interviews (SSi) and validated questionnaires - were used to explore how different individuals of various body weights relate to food, self-perceived body image and self-esteem/confidence and orientation to life. CR's modes of inference - namely abduction and retroduction - were then applied in order to understand the underlying preconditions of what reality must be like for obesity to occur. The research additionally attempted to identify demi-regularities (i.e. semi-predictable patterns) among individuals' behaviours/attitudes and/or dispositions towards food, self and life, and also to identify transfactual (i.e. necessary) conditions for obesity to be what it is. The findings from the first part of this study, carried out on a convenience sample of participants, served as a framework for the second part, which focused on individuals 20-40 years old. In the second study, full body scans, anthropometric measurements, body-fat percent and blood samples were collected in order to support theoretical suppositions and the findings from the first study's SSi and questionnaires. The findings from the combined studies showed that individuals with an overall negative embodied disposition towards food (i.e. believing that food is unimportant) experienced the following (instrumental profile): a dissonant relationship with food (i.e. more food-dependent because of stress and/or negative emotions); a more negative sense of self-perception in terms of body image and self-esteem or confidence; a lower salutogenic outlook; lower physical and mental wellbeing, including a higher body-fat percentage and higher levels of proinflammatory biomarkers. In contrast, the studies also showed that individuals with an overall positive embodied disposition towards food (i.e. believing that food is important) experienced the following (aesthetic and, to some extent, disciplined profile): a less dissonant (or entirely non-dissonant) relationship with food; stronger salutogenesis; higher physical and mental wellbeing, including a lower body-fat percentage and lower levels of proinflammatory biomarkers. This study gives insights into how human behaviour and disposition - towards food, self and life - links to overall wellbeing, body-fat and bio-chemical profile. The findings have provided a new way of understanding and thinking about the complexity of obesity, and laid a new path or framework for carrying out further research and studying obesity. Moreover, this research has suggested that intervention strategies must employ a multi-dimensional approach in order to attenuate obesity prevalence, crossing different disciplines - from the natural to the psycho-social sciences. These intervention strategies must also consider a more targeted approach (stratified interventions) for individuals in function of their embodied dispositions: instrumental, discipline or aesthetic

Intelligent Systems for Sustainable Person-Centered Healthcare

This open access book establishes a dialog among the medical and intelligent system domains for igniting transition toward a sustainable and cost-effective healthcare. The Person-Centered Care (PCC) positions a person in the center of a healthcare system, instead of defining a patient as a set of diagnoses and treatment episodes. The PCC-based conceptual background triggers enhanced application of Artificial Intelligence, as it dissolves the limits of processing traditional medical data records, clinical tests and surveys. Enhanced knowledge for diagnosing, treatment and rehabilitation is captured and utilized by inclusion of data sources characterizing personal lifestyle, and health literacy, and it involves insights derived from smart ambience and wearables data, community networks, and the caregivers’ feedback. The book discusses intelligent systems and their applications for healthcare data analysis, decision making and process design tasks. The measurement systems and efficiency evaluation models analyze ability of intelligent healthcare system to monitor person health and improving quality of life