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

Epistemological issues in the theory of Chinese medicine

Traditional Chinese Medicine (TCM) has been criticized for being unscientific because the theory on which it is based involves entities like qi and ’meridians’ that appear ambiguous and because the internal ‘organs’ like the kidney and the spleen are very different from those of modern anatomy and physiology. Even more so, TCM methods of therapy based on the yin-yang principle, the model of the five elements, and the classification of illnesses according to standard constellations of symptoms (TCM “syndromes”) are largely unproven by the protocols of modern evidence-based medicine. This dissertation attempts to reconstruct TCM theory by: (a) providing explanations of TCM entities as abstractions and constructs that relate to observable body functions and illness symptoms and (b) interpreting TCM theory as comprising heuristic models that were constructed from clinical experience to fit empirical observations of illnesses and their treatments with herbal medications and acupuncture. It suggests that scientists should be less concerned with the ontological status of TCM entities and the epistemic credentials of TCM models than with the ability of these concepts and models to guide physicians in therapy. More importantly, it makes the argument that these models are testable using the methods of evidence-based medicine. There are methodological difficulties associated with randomized controlled trials partly because TCM treatments tend to be individualized and syndromes are dynamic in nature; observational trials may be more appropriate in some situations. It is also possible that, for patients who are more culturally attuned to TCM, the placebo effect is strongly at play and may render the real effects of TCM treatments harder to tease out in clinical trials. The dissertation concludes that the main postulates of TCM should be put to rigorous test. The result may be a leaner but more robust theory, with parts that do not stand up to the test being rejected or modified, and a possible acceptance of its more modest therapeutic claims for a limited range of pathological conditions like pain and chronic illnesses

Optimizing Prescription of Chinese Herbal Medicine for Unstable Angina Based on Partially Observable Markov Decision Process

Objective. Initial optimized prescription of Chinese herb medicine for unstable angina (UA). Methods. Based on partially observable Markov decision process model (POMDP), we choose hospitalized patients of 3 syndrome elements, such as qi deficiency, blood stasis, and turbid phlegm for the data mining, analysis, and objective evaluation of the diagnosis and treatment of UA at a deep level in order to optimize the prescription of Chinese herb medicine for UA. Results. The recommended treatment options of UA for qi deficiency, blood stasis, and phlegm syndrome patients were as follows: Milkvetch Root + Tangshen + Indian Bread + Largehead Atractylodes Rhizome (ADR=0.96630); Danshen Root + Chinese Angelica + Safflower + Red Peony Root + Szechwan Lovage Rhizome Orange Fruit (ADR=0.76); Snakegourd Fruit + Longstamen Onion Bulb + Pinellia Tuber + Dried Tangerine peel + Largehead Atractylodes Rhizome + Platycodon Root (ADR=0.658568). Conclusion. This study initially optimized prescriptions for UA based on POMDP, which can be used as a reference for further development of UA prescription in Chinese herb medicine

Identification and Simultaneous Determination of the Main Toxical Pyrrolizidine Alkaloids in a Compound Prescription of Traditional Chinese Medicine: Qianbai Biyan Tablet

Qianbai biyan tablet (QT) is a compound prescription of traditional Chinese medicine which is used to treat nasal congestion, rhinitis, and nasosinusitis, with Senecio scandens as its main plant material. Several pyrrolizidine alkaloids (PAs) were reported in Senecio scandens and others of Senecio species. Although Senecio scandens is assigned as the legal plant material of QT, whether replaced use of it by other Senecio plants can bring toxicity is unknown because of the lack of quantitative data about toxic PAs between different Senecio species. In the present study, adonifoline, senkirkine, and another PA presumed as emiline have been identified in QT; however, there was no senecionine detected in all tablets. PA contents in QTs varied in different companies and different batches. Adonifoline existed only in Senecio scandens, and senecionine was detected in all eight Senecio plants investigated in the present study. Data showed that replaced use of Senecio scandens with a low level of senecionine by other Senecio plants such as Senecio vulgaris containing a high level of senecionine is advertised to be forbidden. Data of the present study may be used as a reference to make new drug quality regularity and recommendation guideline for the safety of QT

Using IS/IT to support the delivery of Chinese Medicine

This study investigated aspects of utilising IS/IT in Chinese Medicine practice in Australia. The research proposed that a more suitable synthesis should be adopted for the developments in this domain. Hence, the Chinese Medicine Inquiring System is a combination of the key concepts of Knowledge Management, Inquiring Systems and IS/IT

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

Deep Learning for Medication Recommendation: A Systematic Survey

ABSTRACTMaking medication prescriptions in response to the patient's diagnosis is a challenging task. The number of pharmaceutical companies, their inventory of medicines, and the recommended dosage confront a doctor with the well-known problem of information and cognitive overload. To assist a medical practitioner in making informed decisions regarding a medical prescription to a patient, researchers have exploited electronic health records (EHRs) in automatically recommending medication. In recent years, medication recommendation using EHRs has been a salient research direction, which has attracted researchers to apply various deep learning (DL) models to the EHRs of patients in recommending prescriptions. Yet, in the absence of a holistic survey article, it needs a lot of effort and time to study these publications in order to understand the current state of research and identify the best-performing models along with the trends and challenges. To fill this research gap, this survey reports on state-of-the-art DL-based medication recommendation methods. It reviews the classification of DL-based medication recommendation (MR) models, compares their performance, and the unavoidable issues they face. It reports on the most common datasets and metrics used in evaluating MR models. The findings of this study have implications for researchers interested in MR models

Investigating the translation of metaphors used in diagnosis and treatment in Chinese medicine classics Neijing and Shanghan Lun

The language used in Traditional Chinese Medicine (TCM) depicts a world of human physiology, pathology, diagnosis and treatment, in which metaphors serve as an essential vehicle for readers to understand fundamental but often abstract concepts in TCM. While previous work has investigated strategies for translating the TCM classics, the metaphors used to describe diagnosis and treatment and their English translations are critical in understanding TCM, and require a more systematic exploration. This study investigates the diagnosis- and treatment-related metaphors selected from two TCM classics, Neijing and Shanghan Lun, and their English renditions by translators from different professional backgrounds. The thesis also focuses on the analysis of the effectiveness of different translation strategies in delivering pertinent health-related information conveyed by the metaphors of the original texts. A multidimensional framework that combines a conceptual approach with linguistic and cultural elements was established to capture the complexity of the metaphors, particularly from the perspective of translation. The linguistic metaphors in this study were first identified from a purpose-built corpus using a CMT-based metaphor identification procedure adapted from Steen (2010). Following the conceptual metaphor inference procedure developed by Steen (2011), various conceptual metaphors were inferred from the linguistic metaphors. Corresponding English translations were also collected to investigate which translation strategies have been used and which strategy can most effectively deliver the health-related information conveyed by the metaphors. Four main strategies were employed in the English translations: 1) equivalent mapping, by which the source domain is retained; 2) using a simile to translate a metaphor; 3) direct narrative equivalence, which abandons the metaphor and narrates the medical knowledge directly; and 4) complemented equivalent translation, whereby the metaphor is explained with additional content. From the perspective of conveying health-related knowledge, equivalent mapping was effective for metaphors universally understood by Chinese and English readers. For culturally specific metaphors, especially when the metaphor relates to an important TCM concept, complemented equivalent translation, which can reconfigure the cognitive context for the reader, was most suitable. For metaphors not related to important concepts, direct narrative equivalence was found to be effective