Acupoint selection based on pattern identification results or disease state

Discovering the key symptoms for identifying patterns in functional dyspepsia patients: Doctor's decision and machine learning

Background

Pattern identification is a crucial diagnostic process in Traditional East Asian Medicine, classifying patients with similar symptom patterns. This study aims to identify key symptoms for distinguishing patterns in patients with functional dyspepsia (FD) using explicit (doctor's decision-based) and implicit (computational model-based) approaches.

Methods

Data from twenty-one FD patients were collected from local clinics of traditional Korean Medicine and provided to three doctors in a standardized format. Each doctor identified patterns among three types: spleen-stomach weakness, spleen deficiency with qi stagnation/liver-stomach disharmony, and food retention. Doctors evaluated the importance of the symptoms indicated by items in the Standard Tool for Pattern Identification of Functional Dyspepsia questionnaire. Explicit importance was determined through doctors' survey by general evaluation and by selecting specific information used for the diagnosis of patient cases. Implicit importance was assessed by feature importance from the random forest classification models, which classify three types for general differentiation and perform binary classification for specific types.

Results

Key symptoms for distinguishing FD patterns were identified using two approaches. Explicit importance highlighted dietary and nausea-related symptoms, while implicit importance identified complexion or chest tightness as generally crucial. Specific symptoms important for particular pattern types were also identified, and significant correlation between implicit and explicit importance scores was observed for types 1 and 3.

Conclusion

This study showed important clinical information for differentiating FD patients using real patient data. Our findings suggest that these approaches can contribute to developing tools for pattern identification with enhanced accuracy and reliability.

Exploring Acupuncture Actions in the Body and Brain

Acupuncture's actions have been explained by biomedical research. However, the meridian system used in acupuncture needs further clarification. This review describes how acupuncture affects the body and brain. From the perspective of traditional East Asian medicine, the meridian system is closely connected with acupuncture's treatment effects. In the body, the indications of acupoints, primarily established based on the meridian system, have spatial symptom patterns. Spatial patterns of acupoint indications are distant from the stimulated sites and strongly associated with the corresponding meridian's route. Understanding how acupuncture works based on the original meridian system is important. From a neuroscience perspective, an acupuncture-induced sensation originates from the bottom-up action of simple needling in the peripheral receptor and the reciprocal interaction with top-down brain modulation. In the brain, enhanced bodily attention triggered by acupuncture stimulation can activate the salience network and deactivate the default mode network regardless of the actual stimulation. The application of data science technology to acupuncture research may provide new tools to uncover the principles of acupoint selection and enhance the clinical efficacy of acupuncture treatment in various diseases.

Traditional East Asian Herbal Medicine Treatment for Alzheimer's Disease: A Systematic Review and Meta-Analysis

Alzheimer's disease (AD) is a leading progressive neurodegenerative disease worldwide, and its treatment is a challenging clinical problem. This review was conducted to evaluate the efficacy and safety of herbal medicine for AD treatment. The PubMed, CENTRAL, EMBASE, CNKI, OASIS, KTKP, and CiNii databases were searched until June 2020 for randomized controlled trials (RCTs) on herbal medicine for AD, and a meta-analysis of 57 RCTs was conducted. For cognitive function, herbal medicine significantly improved the Mini-Mental State Examination (MMSE) and AD Assessment Scale-Cognitive Subscale (ADAS-cog) scores compared with conventional medicine. The MMSE scores showed no significant difference between the groups treated with herbal medicine and donepezil; however, herbal medicine significantly lowered the ADAS-cog score. Acori Graminei Rhizoma-containing and Cnidii Rhizoma-containing herbal medicine significantly improved the MMSE and ADAS-cog scores compared with conventional medicine. Ginseng Radix-containing herbal medicine showed a positive, but not statistically significant, tendency toward improving the MMSE score compared with conventional medicine. Herbal medicine with conventional medicine significantly improved the MMSE, ADAS-cog, and Montreal Cognitive Assessment (MoCA) scores compared with conventional medicine, and herbal medicine with donepezil also significantly improved these scores compared with donepezil. Acori Graminei Rhizoma or Cnidii Rhizoma-containing herbal medicine with conventional medicine significantly improved the MMSE and ADAS-cog scores compared with conventional medicine. Ginseng Radix-containing herbal medicine + conventional medicine significantly improved the MMSE score, but not the ADAS-cog score, compared with conventional medicine. For behavioral and psychological symptoms of dementia, the Neuropsychiatry Inventory (NPI) score was not significantly different between herbal and conventional medicines. Herbal medicine with conventional medicine significantly improved the NPI and Behavioral Pathology in Alzheimer's Disease Rating Scale scores compared with conventional medicine. The NPI score showed no significant difference between the groups treated with herbal medicine and placebo. Furthermore, herbal medicine with conventional medicine significantly lowered plasma amyloid beta levels compared with conventional medicine alone. Herbal medicine, whether used alone or as an adjuvant, may have beneficial effects on AD treatment. However, owing to the methodological limitations and high heterogeneity of the included studies, concrete conclusions cannot be made.

Interpretation of acupoint location in traditional Chinese medicine teaching: Implications for acupuncture in research and clinical practice

Acupuncture is a therapeutic intervention of traditional Chinese medicine, characterized by the insertion of a fine metal needle through the human body's skin at an acupuncture point (acupoint). Acupuncture has become from cultural curiosity to fast-growing complementary and alternative medicine therapy worldwide, including in the United States. The ability to locate acupoints on the body surface precisely is critical for the acupuncture treatment. However, the location of acupoints varies frequently among practitioners. Therefore, in the present study, the author will address the application of acupoint in traditional Chinese medicine (including the anatomical location of acupoint) to draw attention to the issues about the acupoint location in acupuncture research and clinical practice. Moreover, further studies are needed to determine whether acupoint location with specificity could be validated by employing reliable tools. In the future, the incongruity among acupuncturists regarding acupoint location should be resolved, and the acupoint location methods with more accuracy should be developed.

Identification of Acupoint Indication from Reverse Inference: Data Mining of Randomized Controlled Clinical Trials

The specificity of acupoint indication (i.e., reverse inference—diseases for which an acupoint could be used) might differ from the specificity of acupoint selection (i.e., forward inference—acupoints used for a disease). In this study, we explore acupoint specificity through reverse inferences from the dataset of prescribed acupoints for a certain disease in clinical trials. We searched acupuncture treatment regimens in randomized controlled trials included in the Cochrane Database of Systematic Reviews. For forward inference, the acupoints prescribed for each disease were quantified. For reverse inference, diseases for each acupoint were quantified. Data were normalized using Z-scores. Bayes factor correction was performed to adjust for the prior probability of diseases. The specificities of acupoint selections in 30 diseases were determined using forward inference. The specificities of acupoint indications regarding 49 acupoints were identified using reverse inference and then subjected to Bayes factor correction. Two types of acupoint indications were identified for 24 acupoints: regional and distal. Our approach suggests that the specificity of acupoint indication can be inferred from clinical data using reverse inference. Acupoint indication will improve our understanding of acupoint specificity and will lead to the establishment of a new model of analysis and educational resources for acupoint characteristics.

Commonality and Specificity of Acupuncture Point Selections

OBJECTIVE

Because individual acupoints have a wide variety of indications, it is difficult to accurately identify the associations between acupoints and specific diseases. Thus, the present study aimed at revealing the commonality and specificity of acupoint selections using virtual medical diagnoses based on several cases.

METHODS

Eighty currently practicing Korean Medicine doctors were asked to prescribe acupoints for virtual acupuncture treatment after being presented with medical information extracted from 10 case reports. The acupoints prescribed for each case were quantified; the data were normalised and compared among the 10 cases using z-scores. A hierarchical cluster analysis was conducted to categorise diseases treated based on the acupoint prescription patterns. Additionally, network analyses were performed on the acupoint prescriptions, at the individual case and cluster level.

RESULTS

Acupoints ST36, LI4, and LR3 were most commonly prescribed across all diseases. Regarding the specific acupoints prescribed in each cluster, acupoints around the disease site (knee and lower back) were frequently used in cluster A (musculoskeletal symptoms), acupoints LI4, LR3, PC6, and KI3 were frequently used in cluster B (psychiatric symptoms), and acupoints ST36, LI4, LR3, PC6, CV12, and SP6 were frequently used in cluster C (several symptoms of diseases of internal medicine).

CONCLUSIONS

The present study identified the commonality and specificity of acupoint selections based on virtual acupuncture treatments prescribed by practicing clinicians. Acupoint selection patterns, which were defined using a top-down approach in previous studies and classical medical texts, may be further elucidated using a bottom-up approach based on patient medical records.