Complementary and alternative therapies in multiple sclerosis: a systematic literature classification and analysis.

INTRODUCTION AND AIM: Multiple Sclerosis (MS) is a disease determined by inflammatory demyelination and neurodegeneration in the Central Nervous System (CNS). Despite the extensive utilization of Complementary and Alternative Medicine (CAM) in MS, there is a need to have comprehensive evidence regarding their application in the management of MS symptoms. This manuscript is a Systematic Literature Review and classification (SLR) of CAM therapies for the management of MS symptoms based on the International Classification of Functioning Disability and Health (ICF) model. METHOD: Studies published between 1990 and 2020 IN PubMed, Science Direct, Scopus, Pro-Quest, and Google Scholar using CAM therapies for the management of MS symptoms were analyzed. RESULTS: Thirty-one papers on the subject were analyzed and classified. The findings of this review clearly show that mindfulness, yoga, and reflexology were frequently used for managing MS symptoms. Moreover, most of the papers used mindfulness and yoga as a CAM therapy for the management of MS symptoms, which mostly devoted to mental functions such as fatigue, depression, cognition, neuromuscular functions such as gait, muscle strength, and spasticity, and sensory function such as balance, in addition to, reflexology is vastly used to management of mental functions of MS patients. CONCLUSION: Evidence suggested that CAM therapies in patients with MS have the potential to target and enhancement numerous elements outlined in the ICF model. Although the use of CAM therapies in MS symptom management is promising, there is a need for strict clinical trials. Future research direction should concentrate on methodologically powerful studies to find out the potential efficacy of CAM intervention.

The necessity to develop a national classification system for Iranian traditional medicine.

BACKGROUND: Classification of disease and interventions in traditional medicine (TM) is necessary for standardised coding of information. Currently, in Iran, there is no standard electronic classification system for disease and interventions in TM. OBJECTIVE: The current study aimed to develop a national framework for the classification of disease and intervention in Persian medicine based on expert opinion. METHOD: A descriptive cross-sectional study was carried out in 2018. The existing systems for the classification of disease and interventions in TM were reviewed in detail, and some of the structural and content characteristics were extracted for the development of the classification of Iranian traditional medicine. Based on these features, a self-administered questionnaire was developed. Study participants (25) were experts in the field of Persian medicine and health information management in Tehran medical universities. RESULTS: Main axes for the classification of disease and interventions were determined. The most important applications of the classification system were related to clinical coding, policymaking, reporting of mortality and morbidity data, cost analysis and determining the quality indicators. Half of the participants (50%) stated that the classification system should be designed by maintaining the main axis of the World Health Organization classification system and changing the subgroups if necessary. A computer-assisted coding system for TM was proposed for the current study. CONCLUSION: Development of this classification system will provide nationally comparable data that can be widely used by governments, national organisations and academic researchers.

A systematic literature review and classification of knowledge discovery in traditional medicine.

INTRODUCTION AND OBJECTIVE: Despite the importance of machine learning methods application in traditional medicine there is a no systematic literature review and a classification for this field. This is the first comprehensive literature review of the application of data mining methods in traditional medicine. METHOD: We reviewed 5 database between 2000 to 2017 based on the Kitchenham systematic review methodology. 502 articles were identified and reviewed for their relevance to application of machine learning methods in traditional medicine, 42 selected papers were classified and categorized on four dimension; 1) application domain of data mining techniques in traditional medicine; 2) the data mining methods most frequently used in traditional medicine; 3) main strength and limitation of data mining techniques in traditional medicine; 4) the performance evaluation methods in data mining methods in traditional medicine. RESULT: The result obtained showed that main application domain of data mining techniques in traditional medicine was related to syndrome differentiation. Bayesian Networks (BNs), Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs) were recognized as being the methods most frequently applied in traditional medicine. Furthermore, each data mining techniques has its own strength and limitations when applied in traditional medicine. Single scaler methods were frequently used for performance evaluation of data mining methods. CONCLUSION: Machine learning methods have become an important research field in traditional medicine. Our research provides information about this methods by examining the related articles.