Big Data Analysis of Traditional Knowledge-based Ayurveda Medicine

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Abstract

Introduction:

Modern medicine has embraced data-driven understanding of health, principally through electronic medical records. However, Ayurveda, which is the dominant traditional medicine system in India, much of it is still practiced without digital records.

Methods:

In this study, 353,000 patients’ data were captured digitally by ~300 Ayurveda doctors over teleconsultation and in-person consultations. The entire dataset was analyzed based on age, sex, region, chronicity, Vikriti, disease morbidity, and comorbitidy and reported effectiveness of the treatment.

Results:

Younger patients were found to use more Ayurveda telemedicine, but all age groups were well represented. It was found that 82% patients had disease chronicity greater than 1 year. About 85% of the diseases were related to 6 organ systems, digestive (30.6%), endocrine (14.6%), skeleton (13.5%), skin (11.2%), nervous (7.6%), and respiratory (7.4%). The network analysis of the data revealed difference in sex and age-based patterns. Disease of endocrine and cardiovascular systems become comorbid for patient population at older age-groups as also observed in case of modern medicines.

Conclusion:

Within the limitations of using practice data from a single large group of Ayurveda practitioners, this represents the first data-driven view of Ayurveda practice in India. In spite of 82% of all the patients having chronic diseases, Ayurveda treatment offered complete or partial relief in more than 76% of cases, and only 0.9% reported aggravation in symptoms.

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