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Objective: This study aims at providing a comprehensive documentation on ethanoveterinary plant knowledge of the tribal people in order to preserve the fost-eroding knowledge and resources of the kalasapadi hills, Pappireddipatti revenue Tk, Dharmapuri. Methods: Field work was conducted from 2015 2016. Moreover 36 informants were interviewed. First, successive oral free listing and semi interviews were performed. The veterinary diseases as described by the informants were categorized according to the symptoms they cause and the organs they affect. Information on the cited plants, informant consensus factor (ICF) and fidelity level (FL) was calculated based on use reports. Results: Utilization of 49 plant species 30 genera under 31 families, has been recorded against livestock ailments. Plant parts, such as leaf, root, flower, bark, resin, and rhizome, are used in the preparation. Among the plant parts, bark is predominately used. Most of the preparations parts of more than one plant as the ingredients, and many of such combined preparations are used for treating more than one ailment. Conclusion: According to the local people, the most often mentioned species have high medicinal potential. At the same time the comprehensive pharmacological investigations of the herbal plants will be helpful in development of new drugs for a particular condition. There is a need to conserve the @ IJTSRD |

The widespread use of “electronic health record systems (EHRs)” in health care provides a large amount of real-world data, opening up new opportunities for medical trials. “Deep learning, a subset of machine learning (ML)”, has experienced a meteoric rise in popularity over the last six years, owing to advances in computing power and the accessibility of enormous new datasets. “Natural language processing (NLP)” approaches have been used as an “artificial intelligence strategy” to obtain information from medical narratives in EHRs, as a huge quantity of useful clinical knowledge is contained in clinical stories. This NLP capacity may enable “automated chart review in clinical care to identify individuals with distinct clinical features” and decrease methodological heterogeneity in establishing “phenotypes, masking biological heterogeneity in allergy, asthma, and immunology research”. Aim of this research paper is to understand the advanced technologies such as “Machine Learning, Natural Language Programming” are helpful for digital health field. In this context, secondary data collection method is used to gather information related to this topic.