Datasets Comparison

Numerous studies on medicines are conducted day by day. To address shortcomings of medicines information generation, prediction, and classification models, the authors introduce a large medicines information dataset of textual data. For this motivation, the authors named our dataset ‘MID’. • Value of the data - MID is the largest, to our knowledge, available and representative Medicines Information Dataset (MID) for a wide variety of drugs. It includes the names of over 192k medicines, making it a comprehensive collection of pharmaceutical products. - MID is the largest, making it robust for generating information about drugs such as indications or interactions. - MID offers over 192k rows distributed in 44 variety therapeutic classes, making it robust for drug classification to therapeutic label. - MID provides accurate, authoritative, and trustworthy information on medicines for enhancing predictions and efficiencies in clinical trial management. - MID includes details such as drug names, information URL, salt composition, drug introduction, therapeutic uses, side effects, drug benefits, how to use of drug, how to use of drug, how drug works, quick tips of drug, safety advice of drug, chemical class of drug, habit forming of drug, therapeutic class of drug, and action class of drug. This dataset aims to provide a useful resource for medical researchers, healthcare professionals, drug manufacturers, data scientists, and enthusiasts interested in exploring the world of medicines and healthcare products. - In contrast with the few small available datasets, MID's size makes it a suitable corpus for implementing both classical as well as deep learning models. • MID.xlsx provides the raw data, including medicine information. The data collected to ensure an acceleration and save experimental efforts for medicines through help in predicting or generating or classifying of medicine information preclinically. • Therapeutic_class_counts.xlsx is summarize distribution of medicines per therapeutic class.

Medicine, Pharmacy, Clinical Trial, Natural Language Processing, Drug, Therapeutics, Confidentiality in Healthcare, Drug Information, Clinical Prediction Model, Pharmacoinformatics

Creative Commons Attribution 4.0 International

Numerous studies on medicines are conducted day by day. To address shortcomings of medicines information generation, prediction, and classification models, the authors introduce a large medicines information dataset of textual data. For this motivation, the authors named the medicines information dataset ‘MID’ . • Value of the data - The dataset comprises extensive medicines information, featuring over 192k rows distributed across 22 diverse therapeutic classes. - The dataset can be beneficial to the classification of therapeutic classes and robust for the prediction and generation of medicines information such as indications or interactions for enhancing efficiencies in clinical trial management, facilitating a detailed analysis of the risk affecting participants in clinical trials. - The dataset includes the name, link, contains, introduction, uses, benefits, side effects, how to use, how the drug works, quick tips, chemical class, habit forming, therapeutic class, action class, safety advice to alcohol, safety advice to pregnancy, safety advice to breastfeeding, safety advice to driving, safety advice to kidney, and safety advice to the liver. - The dataset is big data, making it a suitable corpus for implementing both classical as well as deep learning models. - The dataset provides a useful resource for medical researchers, healthcare professionals, drug manufacturers, data scientists, and enthusiasts interested in exploring the world of medicines and healthcare products preclinical for drug development and design. • MID.xlsx provides the raw data, including medicine information. The data collected to ensure an acceleration and save experimental efforts for medicines through help in predicting or generating or classifying of medicine information preclinically. • Therapeutic_class_counts.xlsx is summarize distribution of medicines per therapeutic class.

Medicine, Pharmacy, Clinical Trial, Natural Language Processing, Drug, Therapeutics, Confidentiality in Healthcare, Drug Information, Clinical Prediction Model, Pharmacoinformatics

Creative Commons Attribution 4.0 International