MID: Medicines Information Dataset

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.

{'original_description': '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’ .', 'translated_description': '医药领域的研究日复一日地进行。为解决医药信息生成、预测和分类模型之不足，作者们推出了一项庞大的医药文本信息数据集。基于此动机，该医药信息数据集被命名为‘MID’。'}