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Author: Admin | 2025-04-28
Via data-driven machine learning to help identify incidences of pulmonary ADEs for potential therapy and confounding factors that may have implications for treating patients diagnosed with COVID-19, respectively. Workflow of data-driven methodology for pulmonary symptomology in hypertension using machine learning models from preprocessing and dictionary creation to storing tables in the database an analysis. As a part of data cleaning, we were also challenged by multiple technical issues when combining drugs: (i) there were many drugs’ names that did not track a specific standard. (ii) Formulations of the same active ingredient with different generic or brand names for different routes of administration created confusion in collecting data (for instance, Revatio, Viagra, sildenafil, sildenafil citrate, APO sildenafil, sildenafil film-coated tablet, sildenafil citrate Aurobindo pharma, sildenafil Amneal Pharmaceuticals, Teva sildenafil, sildenafil Pfizer, sildenafil Greenstone, sildenafil Hormosan Filmtabletten, Revathio, sildenafil SUP, etc.). For this purpose, we combined drugs with or without salt, alcohol, etc. from different generic names and brand names. Data integration Request a detailed protocol The data were integrated into the 1DATA databank (www.1DATA.life) (Xu et al., 2019) from multiple sources, including the Food and Drug Administration (FDA) Adverse Drug Events Reporting System (FAERS), the Medical Dictionary for Regulatory Activities (MedDRA), and the ATC classification system. The FAERS database consists of voluntarily or mandatorily reported ADEs from healthcare professionals, manufacturers, and consumers; encompassing drug-related adverse occurrences pertaining to standard use, medical error, overdose, or product quality (FDA Adverse Event Reporting System, 2014). ADE reports from FAERS are typically coded in accordance
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