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Identifies which patient subgroups are at highest risk for specific adverse events.","intents":["I want to understand the safety profile of my drug before running real trials","I need to identify which patient populations are at highest risk for adverse events","I want to predict the frequency and severity of side effects in different subgroups"],"best_for":["Safety and pharmacovigilance teams","Clinical development programs","Regulatory affairs teams"],"limitations":["Cannot predict novel or unprecedented adverse events","Accuracy depends on completeness of historical safety data","Rare adverse events may be underestimated due to limited historical occurrence","Drug-drug interactions and real-world polypharmacy effects are difficult to model"],"requires":["Historical adverse event data from similar drugs or trials","Patient demographic and clinical characteristics","Concomitant medication information","Organ function and comorbidity data"],"input_types":["historical adverse event datasets","patient safety profiles","concomitant medication lists","clinical comorbidity data"],"output_types":["adverse event risk profiles","population-specific safety predictions","severity and frequency estimates","risk mitigation recommendations"],"categories":["healthcare","safety-analysis","clinical-research"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_quanthealth__cap_6","uri":"capability://healthcare.statistical.power.and.sample.size.optimization","name":"statistical-power-and-sample-size-optimization","description":"Calculates optimal sample sizes and statistical power requirements for trial designs based on simulated outcomes and historical data. 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