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Covariate Selection Using Machine Learning for Clinical Models
HMS developed an interactive application for machine learning-based covariate selection for a global pharmaceutical company. It structures the covariate selection process and provides a basis for evaluating pharmacometric models.
A Lab in Your Pocket: Data-Driven Decisions
HMS developed a modular software solution for trinamiX that includes a mobile app, a cloud-based backend, and a web-based customer portal. This solution enables the real-time transmission and analysis of sensor data from an infrared detect…
Planning and Analysis of Clinical Trials in a Validated R Environment at medac
To analyze clinical trial data, HMS designed and implemented a validated R environment within a GxP framework. Controlled package versions and versioned code support traceable analyses on Microsoft Windows.
Planning and Analysis of Clinical Trials in a Validated R Environment at medac
To analyze clinical trial data, HMS designed and implemented a validated R environment within a GxP framework. Controlled package versions and versioned code support traceable analyses on Microsoft Windows.
Role-Based Document Chatbot with Agentic RAG
HMS developed a chatbot for a global pharmaceutical company that searches internal documents based on user roles and makes relevant content accessible via an AI-powered interface.
Identify Pharmaceutical Trends Earlier with AI-Powered News Analysis
HMS developed a platform for an international pharmaceutical company that analyzes news, clusters topics, and highlights trends. This provides departments with a better foundation for market monitoring and updates.