Creating actionable insights to inform personalized care using predictive analytics.
Holmusk analytics work in the intersection of pharma & healthcare practice. Our models help identify patients with unmet needs; those who will benefit most from new interventions, and provide biological insights to facilitate new drug research & development.
Holmusk’s Analytics Platform leverages scientific research, digital health and EHR data to inform predictive algorithms and provide actionable insights for personalized medicine.
Statistical analysis of clinical trial data has been standardized over the years. With the entry of real-world data (RWD) from EHRs and data captured through digital platforms, there is a need for a new approach. The challenge of generating validated evidence from RWD is particularly acute in behavioral health and chronic diseases due to multimorbidity and polypharmacy.
Holmusk has developed a proprietary semi-mechanistic modeling platform specifically designed to overcome this challenge and fully leverage the large longitudinal datasets now available. We combine System Dynamics with Deep Learning Neural Networks and Quantitative Systems Pharmacology and apply it to disease models based on longitudinal data. By informing models with data from biology, pharmacokinetics, pharmacology and clinical trials, robust representations can be developed which are generalizable beyond the datasets. By preserving biological causality, the models can provide explainability to model predictions and insights.