Limbix (by BigHealth)
Evidence-based digital therapeutics for adolescent mental health and behavioral activation.
Open-source reinforcement learning environments for clinical decision support and medical simulation.
Health Gym is a specialized framework designed to bridge the gap between reinforcement learning (RL) research and clinical application. Originally conceived to extend the OpenAI Gym (now Gymnasium) interface into the medical domain, Health Gym provides high-fidelity, standardized environments for simulating complex physiological states. By 2026, it has become the foundational tool for 'offline-to-online' RL transitions, allowing data scientists to train agents on historical EHR (Electronic Health Record) data and validate them in simulated environments before clinical pilot phases. The technical architecture relies on Ordinary Differential Equations (ODEs) and Markov Decision Processes (MDPs) to model conditions like Sepsis and Acute Kidney Injury. Its primary utility lies in its ability to generate synthetic patient trajectories that mimic real-world physiological responses to interventions such as vasopressors, fluid resuscitation, and mechanical ventilation. As a research-centric tool, it emphasizes safety-constrained RL, ensuring that simulated policies adhere to clinical guidelines while seeking optimal patient outcomes. It is extensively used by pharmaceutical R&D and academic hospitals for policy evaluation and treatment optimization.
An MDP environment modeled after the Sepsis-3 definition, using 44+ physiological variables to simulate patient response to IV fluids and vasopressors.
Evidence-based digital therapeutics for adolescent mental health and behavioral activation.
Predictive clinical and operational intelligence to fight death and waste in healthcare.
Predictive medical data and clinical insights for streamlined enterprise underwriting.
The professional medical network for clinicians, providing HIPAA-compliant AI and telehealth solutions.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Uses Ordinary Differential Equations to ensure that state transitions between patient steps follow biological laws rather than random noise.
Standardized interfaces for training agents on static datasets (like MIMIC-III or eICU) before live environment testing.
Built-in Bayesian modules to estimate the confidence of the RL agent's recommended clinical action.
Specialized environment for Acute Kidney Injury tracking creatinine levels and urine output relative to medication dosage.
Tools to define complex rewards based on survival probability, length of stay, and medication side effects.
Fully updated to support the Gymnasium API, ensuring compatibility with modern RL frameworks like CleanRL and Tianshou.
Sepsis is the leading cause of hospital death; over-administration of fluids can lead to organ failure.
Registry Updated:2/7/2026
Validate on unseen test cohorts.
Clinicians often miss early signs of AKI due to delayed creatinine spikes.
Delayed weaning leads to infections; premature weaning leads to re-intubation.