HolisTech Health develops rigorous computational models, physics-informed deployment benchmarks, and leak-free machine learning architectures to anticipate critical physiological deterioration and accelerate molecular discovery.
Modern biomedicine generates immense high-frequency telemetry, molecular profiles, and diagnostic signals. However, translating this data into reliable clinical intelligence is severely constrained by label leakage, cross-device hardware variation, and lack of causal calibration.
At HolisTech Health, our approach is defined by engineering discipline. We build causal, leak-free machine learning frameworks, physics-informed robustness benchmarks, and molecular graph models engineered to perform reliably across heterogeneous real-world settings.
Enforcing strict temporal causality in physiological timeseries to eliminate spurious predictive performance.
Simulating hardware optical drift, detector noise, and sensor heterogeneity prior to physical deployment.
Aligning algorithmic forecast windows with clinical response windows to enable proactive, dignified interventions.
Explore our core initiatives spanning predictive metabolic forecasting, physics-informed spectroscopy benchmarks, geriatric decline modeling, resistance-breaking graph learning, and clinical phenotyping.
Detailed scientific foundations, empirical metrics, architectural diagrams, and clinical roadmaps are hosted on individual project sites.
Patient-specific real-time hypoglycaemia forecasting platform powered by causal machine learning and continuous glucose monitor telemetry.
Physics-informed robustness benchmark evaluating and certifying deep learning models across virtual fleets of low-cost, portable Raman spectrometers.
Predictive trajectory forecasting of geriatric functional decline and frailty from longitudinal dynamometry, kinematics, and cognitive telemetry.
Relational graph neural network modeling pathogen-drug resistomes across broad pathogen isolates, discovering non-antibiotic adjuvants to restore antibiotic potency.
Unsupervised clustering discovering latent physiological phenotypes of counter-regulatory resilience and vulnerability in Type 1 Diabetes.
Multimodal machine learning framework combining continuous ECG waveforms, respiratory dynamics, and ICU context for 1-hour crash prediction and 3-to-6 hour risk stratification.
Sedation-aware multimodal neural network framework predicting transition to new-onset coma within 12 hours in adult ICU patients with calibrated false-alert dampening.
Translating high-dimensional physiological and molecular data into robust, clinical-grade intelligence requires disciplined computational principles.
Rigorous data sanitation enforcing strict time-directionality. We prevent retrospective smoothing leakage in real-time glucose and hemodynamics data to ensure true out-of-sample validity.
Simulating instrument degradation, optical defocus, detector shot noise, and sensor drift. Models are trained and benchmarked against synthetic device populations before physical deployment.
Employing graph neural networks over molecular structures paired with multi-parameter physiological waveform modeling to uncover non-linear biological associations.
Calibrating prediction windows to match real clinical decision cycles: 45 minutes for metabolic response, 1 hour for acute cardiac collapse, and 12 hours for sedation-masked coma.
Driven by a shared commitment to rigorous computational science, clinical precision, and proactive healthcare technologies.
Co-Founder
HolisTech Health
Co-Founder
HolisTech Health
Co-Founder
HolisTech HealthWe welcome dialogue with research institutions, clinical investigators, and technical collaborators interested in causal machine learning, hardware robustness benchmarking, and proactive healthcare systems.
