AI-Powered Public Health Technology
Our platform combines advanced machine learning, epidemiological modeling, and ethical AI principles to deliver actionable public health intelligence at scale.
Our platform combines advanced machine learning, epidemiological modeling, and ethical AI principles to deliver actionable public health intelligence at scale.
We leverage state-of-the-art AI and data science methodologies, adapted specifically for the unique challenges of population health.
Deep learning models for pattern recognition in health data, including convolutional neural networks for medical imaging, recurrent networks for time-series forecasting, and transformer architectures for clinical text analysis.
Advanced NLP extracts structured insights from unstructured sources—clinical notes, death certificates, news reports, social media—enabling real-time disease surveillance and sentiment analysis.
Automated analysis of medical images, satellite imagery for environmental health monitoring, and visual detection of disease vectors through drone and sensor networks.
Predictive models for disease incidence, hospital capacity, and resource needs. We use ARIMA, Prophet, LSTM networks, and ensemble methods to forecast public health trends with quantified uncertainty.
Spatial statistics and GIS integration identify disease clusters, track transmission pathways, and optimize intervention geography. Our models account for spatial autocorrelation and environmental factors.
Beyond correlation, we employ causal ML techniques—instrumental variables, difference-in-differences, synthetic controls—to estimate true intervention effects and guide policy decisions.
Multi-Source Ingestion: Our platform ingests data from electronic health records (HL7 FHIR), syndromic surveillance systems, laboratory networks, vital statistics, environmental sensors, social determinants databases, and open data sources.
Interoperability Standards: We adhere to public health data standards including FHIR, SNOMED CT, ICD-10, and LOINC, ensuring seamless integration with existing health IT infrastructure.
Real-Time Pipelines: Stream processing enables sub-minute latency for critical signals, while batch processing handles large-scale retrospective analyses.
Model Library: Pre-trained models for common public health tasks (influenza forecasting, outbreak detection, vaccine hesitancy prediction) can be fine-tuned to local contexts.
AutoML Capabilities: Automated hyperparameter tuning and model selection reduce the need for specialized ML expertise, democratizing access to sophisticated analytics.
Ensemble Methods: We combine multiple algorithms to improve accuracy and robustness. No single model is perfect; ensemble approaches hedge against individual model failures.
Differential Privacy: Mathematical guarantees that individual-level data cannot be reverse-engineered from aggregate statistics, enabling safe data sharing.
Federated Learning: Models train across distributed datasets without centralizing sensitive information. Institutions maintain data sovereignty while contributing to collective intelligence.
Encryption & Access Control: End-to-end encryption, role-based access control, audit logging, and HIPAA/GDPR compliance are built into every layer of our infrastructure.
AI for public health must be developed and deployed responsibly. We follow rigorous ethical principles aligned with WHO guidance and CDC best practices, ensuring safe and secure AI implementation with human oversight and research excellence at every stage.
Bias Auditing: We systematically test for disparate impact across demographic groups—race, ethnicity, age, geography, socioeconomic status—and mitigate detected biases.
Representative Data: Training datasets must reflect population diversity. We actively source data from underrepresented communities and apply reweighting techniques to address imbalances.
Equity Metrics: Model performance is evaluated not just on average accuracy, but on equity metrics ensuring benefits are distributed fairly.
Interpretable Models: When possible, we use inherently interpretable algorithms (decision trees, linear models, GAMs). For complex deep learning, we apply SHAP values, LIME, and attention visualization.
Model Cards: Every model is documented with a model card detailing intended use, training data, performance metrics, limitations, and ethical considerations.
Stakeholder Communication: Technical outputs are translated into plain language for policymakers and communities, with clear uncertainty quantification.
Human Oversight: Following CDC guidance, AI systems augment, not replace, human judgment. Every AI solution is developed with human oversight to ensure responsible deployment and trustworthy outcomes.[5]
Continuous Monitoring & Research Excellence: Model drift detection ensures algorithms remain accurate as populations and diseases evolve. We maintain rigorous research standards in all AI development and validation processes.
Ethics Review Board: Our internal ethics board, including external public health ethicists and community representatives, reviews all projects to ensure alignment with public health mission and values.
Purpose Limitation: Data is used only for specified public health purposes. No commercial use, no re-identification attempts, no mission creep.
Minimal Data Collection: We collect only what is necessary. Privacy-preserving techniques reduce raw data requirements.
Data Retention Policies: Clear schedules for data retention and secure deletion, compliant with legal requirements and ethical best practices.
Anomaly detection algorithms identify unusual patterns in syndromic surveillance data, lab results, and social signals. Bayesian methods quantify evidence strength, reducing false alarms.
Multi-week ahead forecasts for influenza, COVID-19, and other infectious diseases. Ensemble models combine mechanistic epidemiological models with data-driven ML approaches.
Identify individuals and communities at highest risk for adverse outcomes. Gradient boosting models integrate clinical, demographic, social, and environmental risk factors.
Operations research and reinforcement learning optimize vaccine distribution, testing site placement, and healthcare workforce allocation under resource constraints.
NLP analyzes social media, search trends, and surveys to understand health behaviors, vaccine hesitancy, and misinformation spread, informing communication strategies.
Agent-based models and system dynamics simulations predict the impact of interventions—lockdowns, mask mandates, vaccination programs—before implementation.
Models are validated using temporal cross-validation (testing on future data), spatial cross-validation (testing on held-out geographies), and prospective evaluation (real-world deployment monitoring).
We publish performance metrics including sensitivity, specificity, positive predictive value, calibration curves, and fairness metrics disaggregated by subgroup.
Our development processes align with FDA guidance on software as a medical device, ONC interoperability standards, and CDC data quality frameworks.
For clinical decision support tools, we follow evidence-based medicine principles and pursue appropriate regulatory clearances where required.
All predictions include confidence intervals and uncertainty estimates. We use Bayesian methods, bootstrapping, and ensemble approaches to characterize uncertainty.
Communicating what we don't know is as important as communicating what we do know. Overconfident predictions erode trust and lead to poor decisions.
Significant methodological innovations are submitted to peer-reviewed journals. We engage with the academic community through conferences and collaborative research.
Open science practices—preregistration, code sharing, open data—ensure reproducibility and enable community scrutiny of our work.
Learn more about how our AI platform can support your public health initiatives, or explore our research publications and technical documentation.