Technical FAQ & Knowledge Base
Answers to complex structural, statistical, and deployment questions regarding our B2B predictive suites, clinical data processing, meta-analysis, and HIPAA compliance.
Browse the knowledge base below for detailed answers regarding clinical data processing, predictive modeling, and system deployment.
Data Processing & Ingestion
Can your models ingest raw EMR exports, or do we need to clean the data first?
I handle the data processing. EMR data is noisy and frequently has missing variables. I use longitudinal methods for missing data imputation to structure your raw CSV/JSON exports before applying the predictive models. You provide the raw data; I handle the statistical modeling.
We use a proprietary assessment scale (not PHQ-9 or GAD-7). Can you still build a predictive model?
Yes. My methods are metric-agnostic. As long as the assessment scale is continuous and administered longitudinally, the statistical models can use your specific metric as the primary outcome.
Statistical Methodology
What is the statistical difference between the Empirical Predictive Engine and the Outcomes Predictive Suite?
The Empirical Predictive Engine uses Longitudinal Linear Mixed Models (LLMM) to model random intercepts and slopes. It turns each user into their own control group to isolate true within-user causality (e.g., Day ON vs. Day OFF a protocol) for R&D validation. The Outcomes Predictive Suite (OPS) is an extremely flexible system designed to predict psychotherapy treatment success or failure. It provides extensive options for handling missing data, visualizing outputs, and tracking a patient's probability of success on a session-by-session basis for clinical triage.
Why do you emphasize Effect Sizes (Cohen's d, Hedges' g) instead of standard p-values?
I calculate standardized effect sizes and apply robust variance estimation to quantify the actual magnitude of the clinical impact, providing actionable insight rather than just theoretical p-value thresholds.
Security, Compliance & Deployment
How do you ensure HIPAA compliance and data security?
No Protected Health Information (PHI) is ever transmitted to external servers. The applications run locally, meaning data processing occurs strictly on your HIPAA-compliant hardware without external dependencies.
Can your predictive applications integrate directly into our existing tech stack?
Yes. Whether you require an API for backend integration, a desktop app, or a web interface, the core statistical models (R/Python) run locally and integrate directly into your systems.
Do you provide ongoing support and model retraining after the initial handoff?
Yes. Predictive models experience drift as clinical populations evolve. I offer tiered retainer contracts to provide data health audits and model recalibration.
Proprietary Software & Tools
How does the Stanford C-Score calculator scrape and validate data from Google Scholar?
The C-Score application uses automated web-scraping scripts to securely ingest raw citation metrics from Google Scholar profiles. It normalizes these metrics against the Ioannidis composite indicator formula to generate a global ranking.
How do your language learning applications (Veloz, PQM) integrate with your statistical background?
My applications are built on psychometrics and cognitive science. Veloz utilizes the Shadow Loop method for phonological loop activation. I apply rigorous data tracking (measuring receptivity and perseverance in the PQM Tracker) to optimize user acquisition pathways empirically.