Why Domain Specificity Matters
MedDRA Coding Accuracy Determines Regulatory Acceptance
General-purpose language models trained on internet text lack the specialized knowledge required for pharmacovigilance tasks. MedDRA coding, causality assessment, and regulatory classification require domain-specific training data and validation.
ArcaScience's models are trained exclusively on pharmacovigilance and regulatory data — clinical trial databases, adverse event reports, regulatory submissions, and pharmacoepidemiology literature. This specialization delivers regulatory-grade accuracy that general models cannot match.
False Negative Risk
Missed adverse event signal in spontaneous reporting data leads to delayed regulatory action. Failure to detect drug-drug interaction results in preventable patient harm.
False Positive Consequence
Incorrect MedDRA coding triggers unnecessary signal investigation, wasting months of analyst time and delaying submissions. Overestimated risk profile affects market access decisions.
Four Complementary Categories
24 Models Organized by Analytical Function
Each category addresses a distinct stage of the benefit-risk analysis pipeline. Models are designed to work together with consistent data formats and validation protocols.
NLP Models
Natural language processing for unstructured pharmacovigilance text
Text Extraction
Named entity recognition (NER) for drugs, adverse events, patient demographics, dosages, and temporal relationships from case narratives and regulatory documents.
Trained on: 10M+ case narratives
Entity Recognition
Identification of drug names (generic, brand, synonyms), adverse events (MedDRA terms), patient characteristics, lab values, and causality indicators.
Accuracy: 96.2% on held-out test set
Classification
Automated MedDRA coding (PT, HLT, SOC), seriousness criteria assessment (death, life-threatening, hospitalization), expectedness determination, and regulatory category assignment.
MedDRA PT accuracy: 94.8%
Sentiment Analysis
Severity language detection, clinical significance scoring, outcome assessment (recovered, fatal, permanent), and reporter confidence evaluation.
Severity correlation: 0.91 (Pearson r)
Summarization
Generate executive summaries from multi-source evidence, signal detection reports, benefit-risk synthesis for regulatory submissions (PSUR, CTD 2.5).
ROUGE-L score: 0.87
Translation
Multi-language support (English, French, German, Spanish, Japanese, Mandarin) with pharmacovigilance-specific terminology preservation and MedDRA term mapping.
Supports 6 languages
Statistical Models
Quantitative methods for benefit-risk evaluation
Bayesian Inference
Prior-informed signal detection with posterior probability distributions. Credible intervals for benefit-risk metrics. Uncertainty propagation through decision frameworks.
Method: MCMC with Gibbs sampling
Frequentist Analysis
Classical hypothesis testing (t-tests, chi-square, Fisher's exact), confidence interval estimation, p-value calculation for regulatory documentation. Multiple testing correction (Bonferroni, Benjamini-Hochberg).
Supports all standard tests
Survival Analysis
Kaplan-Meier curves, log-rank tests, Cox proportional hazards regression, time-varying covariates, competing risks analysis. Essential for long-term safety evaluation and oncology endpoints.
Cox PH with stratification support
Dose-Response
Non-linear modeling (Emax, sigmoid, polynomial), threshold detection, benchmark dose (BMD) calculation, safety margin estimation for toxicology and first-in-human studies.
FDA BMDS-compliant methods
Meta-Analysis
Fixed effects and random effects models (DerSimonian-Laird, REML), forest plot generation, heterogeneity assessment (I², Cochran's Q), publication bias detection (funnel plots, Egger's test).
Cochrane-compliant methods
Regression
Linear, logistic, Poisson, negative binomial regression. Multivariable adjustment for confounding, subgroup identification, interaction testing, propensity score matching for observational data.
Supports complex interactions
Predictive Models
Machine learning for signal detection and risk forecasting
Signal Detection
Disproportionality analysis (PRR, ROR, EBGM), multi-item gamma Poisson shrinker (MGPS), Bayesian confidence propagation neural network (BCPNN). Continuous monitoring with automated alerts.
Sensitivity: 89.3% | Specificity: 91.7%
Trend Forecasting
ARIMA time series models, changepoint detection, seasonal decomposition, epidemic curve modeling for emerging safety signals and public health surveillance.
Forecast horizon: 12 months
Risk Scoring
Patient-level adverse event probability prediction using gradient boosting (XGBoost), random forests, and neural networks. Calibrated risk scores with interpretable feature importance.
AUC-ROC: 0.87 (validation set)
Patient Stratification
Unsupervised clustering (k-means, hierarchical, DBSCAN) for subgroup identification. Supervised classification for benefit-risk responder prediction. Treatment effect heterogeneity assessment.
Silhouette score: 0.71
Outcome Prediction
Deep learning models (LSTM, transformer) for longitudinal outcome prediction. Multi-task learning for simultaneous efficacy and safety prediction. Explainable AI with SHAP values.
Mean absolute error: 0.09
Comparative Effectiveness
Real-world evidence synthesis with propensity score weighting, doubly-robust estimation, network meta-analysis for indirect comparisons. G-computation for causal inference.
Supports indirect comparisons
Validation Models
Quality assurance and uncertainty quantification
Consistency Checks
Cross-reference validation across data sources (FAERS vs EudraVigilance), duplicate case detection, temporal consistency verification, data lineage tracking for audit trail.
Detects 99.5% of duplicates
Cross-Validation
K-fold cross-validation (k=5, 10), stratified sampling, temporal validation (train on historical data, test on recent), holdout test sets for performance evaluation.
Standard: 5-fold CV
Bias Detection
Reporting bias assessment (funnel plots, Egger's test), selection bias quantification, confounding detection with directed acyclic graphs (DAGs), immortal time bias checks.
DAG-based causal analysis
Completeness Scoring
Field-level completeness assessment for regulatory submission readiness. Missing data pattern analysis, imputation quality scoring, minimum data quality thresholds for analysis inclusion.
Threshold: 95% completeness
Confidence Calibration
Platt scaling, isotonic regression, temperature scaling for probabilistic model calibration. Reliability diagrams (calibration curves) for visual assessment.
Expected calibration error: 0.03
Uncertainty Quantification
Bayesian credible intervals, bootstrap confidence intervals, Monte Carlo simulation for parameter uncertainty propagation. Prediction intervals for forecasts.
Default: 10,000 bootstrap samples
Training & Validation
Continuous Improvement and Model Lifecycle
All 24 models undergo quarterly retraining on the latest data. Model performance is continuously monitored with automated alerts for degradation.
Validation uses independent test sets never seen during training. Performance benchmarks are measured against regulatory gold standards and published baselines.
Training Data
10M+ adverse event case reports, 500K+ clinical trial records, 2M+ PubMed abstracts, 100K+ regulatory documents. All data is de-identified and compliant with GDPR/HIPAA.
Validation Protocol
80/20 train-test split with temporal validation. Independent expert review of 1000+ predictions per model. Annual external audit by regulatory consultants.
Performance Monitoring
Real-time dashboards tracking accuracy, precision, recall, F1 score, AUC-ROC. Automated alerts for performance degradation below thresholds. Quarterly retraining cycle.
Performance Metrics
Measured Against Regulatory Gold Standards
All models are validated on independent test sets with performance benchmarks exceeding published baselines.
Accuracy
Overall classification accuracy
Precision
True positive rate
Recall
Sensitivity
F1 Score
Harmonic mean of precision and recall