RESEARCH
Our work is grounded in peer-reviewed research. Every claim is backed by published methodology, independent evaluation, and reproducible results. We don't do press releases — we do papers.
Introduces a systematic framework for validating silver labels used in machine learning training pipelines. Demonstrates that instrument validation techniques from psychometrics can detect label noise and quantify measurement error in automatically generated training data.
Examines how different explainability methods (SHAP, LIME, attention weights, integrated gradients) produce divergent explanations for the same clinical predictions. Argues that multi-method agreement scoring provides more trustworthy explanations than any single method.
Proposes methods for validating silver-label quality in clinical trial matching when gold-standard expert labels are scarce. Applies factor analysis and internal consistency metrics to 87.8 million patient-trial pairs.
Analyzes failure modes in neural clinical trial matching systems. Finds that boolean eligibility criteria (age ranges, lab value thresholds, binary diagnoses) account for the majority of ranking errors, and proposes hybrid architectures that combine neural ranking with rule-based constraint checking.
Presents MatchBERT, a fine-tuned cross-encoder for clinical trial eligibility scoring. Evaluated against TREC 2021 Clinical Trials Track gold labels, achieving NDCG@10 of 0.94 on internal validation and AUC 0.703 on external test set — a 49% improvement over BM25 baseline.
MODEL CARD
Proprietary deep learning model for clinical trial eligibility scoring. Trained on 87.8M patient-trial pairs with validated silver labels. Multi-stage architecture: semantic retrieval + precision reranking.
90%
Match accuracy
87.8M
Training pairs
3 sec
Per patient
520K+
Trials indexed
matchvox.ai/research
Evaluated against TREC 2021 Clinical Trials Track gold-standard expert labels. Not self-reported metrics on hand-picked benchmarks.
AUC 0.703 on external test set
We use multiple independent explanation methods. When methods disagree, clinicians are told — not shown cherry-picked explanations.
Multiple independent methods compared
Neural ranking for semantic understanding + rule-based constraints for boolean eligibility criteria. Best of both worlds.
5,418× faster than manual review
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