aceRule-Based Versus Generative Extraction of Psychological Symptoms From Forensic Medical Certificates: A Feasibility Study in the French ORFeAD Network
Forensic medical certificates for victims of interpersonal violence describe psychological symptoms in unstructured prose, rarely explicit and requiring inference. To assess the feasibility of two extraction pipelines-rule-based and a locally served generative large language model-in a French multicentric forensic network, and to identify variables usable without human verification. 110 randomly selected 2021 certificates from 12 ORFeAD units were processed by both pipelines: rule-based (segmentation, lexicons,…
Automated extraction of psychological and subjective variables from unstructured forensic certificates was feasible with high specificity, with 24 of 35 variables meeting an 85% reliability threshold under at least one pipeline.
Sensitivity was heterogeneous and low for low-prevalence symptoms, 11 of 35 variables failed the reliability threshold under either pipeline, and neither pipeline supports individual-level decisions, with the generative model incurring far higher compute cost.
Feasibility design with 110 certificates from 2021, no inferential testing, and out-of-the-box LLM settings limits generalizability and requires per-variable qualification before research use.
Evidence
- Peer-reviewedBehavioral Sciences & the Law2026-10-03
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Truvace Impact Record TRV-2026-1282, v1: “Rule-Based Versus Generative Extraction of Psychological Symptoms From Forensic Medical Certificates: A Feasibility Study in the French ORFeAD Network.” Truvace, 2026-10-05. /record/TRV-2026-1282 (accessed at citation time). sha256 9b3945a0efaa9fce…
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