Built with Opus 4.7 · Cerebral Valley × Anthropic Hackathon · April 2026
An ill person is an organism executing predictions across its four cognitive levels — cellular bioelectric, organic homeostasis, somatic unconscious, explicit consciousness. Altered biomarkers, chronic symptoms, metabolic rigidity: all are the signature of instructions the organism keeps executing, not memories it consults. Inferentia identifies those active instructions — predictive imprints, deficiencies limiting regulation, toxins forcing the system, available agency, amplifying genetics — and computes which interventions expand the frontier the pattern contracted. The outcome is not normalizing a value: it is enabling the organism to do more.
Tesis (original, español):
Una persona enferma es un organismo ejecutando predicciones en sus cuatro niveles cognitivos — bioeléctrico celular, homeostasis orgánica, inconsciente somático y consciencia explícita. Biomarcadores alterados, síntomas crónicos, rigidez metabólica: todo es la signatura de instrucciones que el organismo sigue ejecutando, no recuerdos que consulta. Inferentia identifica esas instrucciones activas — improntas predictivas, carencias que limitan la regulación, toxinas que fuerzan el sistema, agencia disponible, genética amplificadora — y calcula qué intervenciones expanden la frontera que el patrón contrajo. El outcome no es normalizar un valor: es que el organismo pueda más.
Inferentia is a clinical tool that identifies active predictive instructions across six co-present layers — active imprints, nutritional substrate, toxic load, available agency, inferred nutrigenomic profile, and observable signature — and computes interventions that expand the organism's regulatory capacity. The computational engine combines Active Inference (Friston) with multivariate Bayesian classification over clinical signatures, integrating the TAME multi-scale cognition framework (Levin), phenotypic flexibility (van Ommen), and the BV4 clinical framework (Ojeda Rios, proprietary).
Research prototype — built during Cerebral Valley × Anthropic Hackathon (2026-04-21 through 2026-04-26).
- Not a medical device.
- Not a diagnostic tool.
- Not a replacement for professional medical consultation.
- Clinical validation: planned for Phase 2, post-hackathon.
Patient view ←→ Unified backend engine ←→ Clinician view
(narrative Opus 4.7 orchestrator (technical posteriors,
accessible + Active Inference (pymdp) SNPs, metabolic
predictions, + Bayesian inference signature, clinical
agency panel) (NumPyro) note, editable plan)
+ Multimodal fusion
All clinical prescriptions require clinician validation before reaching the patient. The patient sees their own predictive map and agency metrics; the clinician sees the full technical stack and validates.
- Predictive Map — hero visualization of active predictive priors.
- Expected Metabolic Signature — inferred biomarkers with discordance vs. labs.
- Prioritized Nutrigenomic SNPs — Bayesian posterior over relevant SNPs (clinician view only).
- Salutogenic Nutritional Program — clinician-validated, agency-oriented.
- Agency Panel — pre/post delta on primary outcome (not biomarker — agency).
Pilar references (not exhaustive):
- Friston (2010, 2017) — Free Energy Principle / Active Inference.
- Sterling & Eyer (1988); McEwen (1998, 2017) — Allostasis / allostatic load.
- Barrett (2017) — Predictive interoceptive construction.
- Porges (2011) — Polyvagal Theory.
- Levin (2019) — TAME framework / cognitive cone.
The clinical instance used in this prototype references the author's independent clinical framework (BV4 Clinical Treatise, Dr. Miguel Ojeda Rios) — a taxonomy of 13 survival imprints with canonical Spanish names. That framework is proprietary to the author and is not included in this repository. This hackathon project is a distinct new implementation that references BV4 only as one of several possible clinical ontologies mappable to the Active Inference substrate. For a one-page conceptual overview including Spanish-English term equivalences, see docs/concepts/imprints_overview.md.
- All code in this repository was written during the hackathon (2026-04-21 onward).
- No prior code, models, or datasets have been reused.
- No real patient data is used anywhere. All demonstrations use a synthetic patient generated by the author.
- The author's independent clinical framework (BV4) is referenced as intellectual context but is not included in code or data form in this repo.
- This project is released under the MIT License (see LICENSE).
- All dependencies are open source. See CREDITS.md for the full list and respective licenses.
- Public biomedical datasets used (see CREDITS.md): GWAS Catalog (EBI, CC0), gnomAD (CC0), Human Phenotype Ontology (CC-BY), Reactome (CC0).
- Psychometric instruments: Pearlin Mastery Scale (public domain, Pearlin & Schooler 1978). Interoceptive items are author-derived; no copyrighted instrument has been copied.
This project operates within Anthropic's Usage Policy. Specifically:
- The system does not provide autonomous medical diagnosis or treatment recommendations to end users.
- All clinical outputs are presented as probabilistic hypotheses requiring professional validation.
- The two-level architecture (patient view + clinician validation gate) ensures human oversight of all clinical content delivered to patients.
- Language in the patient-facing interface is strictly probabilistic and non-directive.
This prototype is for research and demonstration purposes only.
- Not approved by any regulatory body (FDA, EMA, COFEPRIS, etc.).
- Not intended for clinical decision-making in production settings.
- Not to be used as a substitute for professional medical advice, diagnosis, or treatment.
- The author assumes no liability for use outside the controlled demonstration context of the hackathon.
- Dr. Miguel Ojeda Rios — Author, clinical lead, full stack (solo team).
- Hackathon: Built with Opus 4.7 (Cerebral Valley × Anthropic).
- Problem statement: #1 — Build From What You Know.
- Submission deadline: 2026-04-26, 20:00 EST.