aion-clinical 1.0.4

Formal temporal and causal structure for consistent clinical data.

AION — Algebraic Interval Ontology for Clinical Networks

Formal temporal and causal structure for consistent clinical data across systems.

---

Clinical data systems often produce inconsistent results.

Not because of missing interfaces —
but because time, semantics and causality are not modelled consistently.

AION makes clinical data consistent and verifiable across systems.

It provides a formal, executable structure to:

• represent clinical events over time
• define and validate relationships between them
• support reproducible analytics
• enable reliable AI and interoperable data interpretation

---

Why AION?

Most systems can exchange clinical data.

The harder problem begins afterwards:

• data is interpreted differently across systems
• temporal relations remain implicit
• causal assumptions are hidden in project-specific logic
• analytics become difficult to compare and reproduce

AION addresses this structural layer directly.

Instead of modelling isolated data points only, AION models:

• intervals instead of points
• relations instead of implicit assumptions
• causal structures instead of static data
• verifiable consistency instead of hidden transformation logic

---

Quick Start

pip install aion-clinical

from aion.core import Interval, classify
import datetime

dt = lambda h: datetime.datetime(2024, 1, 1, h, 0)

anaesthesia = Interval(dt(7), dt(12))
operation   = Interval(dt(8), dt(11))

print(classify(anaesthesia, operation))
# -> AllenRelation.CONTAINS

This means:

• the events are not just stored
• their temporal relationship is explicitly defined
• that relationship can be checked and reused across analyses

---

What makes AION different?

Most clinical systems model events as isolated records.

AION models the structure between events.

This enables:

• consistent temporal reasoning
• reproducible analytics
• explicit causal modelling
• formal validation of data structures
• better reuse across systems and projects

---

Core Capabilities

Temporal Model

• Complete Allen interval algebra (13 relations)
• Fuzzy intervals with probabilistic reasoning

Data Model

• Universal clinical event model (6-tuple)
• Type system with hierarchy and schema evolution

Query & Analytics

• Cohort algebra
• Temporal pattern languages

Causal Inference

• Causal graphs
• Do-operator
• Structure learning

AI Integration

• Formal AI component model
• Explainability (Shapley, counterfactuals)

Privacy

• Differential privacy (Laplace / Gaussian)
• Federated computation

Interoperability

• FHIR mapping via structural homomorphism

---

Example: Cohort Query

from aion.query.cohort import query_event_sequence

q = query_event_sequence("Diagnosis", "Procedure")
cohort = q.evaluate(ctx)

---

Example: Causal Analysis

from aion.causal.graph import CausalGraph
from aion.causal.do import DoOperator

g = CausalGraph()
g.add_edge("Diagnosis", "Procedure")

do = DoOperator(g, data)
result = do.intervene("Procedure", "Outcome")

---

Architecture Overview

aion/
├── core        Temporal algebra, event model
├── abstraction Episodes and trajectories
├── query       Cohort algebra
├── causal      Causal inference
├── privacy     Differential privacy
├── ai          AI model integration
├── explain     Explainability
├── schema      Schema evolution
└── adapters    FHIR mapping

---

Test Suite

pytest aion/tests/
# 326 tests, ~2s runtime

---

Positioning

AION is not:

• a data format
• a data warehouse
• a FHIR replacement

AION is:

the structural consistency layer beneath clinical data systems

---

Formal Foundation

AION generalises earlier formal work into a domain-independent structure for clinical information systems.

It formalises, among others:

• temporal relations
• event structure
• abstraction layers
• cohort logic
• causal inference
• explainability
• schema evolution
• privacy-preserving federation
• FHIR-compatible structural mapping

---

License & Citation

EUPL-1.2 — open source
Commercial licensing: [e-mail]

@software{aion2026,
  title  = {AION: Algebraic Interval Ontology for Clinical Networks},
  author = {Matten, Friedhelm},
  year   = {2026},
  url    = {https://codeberg.org/fm2-project/aion}
}

© Friedhelm Matten, ISCaD GmbH