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PSDL - Patient Scenario Definition Language

Tests PyPI version Python 3.8-3.12 License: Apache 2.0

An open, vendor-neutral standard for expressing clinical scenarios in healthcare AI.

What SQL became for data queries, ONNX for ML models, and GraphQL for APIs — PSDL is becoming the semantic layer for clinical AI.

Installation

pip install psdl-lang

# With OMOP adapter
pip install psdl-lang[omop]

# With FHIR adapter
pip install psdl-lang[fhir]

# Full installation
pip install psdl-lang[full]

Quick Start

from psdl.examples import get_scenario, list_scenarios
from psdl.runtimes.single import SinglePatientEvaluator, InMemoryBackend
from datetime import datetime, timedelta

# List available built-in scenarios
print(list_scenarios())
# ['aki_detection', 'hyperkalemia_detection', 'lactic_acidosis', 'sepsis_screening']

# Load a built-in scenario
scenario = get_scenario("aki_detection")
print(f"Loaded: {scenario.name}")

# Set up data backend and add patient data
backend = InMemoryBackend()
now = datetime.now()

backend.add_observation(123, "Cr", 1.0, now - timedelta(hours=6))
backend.add_observation(123, "Cr", 1.3, now - timedelta(hours=3))
backend.add_observation(123, "Cr", 1.8, now)

# Evaluate
evaluator = SinglePatientEvaluator(scenario, backend)
result = evaluator.evaluate(patient_id=123, reference_time=now)

if result.is_triggered:
    print(f"Alert: {result.triggered_logic}")

Define Your Own Scenario

scenario: AKI_Early_Detection
version: "0.3.0"

audit:
  intent: "Detect early acute kidney injury using creatinine trends"
  rationale: "Early AKI detection enables timely intervention"
  provenance: "KDIGO Clinical Practice Guideline for AKI (2012)"

signals:
  Cr:
    ref: creatinine        # Semantic reference (resolved via Dataset Spec)
    unit: mg/dL

trends:
  # v0.3: Trends produce numeric values only
  cr_delta:
    expr: delta(Cr, 6h)
    description: "Creatinine change over 6 hours"

  cr_current:
    expr: last(Cr)
    description: "Current creatinine value"

logic:
  # v0.3: Comparisons belong in logic layer
  cr_rising:
    when: cr_delta > 0.3
    description: "Rising creatinine"

  cr_high:
    when: cr_current > 1.5
    description: "Elevated creatinine"

  aki_risk:
    when: cr_rising AND cr_high
    severity: high
    description: "Early AKI - rising and elevated creatinine"
from psdl.core import parse_scenario

scenario = parse_scenario("my_scenario.yaml")
# or parse from string
scenario = parse_scenario(yaml_content)

Temporal Operators

Operator Example Description
delta delta(Cr, 6h) Change over time window
slope slope(HR, 1h) Linear trend (regression)
last last(Cr) Most recent value
min/max max(Temp, 24h) Min/max in window
sma/ema ema(BP, 2h) Moving averages
count count(Cr, 24h) Observation count

Window formats: 30s, 5m, 6h, 1d, 7d

Why PSDL?

Challenge Without PSDL With PSDL
Portability Logic tied to hospital systems Same scenario runs anywhere
Auditability Scattered across code/configs Single version-controlled file
Reproducibility Hidden state, implicit deps Deterministic execution
Compliance Manual documentation Built-in audit primitives

Links

License

Apache 2.0 - See LICENSE for details.