bh-sentinel-core 0.1.2

Clinical decision support: pattern-based safety signal detection for behavioral health. Not a diagnostic tool.

bh-sentinel-core

Clinical decision support: pattern-based safety signal detection for behavioral health. Not a diagnostic tool.

Clinical Use Notice: bh-sentinel is clinical decision support software. It is not a diagnostic tool, not FDA-cleared, and not a substitute for clinical judgment. All outputs are signals for clinician review. See CLINICAL_DISCLAIMER.md.

bh-sentinel-core is the foundational library of the bh-sentinel project. It provides deterministic, regex-based safety signal detection with configurable rules, negation handling, temporal awareness, and an emotion lexicon.

Installation

pip install bh-sentinel-core

Dependencies

Minimal: pydantic, pyyaml. No ML libraries required.

What's Included

• Pattern Matcher -- compiled regex engine with negation and temporal awareness
• Rules Engine -- configurable severity escalation, de-escalation, and compound risk detection
• Flag Taxonomy -- 40 flags across 6 clinical domains (self-harm, harm to others, medication, substance use, clinical deterioration, protective factors)
• Text Preprocessor -- sentence splitting, normalization, and character offset tracking
• Negation Detector -- "denies SI", "no suicidal ideation" handling
• Temporal Detector -- past vs. present tense detection ("used to cut" vs. "is cutting")
• Emotion Lexicon -- project-owned behavioral health lexicon with 11-category density scoring
• Pipeline -- orchestrator that runs all layers and produces structured results

Quick Start

from bh_sentinel.core import Pipeline

pipeline = Pipeline()
result = pipeline.analyze_sync(
    "Patient reports suicidal ideation for the past two days. "
    "Stopped taking my medication last week."
)

for flag in result.flags:
    print(f"[{flag.severity}] {flag.name} (confidence: {flag.confidence})")

print(f"Immediate review: {result.summary.requires_immediate_review}")
print(f"Recommended: {result.summary.recommended_action}")

Adding the ML Layer

For semantic classification on top of pattern matching, install the companion bh-sentinel-ml package and enable Layer 2:

pip install bh-sentinel-ml

from bh_sentinel.core import Pipeline

pipeline = Pipeline(enable_transformer=True)  # auto-downloads pinned model on first run

bh-sentinel-core remains installable and usable on its own -- it has no runtime dependency on onnxruntime, tokenizers, or huggingface_hub. The ml imports are lazy-loaded only when enable_transformer=True.

Documentation

See the main repository for full documentation, architecture details, flag taxonomy reference, and the release process.

License

Apache License 2.0. See LICENSE for details.