coagulation-thrombosis-agent

<!--

COPYRIGHT NOTICE

This file is part of the "Universal Biomedical Skills" project.

Copyright (c) 2026 MD BABU MIA, PhD <md.babu.mia@mssm.edu>

All Rights Reserved.

#

This code is proprietary and confidential.

Unauthorized copying of this file, via any medium is strictly prohibited.

#

Provenance: Authenticated by MD BABU MIA

-->

---

name: 'coagulation-thrombosis-agent'

description: 'AI-powered analysis of coagulation disorders, thrombosis risk prediction, anticoagulation management, and platelet function assessment using machine learning.'

measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes.

allowed-tools:

• read_file
• run_shell_command

---

Coagulation and Thrombosis Agent

The Coagulation and Thrombosis Agent provides AI-driven analysis of hemostatic disorders, thrombosis risk assessment, and anticoagulation management. It integrates coagulation cascade modeling, platelet function analysis, and machine learning for personalized thrombosis prevention.

When to Use This Skill

• When assessing venous thromboembolism (VTE) risk in hospitalized patients.
• For anticoagulation dose optimization (warfarin, DOACs).
• To analyze coagulation panel results and identify bleeding/clotting disorders.
• For platelet morphology and function assessment.
• When managing thrombosis in myeloproliferative neoplasms (MPNs).

Core Capabilities

1. VTE Risk Prediction: Machine learning models predict deep vein thrombosis (DVT) and pulmonary embolism (PE) risk using clinical and laboratory features.

1. Anticoagulation Optimization: AI-guided dosing for warfarin (incorporating pharmacogenomics) and monitoring for DOACs.

1. Coagulation Cascade Analysis: Interprets PT, aPTT, fibrinogen, D-dimer, and specialized assays to diagnose coagulopathies.

1. Platelet Analysis: CNN-based morphology analysis predicting bleeding and thrombosis risk from peripheral smear images.

1. DIC Scoring: Automated disseminated intravascular coagulation (DIC) scoring and monitoring.

1. MPN Thrombosis Risk: Specialized models for thrombosis prediction in polycythemia vera, essential thrombocythemia.

Workflow

1. Input: Coagulation lab results, patient demographics, clinical risk factors, platelet images (optional).

1. Risk Assessment: Apply ML models for VTE, bleeding, or DIC risk scores.

1. Dosing Optimization: Generate anticoagulation recommendations.

1. Monitoring: Track INR/anti-Xa trends and alert on deviations.

1. Diagnosis: Pattern recognition for coagulation disorders.

1. Output: Risk scores, dosing recommendations, diagnostic suggestions, monitoring alerts.

Example Usage

User: "Calculate VTE risk for this hospitalized patient and optimize LMWH prophylaxis."

Agent Action:

python3 Skills/Hematology/Coagulation_Thrombosis_Agent/thrombosis_analyzer.py \
    --patient_data patient_demographics.json \
    --labs coagulation_panel.csv \
    --risk_model improved_padua \
    --anticoagulant lmwh \
    --renal_function egfr_45 \
    --output vte_assessment.json

Risk Models Implemented

| Model | Application | Key Features |

|-------|-------------|--------------|

| Padua (Enhanced) | Medical VTE risk | 11 clinical factors + ML enhancement |

| Caprini (AI) | Surgical VTE risk | 40+ factors with ML weighting |

| CHADS2-VASc | Atrial fibrillation stroke risk | Standard guideline scoring |

| HAS-BLED | Anticoagulation bleeding risk | Major bleeding prediction |

| IPSET-thrombosis | MPN thrombosis | JAK2, age, prior thrombosis |

Coagulation Panel Interpretation

| Test | Normal Range | Elevations Suggest | Decreases Suggest |

|------|--------------|-------------------|-------------------|

| PT/INR | 11-13.5s / 0.9-1.1 | Warfarin, VII def, liver disease | - |

| aPTT | 25-35s | Heparin, VIII/IX/XI def, lupus AC | - |

| Fibrinogen | 200-400 mg/dL | Acute phase, inflammation | DIC, liver disease |

| D-dimer | <500 ng/mL | VTE, DIC, inflammation | - |

| Platelet | 150-400K | Reactive, MPN | ITP, marrow failure |

AI/ML Components

Deep Learning for Platelet Morphology:

• CNN analysis of peripheral smear images
• Identifies giant platelets, platelet clumps, hypogranular forms
• Predicts bleeding/thrombosis risk from morphology

VTE Prediction Models:

• Gradient boosting (XGBoost) on structured EHR data
• Incorporates labs, vitals, medications, procedures
• AUC > 0.85 for hospital-acquired VTE

Anticoagulation Dosing:

• Reinforcement learning for INR control
• Pharmacogenomic integration (CYP2C9, VKORC1)
• Real-time dose adjustment recommendations

Prerequisites

• Python 3.10+
• scikit-learn, XGBoost, PyTorch
• HL7 FHIR client (for EHR integration)
• Image analysis libraries (for platelet morphology)

Related Skills

• Flow_Cytometry_AI - For platelet function assays
• Pharmacogenomics_Agent - For anticoagulant pharmacogenomics
• Blood_Smear_Analysis - For morphology assessment

Clinical Applications

1. Hospital VTE Prevention: Real-time risk scoring in EMR
2. Anticoagulation Clinic: AI-assisted warfarin dosing
3. DIC Management: Automated scoring and transfusion guidance
4. Inherited Disorders: Pattern recognition for factor deficiencies

Author

AI Group - Biomedical AI Platform

<!-- AUTHOR_SIGNATURE: 9a7f3c2e-MD-BABU-MIA-2026-MSSM-SECURE -->