--- name: coagulation-and-thrombosis-agent description: "---name: coagulation-thrombosis-agent description: AI-powered analysis of coagulation disorders, thrombosis risk prediction, anticoagulation management, and platelet function assessment using machine learning. license: MIT metadata: author: AI Group version: \"1.0.0\" created: \"2026-01-19\" compatib..." --- --- 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. 2. **Anticoagulation Optimization**: AI-guided dosing for warfarin (incorporating pharmacogenomics) and monitoring for DOACs. 3. **Coagulation Cascade Analysis**: Interprets PT, aPTT, fibrinogen, D-dimer, and specialized assays to diagnose coagulopathies. 4. **Platelet Analysis**: CNN-based morphology analysis predicting bleeding and thrombosis risk from peripheral smear images. 5. **DIC Scoring**: Automated disseminated intravascular coagulation (DIC) scoring and monitoring. 6. **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). 2. **Risk Assessment**: Apply ML models for VTE, bleeding, or DIC risk scores. 3. **Dosing Optimization**: Generate anticoagulation recommendations. 4. **Monitoring**: Track INR/anti-Xa trends and alert on deviations. 5. **Diagnosis**: Pattern recognition for coagulation disorders. 6. **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**: ```bash 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 --- Source attribution: mdbabumiamssm Original project: https://github.com/mdbabumiamssm/LLMs-Universal-Life-Science-and-Clinical-Skills-/blob/ef3c40b235ba23083e67263a921759784f0dd251/Skills/Hematology/Coagulation_Thrombosis_Agent/SKILL.md License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Dave AI. ## Upstream license notice MIT License Copyright (c) 2025 MD BABU MIA , PHD Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # Dave AI resource metadata Creator / publisher: mdbabumiamssm Original project: https://github.com/mdbabumiamssm/LLMs-Universal-Life-Science-and-Clinical-Skills-/blob/ef3c40b235ba23083e67263a921759784f0dd251/Skills/Hematology/Coagulation_Thrombosis_Agent/SKILL.md Attribution basis: Recovered original package and original project license at a pinned revision Fields of work: Data & Analytics, Healthcare & Life Sciences Roles: Clinical Research, Data Scientist, Health Informatics, Medical Writer Specialties: API, Testing Tools: Python Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: fa8a737edbec1945b142d86e3e2c1d59e8e62b1c4ded6c0aa4bef34b80fbbac5 Metadata reviewed: 2026-10-27 This metadata supersedes earlier Dave AI catalog attribution. Original instruction and supporting files are preserved.