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| title: Pediatric Pulmonology Chatbot | |
| emoji: 🫁 | |
| colorFrom: blue | |
| colorTo: green | |
| sdk: gradio | |
| sdk_version: 5.40.0 | |
| app_file: app.py | |
| pinned: false | |
| license: mit | |
| # Pediatric Pulmonology Chatbot | |
| An advanced AI-powered educational tool leveraging machine learning to help understand childhood respiratory conditions through natural language processing. | |
| ## Purpose | |
| This intelligent chatbot helps parents and caregivers understand various pediatric pulmonology conditions. It uses state-of-the-art machine learning models to interpret natural language descriptions of symptoms and provide comprehensive educational information about respiratory conditions in children. | |
| ## AI-Powered Features | |
| ### Advanced Natural Language Understanding | |
| - Bio-Clinical BERT Integration: Medical domain-specific language model for accurate symptom interpretation | |
| - TF-IDF Similarity Matching: Finds the most relevant conditions based on symptom descriptions | |
| - Multi-layered Classification: Combines ML models with enhanced rule-based systems | |
| - Confidence Scoring: Provides reliability indicators for each prediction | |
| - Flexible Input Processing: Understands various ways of describing symptoms | |
| ### Smart Response Generation | |
| - Context-Aware Analysis: Considers multiple symptoms and their relationships | |
| - Confidence-Based Responses: Adapts advice based on prediction certainty | |
| - Educational Content Delivery: Structured medical information presentation | |
| - Progressive Disclosure: Asks for more details when needed for better accuracy | |
| ## Medical Conditions Covered | |
| ### Common Pediatric Respiratory Conditions | |
| - Asthma - Chronic airway inflammation and bronchospasm | |
| - Bronchiolitis - Viral infection affecting small airways in infants | |
| - Pneumonia - Bacterial or viral lung infection | |
| - Chronic Cough - Persistent cough lasting >4 weeks | |
| ### Specialized Pulmonology Conditions | |
| - Paradoxical Vocal Fold Movement (PVFM) - Vocal cord dysfunction | |
| - Subglottic Stenosis - Airway narrowing below vocal cords | |
| - Acute Respiratory Distress Syndrome (ARDS) - Severe lung inflammation | |
| - Tracheoesophageal Fistula - Abnormal connection between airways | |
| - Laryngeal Web - Congenital vocal cord membrane | |
| - Primary Ciliary Dyskinesia - Genetic ciliary dysfunction | |
| - Pulmonary Arterial Hypertension - Elevated lung blood pressure | |
| - Hereditary Hemorrhagic Telangiectasia - Genetic vascular disorder | |
| - Esophageal Atresia - Congenital esophageal malformation | |
| - Asbestosis - Occupational lung disease (rare in children) | |
| ## Advanced Capabilities | |
| ### Natural Language Processing | |
| With rule-based logic: "My child wheezes" (exact match required) | |
| With ML model: "Kid sounds funny when breathing and gets tired easily" | |
| ### Intelligent Symptom Analysis | |
| - Multi-symptom Recognition: Processes complex symptom combinations | |
| - Age-Appropriate Assessment: Considers the child's developmental stage | |
| - Severity Indicators: Identifies urgent vs. routine symptoms | |
| - Pattern Recognition: Detects symptom clusters and relationships | |
| ### Confidence-Based Responses | |
| - High Confidence (>60%): Detailed condition information | |
| - Moderate Confidence (30-60%): Condition info with caveats | |
| - Low Confidence (<30%): Requests more specific information | |
| ## How to Use Effectively | |
| ### Optimal Input Examples: | |
| - Good: "My 3-year-old has been wheezing at night and coughs when running around" | |
| - Better: "Baby has stuffy nose, breathing fast, and won't finish bottles for 3 days" | |
| - Best: "Toddler making high-pitched breathing sounds, gets tired easily, voice sounds different" | |
| ### Information to Include: | |
| - Child's age (infant, toddler, school-age) | |
| - Specific symptoms (wheezing, cough, fever, breathing changes) | |
| - Duration and timing of symptoms | |
| - Triggers or patterns you've noticed | |
| - Impact on daily activities (feeding, sleep, play) | |
| ## Technical Architecture | |
| ### Machine Learning Stack | |
| - Primary Model: Bio-Clinical BERT (emilyalsentzer/Bio_ClinicalBERT) | |
| - Fallback Model: PubMed BERT (microsoft/BiomedNLP-PubMedBERT) | |
| - Similarity Engine: TF-IDF with Cosine Similarity | |
| - Enhanced Rules: Multi-keyword scoring system | |
| - Framework: Hugging Face Transformers + scikit-learn | |
| ## Performance Metrics | |
| ### Model Capabilities | |
| - **Language Flexibility**: Handles 90%+ of natural symptom descriptions | |
| - **Accuracy**: High precision for well-described symptoms | |
| - **Coverage**: 14 major pediatric pulmonology conditions | |
| - **Response Time**: <3 seconds average processing time | |
| - **Confidence Calibration**: Reliable uncertainty quantification | |
| ### Supported Input Variations | |
| - **Incomplete sentences**: "baby breathing funny" | |
| - **Medical terms**: "stridor, tachypnea, dyspnea" | |
| - **Lay descriptions**: "sounds wheezy", "can't catch breath" | |
| - **Multiple symptoms**: Complex symptom combinations | |
| - **Contextual details**: Age, triggers, duration | |
| ## Medical Disclaimer & Safety | |
| This AI tool is designed for educational purposes only and is NOT a substitute for professional medical advice. | |
| ### Always Consult Healthcare Providers For: | |
| - **Diagnosis and Treatment**: Professional medical evaluation required | |
| - **Medication Decisions**: Prescription and dosing guidance | |
| - **Urgent Symptoms**: Any concerning changes in child's condition | |
| - **Follow-up Care**: Monitoring and management plans | |
| ### Seek Immediate Emergency Care For: | |
| - Severe breathing difficulty or inability to breathe | |
| - Blue coloration of lips, face, or fingernails (cyanosis) | |
| - Loss of consciousness or extreme lethargy | |
| - Inability to speak or cry due to breathing problems | |
| - High fever with severe breathing distress | |
| ## Technical Resources | |
| ### Medical Knowledge Sources | |
| Data gathered from the following data source | |
| - Mayo Clinic — https://www.mayoclinic.org | |
| - PubMed Central (PMC) — https://pmc.ncbi.nlm.nih.gov | |
| - National Institutes of Health (NIH) | |
| - Peer-reviewed Pediatrics and Pulmonology Journals | |
| - Clinical guidelines and educational materials from pediatric pulmonology societies | |
| - Additional reputable medical platforms including Medscape and Cleveland Clinic | |
| ### Model Documentation | |
| - **Bio-Clinical BERT**: Specialized medical language understanding | |
| - **TF-IDF Similarity**: Symptom pattern matching algorithms | |
| - **Confidence Scoring**: Uncertainty quantification methods | |
| - **Response Templates**: Structured medical information delivery | |
| ## Development Team | |
| Team 3 - Pediatric Pulmonology Chatbot Project | |
| - Leslie El | |
| - Jennifer imogie | |
| - Barakat Abubakar | |
| --- | |
| **Remember: When in doubt about a child's breathing or respiratory symptoms, always be on the side of caution and seek immediate medical evaluation.** | |