SepsisAI

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  AI-Powered Precision for Life-Saving Decisions.

Inspiration: Sepsis is a leading cause of mortality in hospitals worldwide, often due to delayed diagnosis and intervention. Early detection and real-time monitoring are crucial to reducing sepsis-related fatalities. Inspired by the potential of AI in healthcare, we developed SepsisAI, an advanced machine-learning-powered prediction system that can analyze patient data, identify sepsis risk early, and provide real-time alerts to medical professionals.

What It Does; SepsisAI is designed to predict and monitor sepsis using real-time electronic health records (EHR) and machine learning models. It processes patient vitals and lab data from FHIR-integrated EHR systems to assess sepsis risk dynamically. The system: • Utilizes AI models (LSTM, XGBoost, Transformer-based networks) to detect early signs of sepsis. • Sends real-time alerts to clinicians via email, SMS, and web notifications. • Provides a dashboard with interactive visualizations for patient risk assessment. • Uses SHAP/LIME-based AI explainability to provide transparent insights into predictions. • Features a user-friendly web interface where healthcare providers can monitor and manage patients efficiently.

How We Built It: SepsisAI is a full-stack AI-powered solution, structured into several key components: • Backend: Developed using FastAPI (Python), processing patient data, handling authentication, and managing ML model predictions. • Frontend: Built with HTML, CSS, and Flask, ensuring an intuitive, easy-to-navigate user experience. • Machine Learning Pipeline: Trained using real-world patient datasets, utilizing Keras/TensorFlow for deep learning models and scikit-learn for feature engineering. • Database: Stores patient data, alerts, and prediction results securely using SQLite/PostgreSQL. • Deployment: Containerized with Docker, designed to scale on cloud environments like AWS/GCP.

Challenges We Ran Into: Developing SepsisAI came with several challenges, including ensuring real-time processing of patient vitals, fine-tuning AI models to minimize false positives, and integrating FHIR APIs securely. Managing secure authentication while maintaining HIPAA compliance was also a complex task. Designing an efficient alerting mechanism that notifies doctors without causing unnecessary alarm was another critical aspect of the project.

Accomplishments That We’re Proud Of: One of the major achievements of SepsisAI is successfully integrating machine learning with real-time EHR data. The dashboard’s intuitive design ensures that medical professionals can easily access AI-driven predictions, while the early warning system has demonstrated high accuracy in detecting sepsis risk before symptoms escalate. Additionally, we streamlined the training pipeline using train_model.py, allowing for efficient model retraining with updated patient datasets.

What We Learned: This project deepened our understanding of healthcare interoperability, AI in predictive diagnostics, and cloud-based deployments. We gained hands-on experience in FHIR integration, real-time data processing, and optimizing deep learning models for clinical applications. The project also reinforced the importance of explainable AI (XAI) in healthcare to ensure trust and reliability.

What’s Next for SepsisAI?: Moving forward, we aim to expand SepsisAI’s capabilities by: • Integrating wearable IoT devices (smartwatches, patient monitoring systems) for continuous data collection. • Enhancing the machine learning model with federated learning, allowing for collaborative improvements across hospitals while ensuring data privacy. • Developing a mobile app version for doctors and nurses to receive real-time alerts. • Implementing predictive analytics for ICU mortality risk, helping medical teams manage critically ill patients more effectively.

SepsisAI represents the future of AI-driven clinical decision support, combining machine learning, real-time monitoring, and predictive analytics to enhance patient care and save lives.

Built With

• aws/gcp
• backend:-fastapi
• css
• hapi-fhir-?-deployment:-docker
• html
• javascript-?-machine-learning:-tensorflow/keras
• python
• scikit-learn
• shap/lime-?-fhir-integration:-google-cloud-healthcare-api
• sqlite/postgresql-?-frontend:-flask