The HotSauce
Team consisting of undergrad software/AI students skilled in Python, Java, JavaScript, React/Node.js, JavaFX and GitHub, building quant‑trading and AI resume‑screening projects.
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Project Description
Purpose:
NurseJoy.ai is an interactive healthcare assistant web application designed to streamline patient care, clinical decision-making, and public health analytics. It addresses inefficiencies in symptom diagnosis, appointment booking, medicine usage guidance, nutritional support, patient navigation, and social media health monitoring.
Target Users:
Patients: Quick symptom-based diagnosis, appointment booking, medicine info, nutrition advice, and hospital indoor navigation.
Clinicians: Upload patient reports to get data-driven care recommendations including secondary concerns and critical issue alerts.
Analysts: Process social media health data to generate threat alerts, credibility scoring, and urgency flags.
Workflow:
User registers and selects a role (Patient/Clinician/Analyst).
Patients input symptoms (via DrOak.ai) and receive preliminary diagnosis and assigned doctor; they can book visits and access medicine and nutrition modules.
Clinicians upload raw reports for automated analysis and suggested next steps.
Analysts upload social media datasets with metadata to extract actionable public health insights.
All users benefit from integrated geospatial apps built with Leafmap and Streamlit for enhanced hospital navigation and patient journey visualization.
Outcomes & Impact:
Improved patient engagement with AI-driven symptom assessment and booking.
Enhanced clinical decision support with automated report analysis.
Timely public health threat detection from social media data.
Improved hospital wayfinding reduces patient stress and delays.
Scalable platform adaptable to other healthcare settings.
Demo Steps:
Clone and install dependencies (pip install -r requirements.txt).
Run app with streamlit run app.py.
Register as Patient, Clinician, or Analyst.
Use respective modules: symptom input and booking (Patient), report upload and review (Clinician), social media data upload and alert generation (Analyst).
Explore indoor navigation via integrated geospatial maps.
Validation & Feedback:
Disease and symptom datasets sourced from Kaggle and proprietary collections improve diagnosis accuracy.
User feedback highlights ease of use, helpful AI insights, and navigation support.
Clinician users report valuable secondary concern detection assisting care planning.
Judging Criteria Alignment
Innovation: Our Core Features will be Track2 but we also integrated basic features for Track 1 and Track 2. Combines AI-driven diagnosis, social media analytics, and geospatial navigation uniquely in one platform.
Feasibility: Built entirely with stable, widely adopted technologies; tested with real datasets.
Technical Merit: Integrates advanced LLM (Jamaibase), data analytics, and interactive web UI seamlessly.
User Benefit: Tailored modules for distinct user roles improve healthcare experience and outcomes.
Deployability: Streamlit-based web app is lightweight, cloud-ready, and easily maintainable.
Technology Stack
Data: Kaggle datasets for diseases, symptoms, and social media health data
AI / LLM: Jamaibase for natural language understanding, symptom diagnosis, and summarization
Frontend: Streamlit for rapid, interactive web app development
Geospatial: Leafmap to build interactive hospital navigation and patient journey visualizations
Others: Python 3.10 environment, additional libraries as listed in requirements.txt
For further information or support, please contact the HotSauce.
Thank you for considering NurseJoy.ai!
Prior Work
Travel Itenary with DeepSeek ai integration coded with Java