Title: Developing a Question-Answer Matching System for Patient Triage and Expert Routing in a Healthcare Platform

Description:

This project focuses on building a smart, machine learning-based system that automatically matches incoming patient questions with previously answered questions or routes them to the most suitable doctor based on their specialization and the disease category. The system aims to reduce response time, avoid repetition, and improve accuracy in patient support workflows.

Key components include:

Implementation of a semantic similarity model (using embeddings or transformers) to compare new queries with historical question-answer pairs.
A specialty-matching algorithm that evaluates the content of the question and assigns it to the most relevant medical expert (e.g., oncologist, neurologist).
Design of a real-time triage engine integrated into the existing question-answer workflow to improve automation and response efficiency.
Focus on data privacy and ethical handling of sensitive patient queries throughout the pipeline.

Skill Sets:

Python, Scikit-learn, TensorFlow/PyTorch
NLP (BERT, Sentence Transformers)
Knowledge of classification and similarity metrics
Database integration & RESTful APIs
Optional: Experience with healthcare ontologies or domain-specific embeddings

Expected Learning:

Building NLP models for semantic search and classification
Designing real-world patient triage systems
Handling sensitive healthcare data securely
Integrating ML models into live healthcare applications

Specific Courses Required for Project Execution:

Natural Language Processing
Machine Learning / Deep Learning
Database Systems / Software Engineering

IAMSTILLALIVE ONCO ECOSYSTEM PVT LTD

Station Overview

Project 1

Community Post Risk Detector for Patient Forums

Project 2

Developing a Question-Answer Matching System for Patient Triage and Expert Routing in a Healthcare Platform