MediumTechnologyhealthcare

Symptom-Based Early UTI Screening Tool for Rural Healthcare

Added Nov 2025 3 design docs

Urinary tract infections are among the most common infections affecting women worldwide, yet in rural and underserved communities they often go unnoticed until they escalate into painful, costly complications. Seeing a clinician for an early assessment is not always practical, so a lightweight screening tool that works from symptom descriptions and basic health data can make a real difference in prompting timely care.

The intern builds a complete screening application around this need. A questionnaire-style interface built with streamlit collects symptoms such as burning sensation, urinary frequency, and fever alongside simple health indicators, and a machine-learning classifier written in python estimates the likelihood of a UTI from those inputs. A fastapi backend exposes the trained model as an inference service, validates incoming data, and keeps the interface cleanly separated from the prediction logic. The app also surfaces educational content on symptoms, prevention, and red flags that warrant an urgent doctor visit.

Completing the project takes the intern through the full arc of applied machine learning in healthcare: preparing training data, evaluating a classifier honestly, serving it behind an API, and presenting predictions responsibly as guidance rather than diagnosis.

Related projects

Cancer Detection API With SVM Kernel Selection and Tuning

Early detection changes cancer outcomes, and classical machine learning still earns its place in clinical decision support: Support Vector Machines remain strong performers on structured diagnostic measurements. This project builds a diagnostic prediction service with the engineering rigor a healthcare setting implies. The intern trains an SVM classifier with scikit-learn on diagnostic feature data, systematically comparing linear, polynomial, and RBF kernels and tuning hyperparameters such as C and gamma to balance sensitivity against overfitting, with the experiments documented in Jupyter. The tuned model is serialized and served through a FastAPI backend whose prediction endpoints validate input features and return class probabilities, and whose auto-generated Swagger UI allows interactive testing without writing a client. Throughout, the intern focuses on evaluation metrics appropriate for medical screening, where a false negative costs far more than a false positive. The project demonstrates disciplined model selection, kernel method fluency, and clean inference API design — showing the intern can carry a healthcare prediction task from experimentation all the way to a service another application could integrate.

healthcaredata-sciencefastapijupyter+3

PRD · HLD · LLD includedView

TechnologyHard

Explainable Segmentation and Diagnosis Platform for Medical Scans

Radiology departments face rising imaging volumes while specialist time remains scarce, and clinicians are understandably reluctant to trust opaque model outputs when reviewing X-rays and MRIs. What is needed is tooling that both accelerates review and shows its reasoning, so a human expert can verify every automated finding. The intern builds a web application in Streamlit where medical images are uploaded and processed live: a U-Net model produces semantic segmentation masks that outline anatomical structures or suspected lesions, while an EfficientNet classifier proposes a diagnostic label for the scan. Segmentation overlays are rendered directly on the source image with OpenCV, and an explainability dashboard surfaces confidence scores and saliency views so reviewers can see which regions influenced each prediction. Model work spans both PyTorch and TensorFlow, NumPy and pandas handle array manipulation and results tabulation, and a FastAPI layer exposes REST endpoints for batch inference across large scan archives. Docker packaging makes the stack reproducible for hospital IT or research deployment. The project demonstrates advanced skills in medical computer vision, multi-framework model serving, explainable AI presentation, and building clinical software that keeps the human decision-maker in control.

healthcareresearchcomputer-visiondockerefficientnet+9

PRD · HLD · LLD includedView

TechnologyMedium

Retrieval-Augmented Answer Engine for Clinical Reference Documents

General-purpose AI assistants are unusable for serious healthcare questions because their answers cannot be traced to authoritative sources. Clinics and health organizations hold exactly the documents that should ground such answers, including internal protocols, clinical references, and patient-education material, but need a system that confines the AI to that corpus. Retrieval-augmented generation is the architecture that makes this possible, and building it well over medical content is a benchmark skill for AI engineers. The intern builds a complete RAG application for the healthcare domain as a Python FastAPI service. The pipeline combines LangChain for orchestration with LlamaIndex for document ingestion and indexing: medical reference documents are chunked, embedded, and stored for semantic retrieval, and incoming questions trigger retrieval of the most relevant passages before generation. The system supports both OpenAI and Llama models as the generation backend, and the intern implements comparison plumbing to evaluate grounding quality across both. Answers carry citations back to source passages, and the intern tunes chunking, retrieval depth, and prompt constraints to minimize unsupported claims. The project demonstrates end-to-end RAG construction, dual-framework integration, model-backend flexibility, and the evidence-first mindset that domain-specific AI requires.

healthcarefastapigenerative-ailangchain+4

PRD · HLD · LLD includedView