ACUHIT

Background

One of the most critical bottlenecks in modern healthcare is the inability to holistically analyze heterogeneous patient data. Lab results, vital signs, and free-text clinical notes are rarely processed together — yet together they tell the full story of a patient's health trajectory. Existing scoring systems remain largely static, limited in parameters, and disconnected from real-time clinical realities. The Challenge This competition invites participants to tackle three interconnected problems:

1. Unstructured Data Processing Using Natural Language Processing (NLP) techniques, extract meaningful clinical features from scattered doctor notes, nurse observations, and discharge (epikriz) reports.
2. Dynamic Health Scoring Through literature review and data analytics, define new-generation health parameters that are mathematically meaningful and clinically valid — parameters that represent a patient's overall health status or systemic functions beyond what current scoring systems capture.
3. Clinical & Operational Prediction Using machine learning and AI, predict:

Patient prognosis Length of Stay (LOS) Examination and procedure needs Hospital resource allocation requirements

What We Expect From You Participants are not only expected to write code — they are expected to pioneer the digital representation of human health by translating medical literature into mathematical models. The best submissions will demonstrate:

Rigorous data preprocessing and feature engineering Clinically grounded and interpretable health scoring approaches Strong predictive performance on the provided dataset Clear documentation and reproducibility