Bibliographic Details
| Title: |
Data Study Group Final Report: University College London Hospital - Morbidity Prediction Using Preoperative Cardiopulmonary Exercise Test Results |
| Authors: |
Data Study Group Team |
| Publisher Information: |
The Alan Turing Institute, 2025. |
| Publication Year: |
2025 |
| Subject Terms: |
Machine Learning, University College London Hospital, Cardiopulmonary Exercise Test, Predicting postoperative morbidities, Data Study Group, The Alan Turing Institute |
| Description: |
Data Study Groups are week-long events at The Alan Turing Institute bringing together some of the country’s top talent from data science, artificial intelligence, and wider fields, to analyse real-world data science challenges. The purpose of this Data Study Group (DSG) was to apply modern machine learning techniques to develop models predicting postoperative morbidities from pre-surgery Cardiopulmonary Exercise Test (CPET) data. The DSG objectives included: creating models that are more predictive and interpretable than existing CPET-based risk models; comparing different machine learning algorithms in terms of predictive performance and interpretability; and using these models to derive additional predictive features from CPET data. Data Study Group - September 2024 | The Alan Turing Institute |
| Document Type: |
Report |
| Language: |
English |
| DOI: |
10.5281/zenodo.15167430 |
| DOI: |
10.5281/zenodo.15167429 |
| Rights: |
CC BY |
| Accession Number: |
edsair.doi.dedup.....2b4a47a4c1eeda68929b898e13eea3c4 |
| Database: |
OpenAIRE |