Project duration:
Sept 2019 - Dec 2021
machine learning hip fractures
Machine Learning for Enhanced Prediction of Early Mortality In Hip Fracture Patients
Artificial Intelligence (AI) is playing an increasingly important role in health care. To be able to more accurately determine the risk for early mortality after a hip fracture, this project investigates how to further develop machine learning, a subarea of AI, so that it can predict early mortality with sufficient accuracy for the purpose of a support tool in the dialogue with patient and family regarding the decision on whether to operate the patient or not. In particular, we investigate techniques for multi-modal machine learning, combining data from different data types such as images, fields, natural language texts and sensor readings, for the purpose of discovering new variables that are related to early mortality. We also intend to pay attention to the transferability of this technology by investigating its use for other medical conditions and diagnosis/treatment decisions in other departments within ZGT.
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