Cambridge Mathematics of Information in Healthcare |

Recent methodological advances in federated learning for healthcare

24 June 2024

Federated learning (FL) promises to solve the challenges of applying machine learning methods within healthcare, such as isolated datasets, ethical, privacy, and logistical concerns with data sharing, and the lack of diversity in single-center datasets. By connecting multiple sites and keeping data at their source, FL can...

The curious case of the test set AUROC

4 April 2024

The area under the receiver operating characteristic curve (AUROC) is a staple within machine learning for reporting model performance and assessing model generalisability. However, CMIH researchers demonstrate that reporting the AUROC alone for a test set masks not only domain shift between validation and test data but...

The impact of imputation quality on machine learning classifiers for datasets with missing values

6 October 2023

Classifying samples in incomplete datasets is a common aim for machine learning practitioners, but is non-trivial. Missing data is found in most real-world datasets and these missing values are typically imputed using established methods, followed by classification of the now complete samples. The focus of the machine...

Integrating Artificial Intelligence Tools in the Clinical Research Setting: The Ovarian Cancer Use Case

25 September 2023

Artificial intelligence (AI) methods applied to healthcare problems have shown enormous potential to alleviate the burden of health services worldwide and to improve the accuracy and reproducibility of predictions. In particular, developments in computer vision are creating a paradigm shift in the analysis of radiological...

About Us

The Cambridge Mathematics of Information in Healthcare Hub (CMIH) is a collaboration between mathematics, statistics, computer science and medicine, aiming to develop robust and clinically practical data analytics algorithms for healthcare decision making. Our work focusses on some of the most challenging public health problems; Cancer, Cardiovascular Disease, and Dementia.

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Cambridge Mathematics of Information in Healthcare

Recent methodological advances in federated learning for healthcare

The curious case of the test set AUROC

The impact of imputation quality on machine learning classifiers for datasets with missing values

Integrating Artificial Intelligence Tools in the Clinical Research Setting: The Ovarian Cancer Use Case

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