CCAIM
Novel AI to transform healthcare
For our Revolutionizing Healthcare engagement series in conjunction with the van der Schaar Lab, we are inviting practicing clinicians and medical students to our next session on 10 October from 16:00-17:30 …
Over the course of several months, the Science and Technology Committee came together to hear evidence on the rapid advancement of AI, its influence on various aspects of our lives, …
CCAIM Faculty members will present 12 papers when Fortieth International Conference on Machine Learning (ICML 2023) takes place from 23 – 29 July, one of the most prominent annual gatherings of researchers, globally …
We are thrilled to announce that the Cambridge Centre for AI in Medicine (CCAIM) has successfully completed its initial stage, as evidenced by the positive outcomes highlighted in the recent ISAB report. Building …
In a guest article for The Guardian, released on 26 June, our director Prof Mihaela van der Schaar outlines how artificial intelligence could transform the way medical professionals treat diseases …
In an interview with BBC News Mundo, released on 14 June, our director Prof Mihaela van der Schaar spoke with journalist Margarita Rodríguez as part of an article on “3 …
This Friday (2 June 2023), our director Prof Mihaela van der Schaar will be joining the panel on “From chip to bedside – Oncology in silico R&D” as part of …
We introduce LEGATO, a hierarchical graph auto-encoder that learns a smaller, latent graph to dynamically aggregate information from multiple views.
SMC builds a new ensemble weighting existing models according to their likelihood to accurately represent a novel case. Based on our results, SMC is more robust and gives more accurate predictions than existing models.
The CCAIM researchers will publish 12 papers at ICML 2023, the leading international academic conference in machine learning.
Leadership Meet our directors, research engineers, and operational team You can find the Joint Steering Committee consisting of our academic and industrial leadership here.
This edition of the CCAIM AI Clinic will be held online in cooperation with the Revolutionizing Healthcare engagement program of the van der Schaar Lab Who is CCAIM? The Cambridge …
Affiliated clinical PhD Students Fellows in Clinical Artificial Intelligence
We are excited to announce our next WeCREATE session for 18 May from 16:00 – 17:00 BST (other timezones here)! This time, we will talk about Responsible AI. Our session …
We are thrilled to announce the release of our latest software packages, specifically designed to enhance your use of machine learning technology in a wide range of healthcare topics (e.g. …
AutoPrognosis is an automated predictive modelling pipeline designed for clinical prognosis, leveraging state-of-the-art advances in automated machine learning to optimise ML pipelines, incorporate model explainability tools, and enable deployment of …
TemporAI is a Machine Learning-centric time-series library tailored for medicine, focusing on time-series prediction, time-to-event analysis, and counterfactual inference for individualised treatment effects. How is it unique? TemporAI offers a …
HyperImpute is a comprehensive library for handling missing data in your ML pipelines, simplifying the selection process of a data imputation algorithm and offering a range of novel algorithms compatible …
Synthcity is an open-source synthetic data generation library that outperforms rivals (YData, Gretel, SDV, etc.) in terms of compatible use cases and data modalities, offering solutions for privacy, data scarcity, …
A comprehensive collection of Machine Learning interpretability methods, offering users a reference to select the best-suited method for their needs, with a focus on providing insights into ML model predictions …
CCAIM Faculty members will present eight papers at the Artificial Intelligence and Statistics (AISTATS) conference, one of the most prominent annual gatherings of researchers at the intersection of artificial intelligence, machine …
A paper recently accepted for publication by PLOS Digital Health explores machine-learning based prognostic scores and their validity when applied to demographically different patient cohorts: the ‘external validity’. The publication …
