IDA 2020
Wondering about the Call for Papers for IDA 2019?
IDA is moving from September to April, (one deadline less this spring!)
IDA 2020 will be held from April 27-29, 2020 in Konstanz, Germany!
You can expect the Call for Papers for IDA 2020 to appear in spring 2019.
Congratulations to:
Mashael Al Luhaybi for her paper “Predicting Academic Performance: A Bootstrapping Approach for Learning Dynamic Bayesian Networks” which has been accepted at AIED 2019
and to the following for their papers being accepted at IEEE CBMS 2019
Biraja Ghoshal for his paper “Estimating Uncertainty in Deep Learning for Reporting Confidence to Clinicians when Segmenting Nuclei Image Data”
Leila Yousefi and Mahir Arzoky for their paper “Opening the Black Box: Exploring Temporal Pattern of Type 2 Diabetes Complications in Patient Clustering Using Association Rules and Hidden Variable Discovery”
Bashir Dodo for his paper “Retinal OCT Segmentation Using Fuzzy Region Competition and Level Set Methods”
Steve Counsell, Stephen Swift, Mahir Arzoky and Giuseppe Destefanis for their poster / short paper “An Empirical Study of the AGIS Visual Field Metric and its Seasonal Variations ”
Awad Al Yousef for his poster / short paper “Latent Class Multi-Label Classification to Identify Subclasses of Disease for Improved Prediction”
Zenchen Wang, Puja Myles and Allan Tucker, “Generating and Evaluating Synthetic UK Primary Care Data: Preserving Data Utility & Patient Privacy”
Other recent conference acceptances:
Arianna Dagliati, Nophar Geifman, Niels Peek, John H. Holmes, Lucia Sacchi, Seyed Erfan Sajjadi, Allan Tucker, “Inferring Temporal Phenotypes with Topological Data Analysis and Pseudo Time-Series” at AIME 2019
and
Fatima Amer Jid Almahri, David Bell and Mahir Arzoky for her paper “Augmented education within a physical space” which has been accepted at UKAIS 2019
Looks like it will be a busy Summer!
There are EPSRC PhD scholarships currently available for topics including Data Analytics and Artificial Intelligence.
Deadline is 31 May 2019!
More information is here:
The IDA meeting held at WLFB 207/208 (2nd floor of Wilfred Brown) at 2:00PM.
Spotlight presentations from the following PhD students:
Afees Odebode: A sampling-based Clustering Scheme for Large Data Sets
Bashir Dodo: Level Set Segmentation of Retinal OCT Images
Ben Evans: Camera Trapping + ML
Joanna Pawlik: Extracting Predictive Models from Flora Free-Text Documents at The Royal Botanic Gardens, Kew, London
Khalipha Nuhu: Investigating user responses to mandatory IT-induced changes in organizations
Leila Yousefi: Opening the Black Box – Discovering Hidden Variables in Type II Diabetes Prediction and Patient Modelling
Mashael Al-Luhaybi: Predicting Academic Performance (Learning Dynamic Bayesian Networks)
A talk from Dr Noureddin Sadawi titled ‘Embarrassingly Parallel’ (slides can be found here).
Dr Sadawi is a research fellow at the Department of Computer Science, College of Engineering, Design and Physical Sciences, Brunel University London. His scientific research focuses on the applications of machine learning and data mining in areas such as drug design and discovery, omics data, gesture recognition, financial data analysis and object recognition.
Congratulations to Bashir Dodo for his paper on “Level set Segmentation of Retinal OCT Images” that has been shortlisted for Best Paper at BIOIMAGING 2019!
New paper accepted in Statistics and Computing on “Learning Bayesian Networks from Big Data with Greedy Search“.
IDA meeting held at WLFB 207/208 (2nd floor of Wilfred Brown) at 4:00PM after Opening the Black Box seminar series talk by Professor Niels Peek.
Talk from Leila Yousefi (Brunel University London)
Title: Opening the Black Box: Discovering and Explaining Hidden Variables in Type 2 Diabetic Patient Modelling
Authors: Leila Yousefi (Leila.Yousefi@brunel.ac.uk), Stephen Swift, Mahir Arzoky, Lucia Saachi, Luca Chiovato and Allan Tucker
Abstract: Clinicians predict disease and related complications based on prior knowledge and each individual patient’s clinical history. The prediction process is complex because of the existence of unmeasured risk factors, the unexpected development of complications, and varying responses of patients to the disease
over time. Exploiting hidden variables (i.e., unmeasured risk factors) can improve the modeling of disease progression and being able to understand the semantics of the hidden variables will enable clinicians to focus on the early diagnosis and treatment of unexpected conditions among sufferers. However, the overuse
of hidden variables can lead to complex models that can overfit and are not well understood (being ‘black box’ in nature). Identifying and understanding groups of patients with similar disease profiles (based on discovered hidden variables) makes it possible to better understand the manner of disease progression in different patients while improving prediction. Here, we explore the use of a stepwise method for incrementally identifying hidden variables based on the Induction Causation (IC*) algorithm. We exploit Dynamic Time Warping (DTW) and hierarchical clustering to cluster patients based upon these hidden variables to begin to uncover their meaning with respect to the complications of Type 2 Diabetes Mellitus (T2DM) patients. Our results reveal that inferring a small number of targeted hidden variables and using them to cluster patients not only leads to an improvement in the prediction accuracy but also assists the explanation of different discovered sub-groups.
Presentation slides can be found here and the paper transcript can be found here.
