Jonathan M. Garibaldi

Background: Predicting respiratory trajectory and prognosis of high-risk preterm infants could better support targeted interventions and parental counselling. Current models are static, limiting their utility. They provide single day predictions at 36 weeks postmenstrual age, bounded by fixed timepoints, without incorporating clinical trajectory. We aimed to develop and validate a dynamic model for predicting respiratory state trajectory and demonstrate utility in predicting successful extubation and postnatal corticosteroids use. Methods: A dynamic model based on a Markov chain modelling framework was developed and validated in a retrospective population-based National Neonatal Research Database cohort before external validation in a prospective PRemature Infant Outcome Risk cohort. Both cohorts consist of infants born <32 weeks’ gestation in participating units in England and Wales from 2010-2022 (n=93,342) and 2023-2024 (n=1,420), respectively. Fourteen static once only and six daily predictors were used. Missing data was addressed using mean and model-based imputation, respectively. The dynamic model predicted daily respiratory states (no support, supplemental oxygen only, non-invasive or invasive ventilation) or death. Discrimination (area under the receiver operating characteristic curve (AUROC)), calibration, and utility performance were assessed. Findings: The developed online dynamic model (https://premoutcome.shinyapps.io/Respiratory/) showed strong discrimination (AUROCs 0·82–0·84), calibration and utility performance, demonstrating superior net benefit across probabilities of 30%-80%, in predicting bronchopulmonary dysplasia or death during external validation. The dynamic model had excellent performance in predicting non-invasive/invasive ventilation or death within the next 7 or 14 days (AUROCs 0·89-0·96). Infants with predicted risk of poorer respiratory outcomes were more likely to remain intubated and receive postnatal dexamethasone (p<0·001). Interpretation: The dynamic model demonstrated strong performance in predicting the complex task of daily respiratory trajectory and clinical utility in predicting likelihood of extubation timing. Further external validation and impact analysis are needed to establish personalised prediction utility.

It is a great honour and privilege for me to take on the role of Vice President for Publications of the IEEE Computational Intelligence Society (CIS), following on from my illustrious predecessors Profs. KC Tan and Jim Keller. Publications are one of the flagship activities of our Society and the publications portfolio now includes the IEEE Computational Intelligence Magazine, seven IEEE Transactions on Neural Networks and Learning Systems, on Fuzzy Systems, on Evolutionary Computation, on Emerging Topics in Computational Intelligence, on Artificial Intelligence, on Cognitive and Developmental Systems, and on Games, and the IEEE Press/Wiley Book Series on Computational Intelligence. More information about each of these publications can be found at https://cis.ieee.org/publications.

The rules of a rule-based system provide explanations for its behaviour by revealing the relationships between the variables captured. However, ideally, we have AI systems which go beyond explainable AI (XAI), that is, systems which not only explain their behaviour, but also communicate their ‘insights’ in respect to the real world. This requires rules to capture causal relationships between variables. In this paper, we argue that those systems where the rules reflect causal relationships between variables represent an important class of fuzzy rulebased systems with unique benefits. Specifically, such systems benefit from improved performance and robustness; facilitate global explainability and thus cater to a core ambition for AI: the ability to communicate important relationships amongst a system's real-world variables to the human users of AI. We establish two causal-rule focused approaches to designing fuzzy systems, and show the distinctions in their respective application scenarios for the explanations of the rules obtained by these two methods. The results show that rules which reflect causal relationships are more suitable for XAI than rules which ‘only’ reflect correlations, while also confirming that they offer robustness to over-fitting, in turn supporting strong performance.

DNA computing is a highly interdisciplinary field which combines molecular operations with theoretical algorithm design. A number of algorithms have been demonstrated in DNA computing, but to date network flow problems have not been studied. We aim to provide an approach to calculate the value of the maximum flow in networks by encoding the mathematical problem in DNA molecules and by using molecular biology techniques to manipulate the DNA. We present results which demonstrate that the algorithm works for an example network problem. This paper presents the first application of DNA computing to network-flow problems. The presented algorithm has a linear time complexity where the calculation itself is done in a constant number of steps.

Deep learning models have achieved high performance in numerous semantic segmentation tasks. However, when the input data at test time do not resemble the training data, deep learning models can not handle them properly and will probably produce poor results. Therefore, it is important to design algorithms for deep learning models to reliably detect out-of-distribution (OOD) data. In this paper, we propose a novel fuzzy-uncertainty-based method to detect OOD samples for semantic segmentation. Firstly, to capture both data and model uncertainties, test-time augmentation and Monte Carlo dropout are applied to a ready-trained image segmentation model for generating multiple predicted instances of a given test image. Then interval fuzzy sets are generated from these multiple predictions to describe the captured uncertainty via distance transform operators. Finally, an image-level uncertainty score, which is calculated from the generated interval fuzzy sets, is used to indicate if it is an OOD sample. Experiments on testing three OOD test sets on a skin lesion segmentation model show that our proposed method achieved significantly higher classification accuracy in detecting OOD samples than three other state-of-the-art uncertainty-based algorithms.

Currently, type-2 fuzzy systems, fuzzy control strategies and origami structures have been used in robots to assist in the comfortable and smooth operation of such robots. Recent advances in these various fields have been employed to improve the key technologies of wearable robots, such as providing more efficient decision making, improved maneuverability, increased control intelligence and more lightweight structures. The current advances have highlighted the potential for these various methods, both separately and in combination with each other, to achieve further significant advances. Hence, this paper summarizes the latest research results in these three key aspects, elaborates on some of the challenges that remain and discusses potential development directions of wearable robots in the future.