Chris Browne

Mehdi Neshat, Michael Phipps, Chris A. Browne et al.

Predicting hospital length of stay (LoS) stands as a critical factor in shaping public health strategies. This data serves as a cornerstone for governments to discern trends, patterns, and avenues for enhancing healthcare delivery. In this study, we introduce a robust hybrid deep learning model, a combination of Multi-layer Convolutional (CNNs) deep learning, Gated Recurrent Units (GRU), and Dense neural networks, that outperforms 11 conventional and state-of-the-art Machine Learning (ML) and Deep Learning (DL) methodologies in accurately forecasting inpatient hospital stay duration. Our investigation delves into the implementation of this hybrid model, scrutinising variables like geographic indicators tied to caregiving institutions, demographic markers encompassing patient ethnicity, race, and age, as well as medical attributes such as the CCS diagnosis code, APR DRG code, illness severity metrics, and hospital stay duration. Statistical evaluations reveal the pinnacle LoS accuracy achieved by our proposed model (CNN-GRU-DNN), which averages at 89% across a 10-fold cross-validation test, surpassing LSTM, BiLSTM, GRU, and Convolutional Neural Networks (CNNs) by 19%, 18.2%, 18.6%, and 7%, respectively. Accurate LoS predictions not only empower hospitals to optimise resource allocation and curb expenses associated with prolonged stays but also pave the way for novel strategies in hospital stay management. This avenue holds promise for catalysing advancements in healthcare research and innovation, inspiring a new era of precision-driven healthcare practices.

Ehsan Nabavi, Chris Browne

There is a growing debate amongst academics and practitioners on whether interventions made, thus far, towards Responsible AI would have been enough to engage with root causes of AI problems. Failure to effect meaningful changes in this system could see these initiatives to not reach their potential and lead to the concept becoming another buzzword for companies to use in their marketing campaigns. We propose that there is an opportunity to improve the extent to which interventions are understood to be effective in their contribution to the change required for Responsible AI. Using the notions of leverage zones adapted from the 'Systems Thinking' literature, we suggest a novel approach to evaluate the effectiveness of interventions, to focus on those that may bring about the real change that is needed. In this paper we argue that insights from using this perspective demonstrate that the majority of current initiatives taken by various actors in the field, focus on low-order interventions, such as short-term fixes, tweaking algorithms and updating parameters, absent from higher-order interventions, such as redefining the system's foundational structures that govern those parameters, or challenging the underlying purpose upon which those structures are built and developed in the first place(high-leverage). This paper presents a conceptual framework called the Five Ps to identify interventions towards Responsible AI and provides a scaffold for transdisciplinary question asking to improve outcomes towards Responsible AI.

Chris Browne

Changes in the statistical properties of a stochastic process are typically assumed to occur via change-points, which demark instantaneous moments of complete and total change in process behavior. In cases where these transitions occur gradually, this assumption can result in a reduced ability to properly identify and respond to process change. With this observation in mind, we introduce a novel change-dynamic model for the online detection of gradual change in a Bayesian framework, in which change-points are used within a hierarchical model to indicate moments of gradual change onset or termination. We apply this model to synthetic data and EEG readings drawn during epileptic seizure, where we find our change-dynamic model can enable faster and more accurate identification of gradual change than traditional change-point models allow.