358 citations · 1k across the 34 of their papers we have counts for
10 papers · 1 filter
A Reproducible Optimisation Protocol for Calibrating Prompt-Based Large Language Model Workflows in Evidence Synthesis
Teo Susnjak
This methods article presents a reproducible calibration workflow for prompt-based large language models (LLMs) in structured evidence-synthesis tasks. The method separates the rul…
When Predictions Shape Reality: A Socio-Technical Synthesis of Performative Predictions in Machine Learning
Gal Fybish, Teo Susnjak
Machine learning models are increasingly used in high-stakes domains where their predictions can actively shape the environments in which they operate, a phenomenon known as perfor…
Generalisation Bounds of Zero-Shot Economic Forecasting using Time Series Foundation Models
Jittarin Jetwiriyanon, Teo Susnjak, Surangika Ranathunga
This study investigates zero-shot forecasting capabilities of Time Series Foundation Models (TSFMs) for macroeconomic indicators. We apply TSFMs to forecasting economic indicators…
Transfer Learning on Transformers for Building Energy Consumption Forecasting -- A Comparative Study
Robert Spencer, Surangika Ranathunga, Mikael Boulic +2
This study investigates the application of Transfer Learning (TL) on Transformer architectures to enhance building energy consumption forecasting. Transformers are a relatively new…
Towards Clinical Prediction with Transparency: An Explainable AI Approach to Survival Modelling in Residential Aged Care
Teo Susnjak, Elise Griffin
Background: Accurate survival time estimates aid end-of-life medical decision-making. Objectives: Develop an interpretable survival model for elderly residential aged care resident…
Predicting Football Match Outcomes with eXplainable Machine Learning and the Kelly Index
Yiming Ren, Teo Susnjak
In this work, a machine learning approach is developed for predicting the outcomes of football matches. The novelty of this research lies in the utilisation of the Kelly Index to f…
Forecasting Patient Flows with Pandemic Induced Concept Drift using Explainable Machine Learning
Teo Susnjak, Paula Maddigan
Accurately forecasting patient arrivals at Urgent Care Clinics (UCCs) and Emergency Departments (EDs) is important for effective resourcing and patient care. However, correctly est…
A Prescriptive Learning Analytics Framework: Beyond Predictive Modelling and onto Explainable AI with Prescriptive Analytics and ChatGPT
Teo Susnjak
A significant body of recent research in the field of Learning Analytics has focused on leveraging machine learning approaches for predicting at-risk students in order to initiate…
Forecasting Patient Demand at Urgent Care Clinics using Machine Learning
Paula Maddigan, Teo Susnjak
Urgent care clinics and emergency departments around the world periodically suffer from extended wait times beyond patient expectations due to inadequate staffing levels. These del…
The Application of Machine Learning Techniques for Predicting Results in Team Sport: A Review
Rory Bunker, Teo Susnjak
Over the past two decades, Machine Learning (ML) techniques have been increasingly utilized for the purpose of predicting outcomes in sport. In this paper, we provide a review of s…