Introduction:
Pulmonary exacerbations are critical events in paediatric patients with asthma or cystic fibrosis (CF). These exacerbation events are often associated with sudden health deterioration and increased healthcare burden. The early prediction of exacerbations events could allow for timely interventions, and thus improved patient outcomes. This thesis attempted to develop a machine learning (ML) model to predict pulmonary exacerbations before they occur in a paediatric population using remote patient monitoring (RPM) data.

Methods:
A retrospective study was conducted using continuous data from wearable devices, daily spirometry, environmental data, and patient-reported outcomes. Predictions were focused on the occurrence of an exacerbation within three prediction windows (1-day, 3-day, and 7-day). Two ML approaches were considered: anomaly detection (using Gaussian mixture model, Isolation forest, One-class-SVM, and Local outlier factor), and classification models (Logistic regression, Random forest), using 5-fold nested cross-validation. Time-related transformations were performed to capture the temporal dependency of time-series data, including the feature engineering of clinical features related to heart rate and physical activity.

Results:
A total of 2401 home monitoring days of 90 paediatric patients, with 10 observed exacerbation events were included in the analysis. All models struggled to achieve high predictive value, with PR-AUC values below 0.20 and ROC-AUC values ranging from 0.43 to 0.72 across different time windows. No single model consistently outperformed the others. Despite the low performance, the models demonstrated better than random prediction for secondary outcomes, such as weekends and holidays, suggesting the ability to capture patterns in the data.

Conclusion:
This thesis shows the potential and limitations of using ML techniques for predicting pulmonary exacerbations using RPM data. The current anomaly detection and classification model performances are insufficient for clinical application. The low incidence of exacerbation events and the limitations in data quality contribute to these results. These findings point to the need for further refinement and more robust datasets to fully realise the potential of ML in the context of predicting pulmonary exacerbations.