Neutrophil count prediction in childhood cancer patients receiving 6-mercaptopurine chemotherapy treatment

Acute Lymphoblastic Leukaemia (ALL) is a common form of blood cancer, usually affecting children under 15 years of age. Chemotherapy treatment for ALL is delivered in three phases viz. induction (to achieve initial remission), intensification (to kill the majority of abnormal cells), and finally, maintenance. The maintenance phase involves oral administration of the chemotherapy drug 6-Mercaptopurine (6-MP) in varying doses to destroy any remaining abnormal cells and prevent reoccurrence. A key side effect of the treatment is a reduction in neutrophil counts that can result in a condition known as neutropenia, i.e. reduced immune system. This carries a risk of secondary infection and has been linked to 60% of ALL fatalities. Current practice aims to control neutrophil counts by varying 6-MP dosages on a weekly basis based on blood counts. However, its success is varied. This thesis proposes a number of intelligent prediction methods to more accurately predicting neutrophil counts one week ahead using blood count data and corresponding 6-MP dosing regimens. Firstly, a well-known and robust neural network (Nonlinear Autoregressive Exogenous) is applied to blood count data to provide an initial assessment of the feasibility of such an approach. A comparative analysis of a series of more complex algorithms is then considered for more advanced, in-depth analysis viz. Multi-Layer Perceptron (MLP) and Support Vector Machines (SVM). Both methods are shown to have a prediction accuracy of around 60% on the first sample period, with the MLP also having a prediction accuracy of more than 60% in the second sample period in seven out of ten blood data points (there was 10 timeseries blood data predictions). However, in comparison the accuracy of SVM is relatively low. Finally, an incremental learning-based approach is proposed to increase the accuracy of the system and provide a realistic framework for real-time implementation. The accuracy is shown to improve considerably as more data is added, and the predicted neutrophils data is shown to follow the trend of the actual neutrophil counts

Neutrophil count prediction for personalized drug dosing in childhood cancer patients receiving 6-mercaptopurine chemotherapy treatment

Acute Lymphoblastic Leukaemia (ALL) is a common form of blood cancer that usually affects children under 15 years of age. Chemotherapy treatment for ALL is delivered in three phases viz. induction, intensification, and maintenance. The maintenance phase involves oral administration of the chemotherapy drug 6-Mercaptopurine (6-MP) in varying doses to destroy any remaining abnormal cells and prevent reoccurrence. A key side effect of the treatment is a reduction in neutrophil counts which can lead to a condition known as neutropenia. This carries a risk of secondary infection and has been linked to 60% ALL fatalities. Current practice aims to control neutrophil counts by varying 6-MP dosages on a weekly basis and is based upon clinical judgment and experience of the medical professionals involved. Conceived as a decision support aid for clinicians then, presented are the results of a machine learning technique that predicts neutrophil counts one or more weeks ahead using data from ALL blood test results and 6-MP dosing. In this work, a model is trained and validated using data from a single female ALL patient’s maintenance phase. The prediction error is found to be typically within +/- 290/microL at one week and within +/- 820/microL for a 14 day prediction

Laser surface engineering of polymeric materials and the effects on wettability characteristics

Wettability characteristics are believed by many to be the driving force in applications relating to adhesion. So, gaining an in-depth understanding of the wettability characteristics of materials before and after surface treatments is crucial in developing materials with enhanced adhesion properties. This chapter details some of the main competing techniques to laser surface engineering followed by a review of current cutting edge laser surface engineering techniques which are used for wettability and adhesion modulation. A study is provided in detail for laser surface treatment (using IR and UV lasers) of polymeric materials. Sessile drop analysis was used to determine the wettability characteristics of each laser surface treated sample and as-received sample, revealing the presence of a mixed-state wetting regime on some samples. Although this outcome does not follow current and accepted wetting theory, through numerical analysis, generic equations to predict this mixed state wetting regime and the corresponding contact angle are discussed