Predictive Modelling Approach to Data-driven Computational Psychiatry

Alghamdi, Wajdi. 2018. Predictive Modelling Approach to Data-driven Computational Psychiatry. Doctoral thesis, Goldsmiths, University of London [Thesis]

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Abstract or Description

This dissertation contributes with novel predictive modelling approaches to data-driven
computational psychiatry and offers alternative analyses frameworks to the standard statistical
analyses in psychiatric research. In particular, this document advances research in
medical data mining, especially psychiatry, via two phases. In the first phase, this document
promotes research by proposing synergistic machine learning and statistical approaches
for detecting patterns and developing predictive models in clinical psychiatry
data to classify diseases, predict treatment outcomes or improve treatment selections. In
particular, these data-driven approaches are built upon several machine learning techniques
whose predictive models have been pre-processed, trained, optimised, post-processed
and tested in novel computationally intensive frameworks. In the second phase,
this document advances research in medical data mining by proposing several novel extensions
in the area of data classification by offering a novel decision tree algorithm,
which we call PIDT, based on parameterised impurities and statistical pruning approaches
toward building more accurate decision trees classifiers and developing new ensemblebased
classification methods. In particular, the experimental results show that by building
predictive models with the novel PIDT algorithm, these models primarily led to better
performance regarding accuracy and tree size than those built with traditional decision
trees. The contributions of the proposed dissertation can be summarised as follow.
Firstly, several statistical and machine learning algorithms, plus techniques to improve
these algorithms, are explored. Secondly, prediction modelling and pattern detection approaches
for the first-episode psychosis associated with cannabis use are developed.
Thirdly, a new computationally intensive machine learning framework for understanding
the link between cannabis use and first-episode psychosis was introduced. Then, complementary
and equally sophisticated prediction models for the first-episode psychosis associated
with cannabis use were developed using artificial neural networks and deep learning
within the proposed novel computationally intensive framework. Lastly, an efficient
novel decision tree algorithm (PIDT) based on novel parameterised impurities and statistical
pruning approaches is proposed and tested with several medical datasets. These contributions
can be used to guide future theory, experiment, and treatment development in
medical data mining, especially psychiatry.

Item Type:

Thesis (Doctoral)

Identification Number (DOI):

https://doi.org/10.25602/GOLD.00025905

Keywords:

Data mining, Machine Learning, Predictive modelling, Data-driven, Computational psychiatry, First-episode psychosis, Gaussian Processes, Decision Trees, Support Vector Machines, Artificial Neural Networks, Deep Learning, Data Pre-processing, Data Post-processing, Monte Carlo, T-test, Parameterised Impurities, S-condition, PIDT

Departments, Centres and Research Units:

Computing

Date:

31 October 2018

Item ID:

25905

Date Deposited:

27 Feb 2019 15:42

Last Modified:

01 May 2020 14:27

URI:

http://research.gold.ac.uk/id/eprint/25905

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