Predictive Modelling Approach to Data-driven Computational Psychiatry

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

Secure Multi-Party Computation for Collaborative Data Analysis

A potent cryptographic mechanism called Secure Multi-Party Computation (SMPC) has evolved that allows numerous participants to work together and execute data analytic tasks while maintaining the privacy and secrecy of their individual data. In several fields, like healthcare, finance, and the social sciences, where numerous stakeholders must exchange and evaluate sensitive information without disclosing it to others, collaborative data analysis is becoming more and more common. This study gives a thorough investigation of SMPC for group data analysis. The main goal is to give a thorough understanding of the SMPC’s guiding ideas, protocols, and applications while stressing the advantages and difficulties it presents for fostering safe cooperation among various data owners. In summary, this study offers a thorough and current examination of Secure Multi-Party Computation for Collaborative Data examination. It provides a thorough grasp of the SMPC deployment issues as well as the underlying ideas, protocols, and applications. The goal of the article is to function as a useful resource for researchers, professionals, and decision-makers interested in using SMPC to facilitate group data analysis while protecting confidentiality and privacy

Quantum Computing: Algorithms,Architectures, and Applications

Cryptography, optimization, simulation, and machine learning are just a few of the industries that might be completely transformed by quantum computing. This abstract gives a thorough introduction to quantum computing with an emphasis on its algorithms, architectures, and applications. In conclusion, this abstract offers an in-depth analysis of quantum computing, including its algorithms, structures, and applications. It highlights the revolutionary potential of quantum computing in tackling difficult issues that are beyond the scope of conventional computers, laying the groundwork for further research and understanding of this quickly developing topic

Deep Learning Techniques for Image Recognition and Object Detection

Particularly in the fields of object identification and picture recognition, deep learning approaches have transformed the science of computer vision. This abstract provides a summary of recent developments and cutting-edge methods in deep learning for applications like object identification and picture recognition. The automated identification and classification of objects or patterns inside digital photographs is known as image recognition. Convolutional neural networks (CNNs), for example, have displayed outstanding performance in image identification tests. By directly learning hierarchical representations of visual characteristics from raw pixel data, these algorithms are able to recognize complex patterns and provide precise predictions. The ability for models to learn sophisticated visual representations straight from raw pixel data has transformed applications like object identification and picture recognition. The development of extremely accurate and effective systems has been accelerated by advances in deep learning architectures and large-scale annotated datasets. Further advances in object identification and picture recognition are anticipated as deep learning develops, with applications in a variety of fields including autonomous driving, surveillance, and medical imaging

Artificial Intelligence in Healthcare: Diagnosis, Treatment, and Prediction

One of the most potential uses of artificial intelligence (AI), which has changed a number of industries, is in healthcare. The application of AI in healthcare is discussed in general in this study, with an emphasis on diagnosis, treatment, and prediction. In the area of diagnostics, AI has proven to be remarkably adept at deciphering X-rays, CT scans, and MRI pictures to spot illnesses and anomalies. A branch of AI known as deep learning algorithms has shown to be particularly good at accurately identifying and categorizing medical disorders. Large volumes of imaging data may be swiftly analyzed by AI systems, enabling medical personnel to diagnose patients more accurately and with fewer mistakes. Additionally, AI may combine patient information, genetic data, and other pertinent data to produce tailored diagnostic suggestions. Consequently, AI has become a game-changing force in healthcare, especially in the disciplines of diagnosis, treatment, and prediction. AI systems can help medical personnel make more precise diagnoses, create individualized treatment plans, and forecast patient outcomes by utilizing machine learning algorithms and advanced data analytics. While there are still difficulties, there are enormous potential advantages for AI in healthcare, and coordinated efforts are required to realize these advantages and assure its ethical and fair incorporation into healthcare systems

Swarmic approach for symmetry detection of cellular automata behaviour

Since the introduction of cellular automata in the late 1940s they have been used to address various types of problems in computer science and other multidisciplinary fields. Their generative capabilities have been used for simulating and modelling various natural, physical and chemical phenomena. Besides these applications, the lattice grid of cellular automata has been providing a by-product interface to generate graphical contents for digital art creation. One important aspect of cellular automata is symmetry, detecting of which is often a difficult task and computationally expensive. In this paper a swarm intelligence algorithm—Stochastic Diffusion Search—is proposed as a tool to identify points of symmetry in the cellular automata-generated patterns

Predicting Psychosis Using the Experience Sampling Method with Mobile Apps

Smart phones have become ubiquitous in the recent years, which opened up a new opportunity for rediscovering the Experience Sampling Method (ESM) in a new efficient form using mobile apps, and provides great prospects to become a low cost and high impact mHealth tool for psychiatry practice. The method is used to collect longitudinal data of participants' daily life experiences, and is ideal to capture fluctuations in emotions (momentary mental states) as an early indicator for later mental health disorder. In this study ESM data of patients with psychosis and controls were used to examine emotion changes and identify patterns. This paper attempts to determine whether aggregated ESM data, in which statistical measures represent the distribution and dynamics of the original data, are able to distinguish patients from controls. Variable importance, recursive feature elimination and ReliefF methods were used for feature selection. Model training and tuning, and testing were performed in nested cross-validation, and were based on algorithms such as Random Forests, Support Vector Machines, Gaussian Processes, Logistic Regression and Neural Networks. ROC analysis was used to post-process these models. Stability of model performances was studied using Monte Carlo simulations. The results provide evidence that pattern in mood changes can be captured with the combination of techniques used. The best results were achieved by SVM with radial kernel, where the best model performed with 82% accuracy and 82% sensitivity

Machine Learning for Predictive Analytics in Social Media Data

Machine Learning (ML) has become a potent predictive analytics tool in several fields, including the study of social media data. Social media sites have developed into massive repositories of user-generated information, providing insightful data about user trends, interests, and behavior. This abstract emphasizes the use of machine learning methods for predictive analytics in social media data and examines the potential and problems unique to this field. Utilizing the capabilities of machine learning algorithms to identify significant trends and forecast user behavior from social media data is the goal of this study. The study makes use of a sizable dataset made up of user profiles, blog posts, comments, and engagement metrics gathered from well-known social networking sites. Predictive models are created using a variety of machine learning algorithms, such as ensemble methods, neural networks, decision trees, and support vector machines. As a result, this study emphasizes how important machine learning is for doing predictive analytics on social media data. The employment of diverse algorithms and preprocessing methods yields insightful information about user behavior and enables precise prediction of user behaviors. To improve the prediction powers of machine learning in this area, future research should concentrate on tackling the obstacles related to social media data, such as privacy concerns and data quality issues