Paul R. Trundle

Medical image analysis with artificial neural networks

Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging.

Social media analysis for product safety using text mining and sentiment analysis

The growing incidents of counterfeiting and associated economic and health consequences necessitate the development of active surveillance systems capable of producing timely and reliable information for all stake holders in the anti-counterfeiting fight. User generated content from social media platforms can provide early clues about product allergies, adverse events and product counterfeiting. This paper reports a work in progress with contributions including: the development of a framework for gathering and analyzing the views and experiences of users of drug and cosmetic products using machine learning, text mining and sentiment analysis; the application of the proposed framework on Facebook comments and data from Twitter for brand analysis, and the description of how to develop a product safety lexicon and training data for modeling a machine learning classifier for drug and cosmetic product sentiment prediction. The initial brand and product comparison results signify the usefulness of text mining and sentiment analysis on social media data while the use of machine learning classifier for predicting the sentiment orientation provides a useful tool for users, product manufacturers, regulatory and enforcement agencies to monitor brand or product sentiment trends in order to act in the event of sudden or significant rise in negative sentiment.

Using random forest and decision tree models for a new vehicle prediction approach in computational toxicology

Drug vehicles are chemical carriers that provide beneficial aid to the drugs they bear. Taking advantage of their favourable properties can potentially allow the safer use of drugs that are considered highly toxic. A means for vehicle selection without experimental trial would therefore be of benefit in saving time and money for the industry. Although machine learning is increasingly used in predictive toxicology, to our knowledge there is no reported work in using machine learning techniques to model drug-vehicle relationships for vehicle selection to minimise toxicity. In this paper we demonstrate the use of data mining and machine learning techniques to process, extract and build models based on classifiers (decision trees and random forests) that allow us to predict which vehicle would be most suited to reduce a drug’s toxicity. Using data acquired from the National Institute of Health’s (NIH) Developmental Therapeutics Program (DTP) we propose a methodology using an area under a curve (AUC) approach that allows us to distinguish which vehicle provides the best toxicity profile for a drug and build classification models based on this knowledge. Our results show that we can achieve prediction accuracies of 80 % using random forest models whilst the decision tree models produce accuracies in the 70 % region. We consider our methodology widely applicable within the scientific domain and beyond for comprehensively building classification models for the comparison of functional relationships between two variables.

Bipartite Network Model for Inferring Hidden Ties in Crime Data

Certain crimes are difficult to be committed by individuals but carefully organised by group of associates and affiliates loosely connected to each other with a single or small group of individuals coordinating the overall actions. A common starting point in understanding the structural organisation of criminal groups is to identify the criminals and their associates. Situations arise in many criminal datasets where there is no direct connection among the criminals. In this paper, we investigate ties and community structure in crime data in order to understand the operations of both traditional and cyber criminals, as well as to predict the existence of organised criminal networks. Our contributions are twofold: we propose a bipartite network model for inferring hidden ties between actors who initiated an illegal interaction and objects affected by the interaction, we then validate the method in two case studies on pharmaceutical crime and underground forum data using standard network algorithms for structural and community analysis. The vertex level metrics and community analysis results obtained indicate the significance of our work in understanding the operations and structure of organised criminal networks which were not immediately obvious in the data. Identifying these groups and mapping their relationship to one another is essential in making more effective disruption strategies in the future.

HERMES: a FP7 funded project towards the development of a computer-aided memory management system via intelligent computations

In this article, we introduce a new concept in HERMES, the FP7 funded project in Europe, in developing technology innovations towards computer aided memory management via intelligent computation, and helping elderly people to overcome their decline in cognitive capabilities.In this project, an integrated computer aided memory management system is being developed from a strong interdisciplinary perspective, which brings together knowledge from gerontology to software and hardware integration. State‐of‐the‐art techniques and algorithms for image, video and speech processing, pattern recognition, semantic summarisation are illustrated, and the objectives and strategy for HERMES are described. Also, more details on the software that has been implemented are provided with future development direction.

Classification of Heterogeneous Data Based on Data Type Impact on Similarity

Real-world datasets are increasingly heterogeneous, showing a mixture of numerical, categorical and other feature types. The main challenge for mining heterogeneous datasets is how to deal with heterogeneity present in the dataset records. Although some existing classifiers (such as decision trees) can handle heterogeneous data in specific circumstances, the performance of such models may be still improved, because heterogeneity involves specific adjustments to similarity measurements and calculations. Moreover, heterogeneous data is still treated inconsistently and in ad-hoc manner. In this paper, we study the problem of heterogeneous data classification: our purpose is to use heterogeneity as a positive feature of the data classification effort by using consistently the similarity between data objects. We address the heterogeneity issue by studying the impact of mixing data types in the calculation of data objects’ similarity. To reach our goal, we propose an algorithm to divide the initial data records based on pairwise similarity for classification subtasks with the aim to increase the quality of the data subsets and apply specialized classifier models on them. The performance of the proposed approach is evaluated on 10 publicly available heterogeneous data sets. The results show that the models achieve better performance for heterogeneous datasets when using the proposed similarity process.

Using computational methods for the prediction of drug vehicles

Drug vehicles are chemical carriers that aid a drugs passage through an organism. Whilst they possess no intrinsic efficacy they are designed to achieve desirable characteristics which can include improving a drugs permeability and or solubility, targeting a drug to a specific site or reducing a drugs toxicity. All of which are ideally achieved without compromising the efficacy of the drug. Whilst the majority of drug vehicle research is focused on the solubility and permeability issues of a drug, significant progress has been made on using vehicles for toxicity reduction. Achieving this can enable safer and more effective use of a potent drug against diseases such as cancer. From a molecular perspective, drugs activate or deactivate biochemical pathways through interactions with cellular macromolecules resulting in toxicity. For newly developed drugs such pathways are not always clearly understood but toxicity endpoints are still required as part of a drugs registration. An understanding of which vehicles could be used to ameliorate the unwanted toxicities of newly developed drugs would be highly desirable to the pharmaceutical industry. In this paper we demonstrate the use of different classifiers as a means to select vehicles best suited to avert a drugs toxic effects when no other information about a drugs characteristics is known. Through analysis of data acquired from the Developmental Therapeutics Program (DTP) we are able to establish a link between a drugs toxicity and vehicle used. We demonstrate that classification and selection of the appropriate vehicle can be made based on the similarity of drug choice.

A Memory Management System towards Cognitive Assistance of Elderly People

This paper describes technology innovations towards computer aided memory management via intelligent data processing, and helping elderly people to overcome their decline in terms of cognitive. The system which integrates the functionalities to be delivered by HERMES, the FP7 funded project in Europe, aims at assisting the user who suffers from memory decline due to aging with effective memory refreshment based on the correlation of textual, spoken, or visual data. In this project, the system is being developed from a strong interdisciplinary perspective, which brings together knowledge from gerontology to software and hardware implementation.

Human Memory Assistance through Semantic-Based Text Processing

The proportion of elderly people across the world is predicted to increase significantly in the next 50 years. Tools to assist the elderly with remaining independent must be developed now to reduce the impact this will have on future generations. Technological solutions have the potential to alleviate some of the problems associated with old age, particularly those associated with the deterioration of memory. This paper proposes an algorithm for semantic-based text processing within the context of a cognitive care platform for older people, and an implementation of the algorithm used within the EU FP7 project HERMES is introduced. The algorithm facilitates computerised human-like memory management through semantic interpretation of everyday events and textual search terms, and the utilisation of human language lexical resources.

Multi-source Data Modelling: Integrating Related Data to Improve Model Performance

Traditional methods in Data Mining cannot be applied to all types of data with equal success. Innovative methods for model creation are needed to address the lack of model performance for data from which it is difficult to extract relationships. This paper proposes a set of algorithms that allow the integration of data from multiple datasets that are related, as well as results from the implementation of these techniques using data from the field of Predictive Toxicology. The results show significant improvements when related data is used to aid in the model creation process, both overall and in specific data ranges. The proposed algorithms have potential for use within any field where multiple datasets exist, particularly in fields combining computing, chemistry and biology.