Abstract: A computer implemented method for training a machine learning engine to label sensitive information from text data. The method includes the steps of (i) receiving text data and a list of classes that defines the sensitive information to be labelled; (ii) generating a set of synthetic sentences and using the set of synthetic sentences for training the machine learning engine; (iii) predicting labels for entities in a sample of the text data, selecting a subsample of labelled sentences from the sample of text data to provide to an annotator for reviewing, and updating the training data with the user reviewed sentences; and (iv) training the machine learning engine with the updated training data and repeating step (iii) until the performance of the machine learning meets an end-user requirement.

Abstract: A computer-implemented process of altering original data in a dataset, in which original data is anonymised and a digital watermark is included in the anonymised data. Anonymising the original data incurs information loss, and the process of including the digital watermark does not add significant further information loss. The original data can be a tabular file, a relational or a non-relational database, or the results of interactive database queries. Anonymising the data is achieved using one or more techniques that perturb the original data, such as tokenisation, generalisation; data blurring, synthetic record insertion, record removal or re-ordering.

Abstract: A computer-implemented process of altering original data in a dataset, in which original data is anonymised and a digital watermark is included in the anonymised data. Anonymising the original data incurs information loss, and the process of including the digital watermark does not add significant further information loss. The original data can be a tabular file, a relational or a non-relational database, or the results of interactive database queries. Anonymising the data is achieved using one or more techniques that perturb the original data, such as tokenisation, generalisation; data blurring, synthetic record insertion, record removal or re-ordering.

Abstract: A computer-implemented process of altering original data in a dataset, in which original data is anonymised and a digital watermark is included in the anonymised data. Anonymising the original data incurs information loss, and the process of including the digital watermark does not add significant further information loss. The original data can be a tabular file, a relational or a non-relational database, or the results of interactive database queries. Anonymising the data is achieved using one or more techniques that perturb the original data, such as tokenisation, generalisation; data blurring, synthetic record insertion, record removal or re-ordering.

Abstract: The invention relates to systems for structuring clinical trials protocols into machine interpretable form. A hybrid human and natural language processing system is used to generate a structured computer parseable representation of a clinical trial protocol and its eligibility criteria. Furthermore, a web-based search engine to allow patients to find relevant clinical trials is developed. It works by asking a series of questions, which are generated dynamically such that previous answers will decide which question is generated next. Using a probabilistic model of trial suitability, questions are prioritized so as to minimize the total question burden. Furthermore, data collected across multiple trials is used to optimize the model and to optimize the design of future clinical trials.

Abstract: The invention relates to systems for structuring clinical trials protocols into machine interpretable form. A hybrid human and natural language processing system is used to generate a structured computer parseable representation of a clinical trial protocol and its eligibility criteria. Furthermore, a web-based search engine to allow patients to find relevant clinical trials is developed. It works by asking a series of questions, which are generated dynamically such that previous answers will decide which question is generated next. Using a probabilistic model of trial suitability, questions are prioritized so as to minimize the total question burden. Furthermore, data collected across multiple trials is used to optimize the model and to optimize the design of future clinical trials.