eHealth tools

Some example of the eHealth assessment and analysis tools that I have designed and developed can be found here. All tools were developed using Concerto, the Psychometrics Centre, University of Cambridge's open-source tool for developing electronic assessments and web-based statistical applications. 

(Computerised adaptive testing)

A computer adaptive test developed in collaboration with the International Hub for Quality of Life research at the University of Manchester. It uses an algorithmic question selection procedure based on a user's previous responses to deliver assessments that are shorter and more reliable than paper-based questionnaires. You can read the accompanying journal article here

(Computerised adaptive testing)

It is true that "All CATs are black in the dark" - once we deploy computer adaptive tests in to the 'real world' it can be difficult to judge how well they are really working. This system allows users to rate model-based predictions about their quality of life as a means to validate the models. I was awarded the 2016 New Investigator Award at the 26th International ISOQOL symposium for my presentation of this new method. 

(Machine Learning)

Open-text information is routinely collected in many health services but it is often left unused. When qualitative data are collected in enormous quantities; human raters are an inefficient and expensive way to make sense of this information. We trained an ensemble of machine learning algorithms to classify open-text reports of doctors' performance with startling accuracy! 

(Computerised adaptive testing)

Adaptive version of popular 'legacy' depression screening instrument in collaboration with Aiden Loe, doctoral candidate in psychology, University of Cambridge.

An example implementation of sentiment analysis using machine learning within Concerto. Used for teaching machine learning courses. 

Other tools developed at the University of Cambridge Psychometrics Centre. 

Predictive World (Gibbons, Hoferkova, Barclay and Popov in collaboration with Sid Lee, Stink Digital and Ubisoft Entertainment) 

The Psychometrics Centre worked with industrial partners to develop a predictive online system to support the launch of Watch Dogs 2. Predictive World draws together 2.5 billion pieces of publically-available data to make predictions about individuals; now and in the future. More than just a marketing tool, the system draws on epidemiological and psychological research evidence to allow users to explore the effects of different lifestyle behaviours including smoking and drinking on outcomes related to health, work, and leisure. 

Predictive world received over a million users in the first two weeks of release. The website was well-received by the internationally community and was awarded two prestigious design awards, the D&AD graphite pencil and the ADC Gold Award. 

Apply Magic Sauce (Kosinski, Stillwell and Rust) 

Apply Magic Sauce (AMS, to its friends) is a personalisation engine that accurately predicts psychological traits from digital footprints of human behaviour. AMS uses machine learning algorithms to predict intimate psychological traits from Facebook 'Likes' (papers here and here) and open-text. 

Aurei X Concerto Demo (Stillwell, Aurei) 

Demo of the Aurei platform for fully flexible emotional reporting.