An avatar-based system for identifying individuals likely to develop dementia

This paper presents work on developing an automatic dementia screening test based on patients’ ability to interact and communicate — a highly cognitively demanding process where early signs of dementia can often be detected. Such a test would help general practitioners, with no specialist knowledge, make better diagnostic decisions as current tests lack specificity and sensitivity. We investigate the feasibility of basing the test on conversations between a ‘talking head’ (avatar) and a patient and we present a system for analysing such conversations for signs of dementia in the patient’s speech and language. Previously we proposed a semi-automatic system that transcribed conversations between patients and neurologists and extracted conversation analysis style features in order to differentiate between patients with progressive neurodegenerative dementia (ND) and functional memory disorders (FMD). Determining who talks when in the conversations was performed manually. In this study, we investigate a fully automatic system including speaker diarisation, and the use of additional acoustic and lexical features. Initial results from a pilot study are presented which shows that the avatar conversations can successfully classify ND/FMD with around 91% accuracy, which is in line with previous results for conversations that were led by a neurologist

Toward the Automation of Diagnostic Conversation Analysis in Patients with Memory Complaints.

BACKGROUND: The early diagnosis of dementia is of great clinical and social importance. A recent study using the qualitative methodology of conversation analysis (CA) demonstrated that language and communication problems are evident during interactions between patients and neurologists, and that interactional observations can be used to differentiate between cognitive difficulties due to neurodegenerative disorders (ND) or functional memory disorders (FMD). OBJECTIVE: This study explores whether the differential diagnostic analysis of doctor-patient interactions in a memory clinic can be automated. METHODS: Verbatim transcripts of conversations between neurologists and patients initially presenting with memory problems to a specialist clinic were produced manually (15 with FMD, and 15 with ND). A range of automatically detectable features focusing on acoustic, lexical, semantic, and visual information contained in the transcripts were defined aiming to replicate the diagnostic qualitative observations. The features were used to train a set of five machine learning classifiers to distinguish between ND and FMD. RESULTS: The mean rate of correct classification between ND and FMD was 93% ranging from 97% by the Perceptron classifier to 90% by the Random Forest classifier.Using only the ten best features, the mean correct classification score increased to 95%. CONCLUSION: This pilot study provides proof-of-principle that a machine learning approach to analyzing transcripts of interactions between neurologists and patients describing memory problems can distinguish people with neurodegenerative dementia from people with FMD

Turbulent Mixing in the Interstellar Medium -- an application for Lagrangian Tracer Particles

We use 3-dimensional numerical simulations of self-gravitating compressible turbulent gas in combination with Lagrangian tracer particles to investigate the mixing process of molecular hydrogen (H2) in interstellar clouds. Tracer particles are used to represent shock-compressed dense gas, which is associated with H2. We deposit tracer particles in regions of density contrast in excess of ten times the mean density. Following their trajectories and using probability distribution functions, we find an upper limit for the mixing timescale of H2, which is of order 0.3 Myr. This is significantly smaller than the lifetime of molecular clouds, which demonstrates the importance of the turbulent mixing of H2 as a preliminary stage to star formation.Comment: 10 pages, 5 figures, conference proceedings "Turbulent Mixing and Beyond 2007

A case-control evaluation of 143 single nucleotide polymorphisms for breast cancer risk stratification with classical factors and mammographic density

Breast Cancer Now. Grant Number: 2015MayPR515National Institute for Health Research. Grant Numbers: IS‐BRC‐1215‐20007, NF‐SI‐0513‐10076Prevent Breast Cancer. Grant Numbers: GA09‐002, GA11‐002Cancer Research UK. Grant Numbers: C1287/A10118, C1287/A16563, C569/A16891National Institutes of Health. Grant Numbers: X01HG007492, U19 CA148065Canadian Institutes of Health Research. Grant Number: GPH‐129344Horizon 2020 Research and Innovation Programme. Grant Numbers: 634935, 633784European Union. Grant Number: HEALTH‐F2‐2009‐22317