Comorbidity in General Practice

There is a definite role for GPs in providing care for people with coexisting substance use and mental health problems. However, the current level of care being provided by GPs is inconsistent and a number of areas for improvement have been identified. The PARC comorbidity project has explored issues regarding the approaches that GPs use when faced with patients experiencing comorbidity difficulties. The ultimate aim of this project is to improve the level of care provided to people with coexisting mental health and substance use problems by establishing positive changes in the management of comorbidity in the general practice setting. The first step is to establish what the management ought to be. The PARC Comorbidity Project conducted a review of the activities of the Divisions of General Practice in the area of comorbidity, and identified key issues in the identification and management of people with coexisting mental health and substance use problems in the general practice setting. It consulted with GPs, consumers and other health-care professionals to determine pragmatic ‘best practice’ approaches to the detection, assessment and treatment of comorbidity in the general practice setting, established a set of basic principles that will guide GPs in providing care for patients experiencing comorbidity difficulties and identified key areas of change to enhance the level of care provided to people with coexisting mental health and substance use problems in general practice.Primary Mental Health Care Australian Resource Centr

Computational Models of Miscommunication Phenomena

Miscommunication phenomena such as repair in dialogue are important indicators of the quality of communication. Automatic detection is therefore a key step toward tools that can characterize communication quality and thus help in applications from call center management to mental health monitoring. However, most existing computational linguistic approaches to these phenomena are unsuitable for general use in this way, and particularly for analyzing human–human dialogue: Although models of other-repair are common in human-computer dialogue systems, they tend to focus on specific phenomena (e.g., repair initiation by systems), missing the range of repair and repair initiation forms used by humans; and while self-repair models for speech recognition and understanding are advanced, they tend to focus on removal of “disfluent” material important for full understanding of the discourse contribution, and/or rely on domain-specific knowledge. We explain the requirements for more satisfactory models, including incrementality of processing and robustness to sparsity. We then describe models for self- and other-repair detection that meet these requirements (for the former, an adaptation of an existing repair model; for the latter, an adaptation of standard techniques) and investigate how they perform on datasets from a range of dialogue genres and domains, with promising results.EPSRC. Grant Number: EP/10383/1; Future and Emerging Technologies (FET). Grant Number: 611733; German Research Foundation (DFG). Grant Number: SCHL 845/5-1; Swedish Research Council (VR). Grant Numbers: 2016-0116, 2014-3

Exploring conflicts in rule-based Sensor Networks

This paper addresses rule conflicts within wireless sensor networks. The work is situatedwithin psychiatric ambulatory assessment settings where patients are monitored in andaround their homes. Detecting behaviours within these settings favours sensor networks,while scalability and resource concerns favour processing data on smart nodes incorporatingrule engines. Such monitoring involves personalisation, thereby becoming important toprogram node rules on the fly. Since rules may originate from distinct sources and changeover time, methods are required to maintain rule consistency. Drawing on lessons fromFeature Interaction, the paper contributes novel approaches for detecting and resolving rule-conflict across sensor networks

Stylometric text analysis for Dutch-speaking adolescents with autism spectrum disorder

One of the main characteristics of individuals with autism spectrum disorder (ASD) is a deficit in social communication. The effects of ASD on both verbal and non-verbal communication are widely researched in this respect. In this exploratory study, we investigate whether texts of Dutchspeaking adolescents with ASD (aged 12-18 years) are (automatically) distinguishable from texts written by typically developing peers. First, we want to reveal whether specific characteristics can be found in the writing style of adolescents with ASD, and secondly, we examine the possibility to use these features in an automated classification task. We look for surface features (word and character n-grams, and simple linguistic metrics), but also for deep linguistic features (namely syntactic, semantic and discourse features). The differences between the ASD group and control group are tested for statistical significance and we show that mainly syntactic features are different among the groups, possibly indicating a less dynamic writing style for adolescents with ASD. For the classification task, a Logistic Regression classifier is used. With a surface feature approach, we could reach an F-score of 72.15%, which is much higher than the random baseline of 50%. However, a pure n-gram-based approach very much relies on content and runs the risk of detecting topics instead of style, which argues the need of using deeper linguistic features. The best combination in the deep feature approach originally reached an F-score of just 62.14%, which could not be boosted by automatic feature selection. However, by taking into account the information from the statistical analysis and merely using the features that were significant or trending, we could equal the surface-feature performance and again reached an F-score of 72.15%. This suggests that a carefully composed set of deep features is as informative as surface-feature word and character n-grams. Moreover, combining surface and deep features resulted in a slight increase in F-score to 72.33%