Increased positive thought disorder with illness duration in patients with schizophrenia

It is unclear whether the severity of positive formal thought disorder, a core clinical feature of schizophrenia, is stable or worsening through the chronic course of the illness. The neurocognitive basis for positive thought disorder also remains unclear. The aim of the present paper was to examine the relationship between thought disorder as measured by the Thought Disorder Index (TDI) and duration of illness and neuropsychological indices in 79 patients with schizophrenia. TDI scores increased in proportion to illness duration. TDI scores were not associated with verbal memory or executive functioning. These results indicate an ongoing worsening of positive thought disorder through the course of illness in schizophrenia.

Stable feature selection for clinical prediction: Exploiting ICD tree structure using Tree-Lasso

Modern healthcare is getting reshaped by growing Electronic Medical Records (EMR). Recently, these records have been shown of great value towards building clinical prediction models. In EMR data, patients' diseases and hospital interventions are captured through a set of diagnoses and procedures codes. These codes are usually represented in a tree form (e.g. ICD-10 tree) and the codes within a tree branch may be highly correlated. These codes can be used as features to build a prediction model and an appropriate feature selection can inform a clinician about important risk factors for a disease. Traditional feature selection methods (e.g. Information Gain, T-test, etc.) consider each variable independently and usually end up having a long feature list. Recently, Lasso and related l1-penalty based feature selection methods have become popular due to their joint feature selection property. However, Lasso is known to have problems of selecting one feature of many correlated features randomly. This hinders the clinicians to arrive at a stable feature set, which is crucial for clinical decision making process. In this paper, we solve this problem by using a recently proposed Tree-Lasso model. Since, the stability behavior of Tree-Lasso is not well understood, we study the stability behavior of Tree-Lasso and compare it with other feature selection methods. Using a synthetic and two real-world datasets (Cancer and Acute Myocardial Infarction), we show that Tree-Lasso based feature selection is significantly more stable than Lasso and comparable to other methods e.g. Information Gain, ReliefF and T-test. We further show that, using different types of classifiers such as logistic regression, naive Bayes, support vector machines, decision trees and Random Forest, the classification performance of Tree-Lasso is comparable to Lasso and better than other methods. Our result has implications in identifying stable risk factors for many healthcare problems and therefore can potentially assist clinical decision making for accurate medical prognosis.

Mixed-initiative decision support for care planning based on clinical practice guidelines

Care Plan On-Line (CPOL) is an intranet based system that supports a “Coordinated Care” model for chronic/complex disease management. CPOL combines provision of solicited and unsolicited advice features based on integration of the electronic medical record (EMR) with its decision support logic. The objective is to support General Practitioners (GPs) in formulating a 12-month care plan of services such that: (a) the plan is proactive and patient-centered; (b) the GP is kept in awareness of project- and diseasespecific clinical practice guidelines; and (c) the support integrates with GP workflow in a natural fashion. A key feature of our approach is to blur the distinction of EMR and decision support by presenting guidelines in layers with the top-most being a problem-oriented presentation of patient status, progressing on through to patient-independent supporting evidence. In conjunction with a degree of automated inclusion of care planning services, the system demonstrates mixed user and software initiative. We describe the CPOL deployment setting, the challenges of guideline-based clinical decision support, our approach to guideline delivery, and the CPOL architecture.

Readiness for anger management : clinical and theoretical issues

Anger management methods are a common and successful feature of contemporary cognitive behavioral therapy. Meta-analyses and narrative reviews of the outcome of anger management have been broadly supportive of the view that it is an effective approach. We argue in this paper that an important impediment to the future success of anger management is the failure to fully address the issue of treatment readiness. We discuss distinctive features of anger that make readiness a more important issue than it is for other problem emotions and affects. Relevant theoretical models of readiness are discussed and we review the components of a lack of readiness, including difficulties in establishing a therapeutic alliance. Progress in this area requires greater attention to the measurement and analysis of readiness, to its inclusion as an independent variable in outcome studies and to its clinical modification when readiness is low.

Interventions to improve empathy awareness in sexual and violent offenders : conceptual, empirical, and clinical issues

Interventions that seek to increase empathy are a common feature of programs offered to sexual and violent offenders. Yet, there is little empirical evidence to suggest that they contribute positively to program outcomes. This paper explores the rationale for the delivery of empathy training with violent offenders, describes some of the most commonly used approaches, and reviews the current evidence base relating to effectiveness. It is concluded that while there are strong theoretical grounds for identifying empathy deficits as an important area of criminogenic need, there are considerable difficulties in establishing the extent to which the interventions offered in this area might be considered to be successful in reducing risk.

Patient engagement in clinical communication: an exploratory study

AIM: Existing practice strategies for actively involving patients in care during hospitalisation are poorly understood. The aim of this study was to explore how healthcare professionals engaged patients in communication associated with care transitions.

METHOD: An instrumental, collective case study approach was used to generate empirical data about patient transitions in care. A purposive sample of key stakeholders representing (i) patients and their families; (ii) hospital discharge planning team members; and (iii) healthcare professionals was recruited in five Australian health services. Individual and group semi-structured interviews were conducted to elicit detailed explanations of patient engagement in transition planning. Interviews lasted between 30 and 60 minutes and were digitally recorded and transcribed verbatim. Data collection and analysis were conducted simultaneously and continued until saturation was achieved. Thematic analysis was undertaken.

RESULTS: Five themes emerged as follows: (i) organisational commitment to patient engagement; (ii) the influence of hierarchical culture and professional norms on patient engagement; (iii) condoning individual healthcare professionals' orientations and actions; (iv) understanding and negotiating patient preferences; and (v) enacting information sharing and communication strategies. Most themes illustrated how patient engagement was enabled; however, barriers also existed.

CONCLUSION: Our findings show that strong organisational and professional commitment to patient-centred care throughout the organisation was a consistent feature of health services that actively engaged patients in clinical communication. Understanding patients' needs and preferences and having both formal and informal strategies to engage patients in clinical communication were important in how this involvement occurred.

Stabilizing high-dimensional prediction models using feature graphs

We investigate feature stability in the context of clinical prognosis derived from high-dimensional electronic medical records. To reduce variance in the selected features that are predictive, we introduce Laplacian-based regularization into a regression model. The Laplacian is derived on a feature graph that captures both the temporal and hierarchic relations between hospital events, diseases, and interventions. Using a cohort of patients with heart failure, we demonstrate better feature stability and goodness-of-fit through feature graph stabilization.

Stabilizing l1-norm prediction models by supervised feature grouping

Emerging Electronic Medical Records (EMRs) have reformed the modern healthcare. These records have great potential to be used for building clinical prediction models. However, a problem in using them is their high dimensionality. Since a lot of information may not be relevant for prediction, the underlying complexity of the prediction models may not be high. A popular way to deal with this problem is to employ feature selection. Lasso and l1-norm based feature selection methods have shown promising results. But, in presence of correlated features, these methods select features that change considerably with small changes in data. This prevents clinicians to obtain a stable feature set, which is crucial for clinical decision making. Grouping correlated variables together can improve the stability of feature selection, however, such grouping is usually not known and needs to be estimated for optimal performance. Addressing this problem, we propose a new model that can simultaneously learn the grouping of correlated features and perform stable feature selection. We formulate the model as a constrained optimization problem and provide an efficient solution with guaranteed convergence. Our experiments with both synthetic and real-world datasets show that the proposed model is significantly more stable than Lasso and many existing state-of-the-art shrinkage and classification methods. We further show that in terms of prediction performance, the proposed method consistently outperforms Lasso and other baselines. Our model can be used for selecting stable risk factors for a variety of healthcare problems, so it can assist clinicians toward accurate decision making.

BioKin: an ambulatory platform for gait kinematic and feature assessment

A platform to move gait analysis, which is normally restricted to a clinical environment in a well-equipped gait laboratory, into an ambulatory system, potentially in non-clinical settings is introduced. This novel system can provide functional measurements to guide therapeutic interventions for people requiring rehabilitation with limited access to such gait laboratories. BioKin system consists of three layers: a low-cost wearable wireless motion capture sensor, data collection and storage engine, and the motion analysis and visualisation platform. Moreover, a novel limb orientation estimation algorithm is implemented in the motion analysis platform. The performance of the orientation estimation algorithm is validated against the orientation results from a commercial optical motion analysis system and an instrumented treadmill. The study results demonstrate a root-mean-square error less than 4° and a correlation coefficient more than 0.95 when compared with the industry standard system. These results indicate that the proposed motion analysis platform is a potential addition to existing gait laboratories in order to facilitate gait analysis in remote locations.