Support system for decision making in the identification of risk for body dysmorphic disorder: A fuzzy model

PurposeTo develop a fuzzy linguistic model to quantify the level of distress of patients seeking cosmetic surgery. Body dysmorphic disorder (BDD) is a mental condition related to body image relatively common among cosmetic surgery patients; it is difficult to diagnose and is a significant cause of morbidity and mortality. Fuzzy cognitive maps are an efficient tool based on human knowledge and experience that can handle uncertainty in identifying or grading BDD symptoms and the degree of body image dissatisfaction. Individuals who seek cosmetic procedures suffer from some degree of dissatisfaction with appearance.MethodsA fuzzy model was developed to measure distress levels in cosmetic surgery patients based on the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV), diagnostic criterion B for BDD. We studied 288 patients of both sexes seeking abdominoplasty, rhinoplasty, or rhytidoplasty in a university hospital.ResultsPatient distress ranged from “none” to “severe” (range = 7.5–31.6; cutoff point = 18; area under the ROC curve = 0.923). There was a significant agreement between the fuzzy model and DSM-IV criterion B (kappa = 0.805; p < 0.001).ConclusionThe fuzzy model measured distress levels with good accuracy, indicating that it can be used as a screening tool in cosmetic surgery and psychiatric practice.     

Increased identification of the primary care provider as the main source of asthma care among urban minority children

Objective: Urban, minority, and disadvantaged youth with asthma frequently use emergency departments (EDs) for episodic asthma care instead of their primary care providers (PCPs). We sought to increase the rate of guardians' identification of the PCP as the source of asthma care for their children through integrated electronic health records and care coordination. Methods: In this prospective cohort study, we implemented an electronic communication process between an asthma specialty clinic and PCPs coupled with short-term care coordination in sample of youth aged 2–12 years with asthma and surveyed their guardians at baseline and 3 and 6 months after the intervention. Results: Guardians of 50 children (median age 5.8 years, 64% male, 98% African American, 94% public insurance) were enrolled. Compared to baseline, at 3 and 6 months after the intervention, significantly more guardians reported that the PCP was their child's primary asthma health care provider [70% at baseline, 85% at 3 months, 83% at 6 months (time averaged adjusted OR 77.4, 95% CI 3.0, 2027.1]. Further, significantly more guardians reported that they took their child to the PCP when the child experienced problems with his/her asthma [16% at baseline, 35% at 3 months, 41% at 6 months (time averaged adjusted odds ratio (OR) 10.6, 95% CI 2.7, 41.7]. Conclusion: Care in a subspecialty asthma clinic augmented by electronic communication with PCPs and short term care coordination was associated with significantly improved identification of PCPs as the primary source of asthma care in a cohort of urban minority youth.     

Feasibility study of the BrightBrainer™ integrative cognitive rehabilitation system for elderly with dementia

Purpose: To describe the development of BrightBrainer? integrative cognitive rehabilitation system and determine clinical feasibility with nursing home-bound dementia patients. Method: BrightBrainer cognitive rehabilitation simulations were first played uni-manually, then bimanually. Participants sat in front of a laptop and interacted through a game controller that measured hand movements in 3D, as well as flexion of both index fingers. Interactive serious games were designed to improve basic and complex attention (concentration, short-term memory, dual tasking), memory recall, executive functioning and emotional well-being. Individual simulations adapted automatically to each participant’s level of motor functioning. The system underwent feasibility trials spanning 16 sessions over 8 weeks. Participants were evaluated pre- and post-intervention, using standardized neuropsychological measures. Computerized measures of movement repetitions and task performance were stored on a remote server. Results: Group analysis for 10 participants showed statistically significant improvement in decision making (p?p?p?p?p?Conclusions: Preliminary findings demonstrate utility within an advanced dementia population, suggesting that it will be beneficial to evaluate BrightBrainer through controlled clinical trials and to investigate its application in other clinical populations.

• Implications for Rehabilitation

• It is possible to improve cognitive function in older low-functioning patients. Integrative rehabilitation through games combining cognitive (memory, focusing, executive function) and physical (bimanual whole arm movement, grasping, task sequencing) elements is enjoyable for this population.

• The severity of depression in these elderly can be reduced through virtual reality bimanual games.

• The number of upper extremity active repetitions performed in the process of solving cognitive problems with the BrightBrainer? system is 600. This number is 18 times (1875%) larger than those observed by other researchers in conventional physical or occupational rehabilitation sessions.

     

Describing disease processes using a probabilistic logic of qualitative time

BackgroundClinical knowledge about progress of diseases is characterised by temporal information as well as uncertainty. However, precise timing information is often unavailable in medicine. In previous research this problem has been tackled using Allen's qualitative algebra of time, which, despite successful medical application, does not deal with the associated uncertainty.ObjectivesIt is investigated whether and how Allen's temporal algebra can be extended to handle uncertainty to better fit available knowledge and data of disease processes.MethodsTo bridge the gap between probability theory and qualitative time reasoning, methods from probabilistic logic are explored. The relation between the probabilistic logic representation and dynamic Bayesian networks is analysed. By studying a typical, and clinically relevant problem, the detection of exacerbations of chronic obstructive pulmonary disease (COPD), it is determined whether the developed probabilistic logic of qualitative time is medically useful.ResultsThe probabilistic logic extension of Allen's temporal algebra, called Qualitative Time CP-logic provides a tool to model disease processes at a natural level of abstraction and is sufficiently powerful to reason with imprecise, uncertain knowledge. The representation of the COPD disease process gives evidence that the framework can be applied functionally to a clinical problem.ConclusionThe combination of qualitative time and probabilistic logic offers a useful framework for modelling knowledge and data to describe disease processes in clinical medicine.     

Exercise Studies in Patients With Rotary Blood Pumps: Cause,Effects, and Implications for Starling‐Like Control of Changes in Pump Flow

This multicenter study examines in detail the spontaneous increase in pump flow at fixed speed that occurs in exercise. Eight patients implanted with the VentrAssist rotary blood pump were subjected to maximal and submaximal cycle ergometry studies, the latter being completed with patients supine and monitored with right heart catheter and echocardiography. Maximal exercise studies conducted in each patient at three different pump speeds on separate days established initially the magnitude and consistency of increases in pump flow that correlated well with changes in heart rate. However, there was considerable variation, coefficients of variation for mean heart rate and pump flow being 47.9 and 49.3%, respectively. Secondly, these studies indicated that increasing pump flows caused significant improvements in maximal exercise capacity. An increase of 2.1 L/min (35%) in maximum blood flow caused 12 W (16%) further increase in achievable work, 1.26 (9.3%) mL/kg/min in maximal oxygen uptake, and 2.3 (23%) mL/kg/min in anaerobic threshold. Mean increases in lactate were 0.85 mm (24%), but mean B‐type natiuretic peptide fell by 126 mm , (?78%). From submaximal supine exercise studies, multiple linear regression of pump flow on factors thought to underlie the spontaneous increase in pump flow indicated that it was associated with increases in heart rate (P = 0.039), pressure gradient across the left ventricle (P = 0.032), and right atrial pressure (P = 0.003). These changes have implications for the recently reported Starling‐like controller for pump flow based on pump pulsatility values, which emulates the Starling curve relating pump output to left ventricular preload. Unmodified, the controller would not permit the full benefits of this effect to be afforded to patients implanted with rotary blood pumps. A modification to the pump control algorithm is proposed to eliminate this problem     

Application of adaptive neuro-fuzzy inference system for epileptic seizure detection using wavelet feature extraction

Intelligent computing tools such as artificial neural network (ANN) and fuzzy logic approaches are demonstrated to be competent when applied individually to a variety of problems. Recently, there has been a growing interest in combining both these approaches, and as a result, neuro-fuzzy computing techniques have been evolved. In this study, a new approach based on an adaptive neuro-fuzzy inference system (ANFIS) was presented for epileptic seizure detection. The proposed ANFIS model combined the neural network adaptive capabilities and the fuzzy logic qualitative approach. Decision making was performed in two stages: feature extraction using the wavelet transform (WT) and the ANFIS trained with the backpropagation gradient descent method in combination with the least squares method. Some conclusions concerning the impacts of features on the detection of epileptic seizures were obtained through analysis of the ANFIS. The results are highly promising, and a comparative analysis suggests that the proposed modeling approach outperforms ANN model in terms of training performances and classification accuracies. The results confirmed that the proposed ANFIS model has some potential in epileptic seizure detection. The ANFIS model achieved accuracy rates which were higher than that of the stand-alone neural network model.     

PredyCLU: A prediction system for chronic leg ulcers based on fuzzy logic; part II—Exploring the arterial side

Peripheral arterial disease (PAD) and its most severe form, critical limb ischaemia (CLI), are very common clinical conditions related to atherosclerosis and represent the major causes of morbidity, mortality, disability, and reduced quality of life (QoL), especially for the onset of ischaemic chronic leg ulcers (ICLUs) and the subsequent need of amputation in affected patients. Early identification of patients at risk of developing ICLUs may represent the best form of prevention and appropriate management. In this study, we used a Prediction System for Chronic Leg Ulcers (PredyCLU) based on fuzzy logic applied to patients with PAD. The patient population consisted of 80 patients with PAD, of which 40 patients (30 males [75%] and 10 females [25%]; mean age 66.18 years; median age 67.50 years) had ICLUs and represented the case group. Forty patients (100%) (27 males [67.50%] and 13 females [32.50%]; mean age 66.43 years; median age 66.50 years) did not have ICLUs and represented the control group. In patients of the case group, the higher was the risk calculated with the PredyCLU the more severe were the clinical manifestations recorded. In this study, the PredyCLU algorithm was retrospectively applied on a multicentre population of 80 patients with PAD. The PredyCLU algorithm provided a reliable risk score for the risk of ICLUs in patients with PAD.     

Delay‐dependent feedforward control of time‐delay systems with parametric uncertainties via dynamic IQCs

This paper studies the design problem of a robust delay‐dependent H _∞ feedforward controller design for a class of linear uncertain time‐delay system having state and control delays when the system is subject to ‐type disturbances. The proposed controller scheme involves two main controllers, which are static state‐feedback and dynamic feedforward controllers. The state‐feedback controller is used for stabilizing the delay and uncertainty‐free system, whereas the feedforward controller performs disturbance attenuation. Dynamic type integral quadratic constraints (IQCs), which consist of frequency‐dependent multipliers, have been introduced to represent the delays and parametric uncertainties in the system where the degree of the multiplier used in IQC representation is in an adjustable nature. This scheme allows the designer to obtain less conservative controllers with increasing precision. Sufficient delay‐dependent criteria in terms of linear matrix inequalities are obtained such that the uncertain linear time‐delay system is guaranteed to be globally, uniformly, asymptotically stable with a minimum disturbance attenuation level. Several numerical examples together with the simulation studies provided at the end illustrate the usefulness of the proposed design. Copyright © 2012 John Wiley & Sons, Ltd.