Adaptive Neuro-Fuzzy Inference System for Prediction of Surgery Time for Ischemic Stroke Patients

With the advent of machine learning techniques, creation and utilization of prediction models for different medical procedures including prediction of diagnosis, treatment and recovery of different medical conditions has become the norm. Recent studies focus on the automation of infarction volume growth rate prediction by the utilization of machine learning techniques. These techniques when effectively applied, could significantly help in reducing the time needed to attend to stroke patients. We propose, in this proposal, a Fuzzy Inference System that can determine when a stroke patient should undergo Decompressive Hemicraniectomy. The second infarction volume growth rate and the decision whether a patient needs to undergo this procedure, both predicted outputs of two trained models, act as inputs to this system. While the initial prediction model, that which predicts the second infarction volume growth rate is adopted from an earlier model, we propose the later model in this paper. Three Machine Learning techniques - Support Vector Machine, Artificial Neural Network and Adaptive Neuro Fuzzy Inference System with and without the feature reduction technique of Principle Component Analysis were modelled and evaluated, the best of which was selected to model the proposed prediction model. We also defined the structure of Fuzzy Inference System along with its rules and obtained an overall accuracy of 95.7% with a precision of 1 showing promising results from the use of fuzzy logic

High-efficacy therapies for relapsing-remitting multiple sclerosis: implications for adherence. An expert opinion from the United Arab Emirates

The number of disease-modifying treatments (DMDs) for relapsing-remitting multiple sclerosis has increased. DMDs differ not only in their efficacy and safety/tolerability, but also in the treatment burden of, associated with their initiation, route/frequency of administration, maintenance treatment and monitoring. High-efficacy DMDs bring the prospect of improved suppression of relapses and progression of disability, but may have serious safety issues, and burdensome long-term monitoring. Studies of patient preferences in this area have focused on side effects, efficacy and route of administration. Adherence to DMDs is often suboptimal in relapsing-remitting multiple sclerosis and there is a need to understand more about how the complex therapeutic and administration profiles of newer DMDs interact with these barriers to support optimal adherence to therapy

Disease-Modifying Drugs and Family Planning in People with Multiple Sclerosis: A Consensus Narrative Review from the Gulf Region

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Expert consensus from the Arabian Gulf on selecting disease-modifying treatment for people with multiple sclerosis according to disease activity.

AbstractRecent research has expanded our understanding of the natural history and clinical course of multiple sclerosis (MS) in the Arabian Gulf region. In addition, the number of available therapi..

Adaptive Neuro-Fuzzy Inference System for Prediction of Surgery Time for Ischemic Stroke Patients

With the advent of machine learning techniques, creation and utilization of prediction models for different medical procedures including prediction of diagnosis, treatment and recovery of different medical conditions has become the norm. Recent studies focus on the automation of infarction volume growth rate prediction by the utilization of machine learning techniques. These techniques when effectively applied, could significantly help in reducing the time needed to attend to stroke patients. We propose, in this proposal, a Fuzzy Inference System that can determine when a stroke patient should undergo Decompressive Hemicraniectomy. The second infarction volume growth rate and the decision whether a patient needs to undergo this procedure, both predicted outputs of two trained models, act as inputs to this system. While the initial prediction model, that which predicts the second infarction volume growth rate is adopted from an earlier model, we propose the later model in this paper. Three Machine Learning techniques - Support Vector Machine, Artificial Neural Network and Adaptive Neuro Fuzzy Inference System with and without the feature reduction technique of Principle Component Analysis were modelled and evaluated, the best of which was selected to model the proposed prediction model. We also defined the structure of Fuzzy Inference System along with its rules and obtained an overall accuracy of 95.7% with a precision of 1 showing promising results from the use of fuzzy logic

Prediction of infarction volume and infarction growth rate in acute ischemic stroke

The prediction of infarction volume after stroke onset depends on the shape of the growth dynamics of the infarction. To understand growth patterns that predict lesion volume changes, we studied currently available models described in literature and compared the models with Adaptive Neuro-Fuzzy Inference System [ANFIS], a method previously unused in the prediction of infarction growth and infarction volume (IV). We included 67 patients with malignant middle cerebral artery [MMCA] stroke who underwent decompressive hemicraniectomy. All patients had at least three cranial CT scans prior to the surgery. The rate of growth and volume of infarction measured on the third CT was predicted with ANFIS without statistically significant difference compared to the ground truth [P = 0.489]. This was not possible with linear, logarithmic or exponential methods. ANFIS was able to predict infarction volume [IV3] over a wide range of volume [163.7-600 cm3] and time [22-110 hours]. The cross correlation [CRR] indicated similarity between the ANFIS-predicted IV3 and original data of 82% for ANFIS, followed by logarithmic 70%, exponential 63% and linear 48% respectively. Our study shows that ANFIS is superior to previously defined methods in the prediction of infarction growth rate (IGR) with reasonable accuracy, over wide time and volume range. 1 2017 The Author(s).Scopu

Rescue treatment in patients with poorly responsive Guillain–Barre syndrome

Objectives: To evaluate the effectiveness of rescue treatment (intravenous immunoglobulin or plasma exchange) in patients with Guillain–Barre syndrome who did not respond or deteriorated after the initial management with intravenous immunoglobulin. Methods: We performed a retrospective review of the medical records of patients who responded poorly or did not respond to intravenous immunoglobulin treatment. The disability parameters of those who received second-line treatment with intravenous immunoglobulin or plasma exchange (20 patients) were compared with those who did not receive second-line treatment (19 patients). Results: There was a statistically significant improvement in disability scores at 1 month in the patients who received the rescue treatment (p = 0.033). However, there was no significant difference in the disability scores at 3 and 6 months, or in length of intensive care unit stay. Conclusion: Our study showed that a second course of treatment to carefully selected patients may be beneficia

Revisiting Hemicraniectomy: Late Decompressive Hemicraniectomy for Malignant Middle Cerebral Artery Stroke and the Role of Infarct Growth Rate

Objective and Methods. The outcome in late decompressive hemicraniectomy in malignant middle cerebral artery stroke and the optimal timings of surgery has not been addressed by the randomized trials and pooled analysis. Retrospective, multicenter, cross-sectional study to measure outcome following DHC under 48 or over 48 hours using the modified Rankin scale [mRS] and dichotomized as favorable ≤4 or unfavorable >4 at three months. Results. In total, 137 patients underwent DHC. Functional outcome analyzed as mRS 0–4 versus mRS 5-6 showed no difference in this split between early and late operated on patients [P=0.140] and mortality [P=0.975]. Multivariate analysis showed that age ≥ 55 years, MCA with additional infarction, septum pellucidum deviation ≥1 cm, and uncal herniation were independent predictors of poor functional outcome at three months. In the “best” multivariate model, second infarct growth rate [IGR2] >7.5 ml/hr, MCA with additional infarction, and patients with temporal lobe involvement were independently associated with surgery under 48 hours. Both first infarct growth rate [IGR1] and second infarct growth rate [IGR2] were nearly double [P<0.001] in patients with early surgery [under 48 hours]. Conclusions. The outcome and mortality in malignant middle cerebral artery stroke patients operated on over 48 hours of stroke onset were comparable to those of patients operated on less than 48 hours after stroke onset. Our data identifies IGR, temporal lobe involvement, and middle cerebral artery with additional infarct as independent predictors for early surgery

Additional file 1: Table S1. of Effectiveness, safety and health-related quality of life of multiple sclerosis patients treated with fingolimod: results from a 12-month, real-world, observational PERFORMS study in the Middle East

Patient disposition. (DOCX 51 kb