Predicting the outcomes of traumatic brain injury using accurate and dynamic predictive model

Predictive models have been used widely to predict the diseases outcomes in health sector. These predictive models are emerged with new information and communication technologies. Traumatic brain injury has recognizes as a serious and crucial health problem all over the world. In order to predict brain injuries outcomes, the predictive models are still suffered with predictive performance. In this paper, we propose a new predictive model and traumatic brain injury predictive model to improve the predictive performance to classifying the disease predictions into different categories. These proposed predictive models support to develop the traumatic brain injury predictive model. A primary dataset is constructed which is based on approved set of features by the neurologist. The results of proposed model is indicated that model has achieved the best average ranking in terms of accuracy, sensitivity and specificity

Risk Adjustment In Neurocritical care (RAIN)--prospective validation of risk prediction models for adult patients with acute traumatic brain injury to use to evaluate the optimum location and comparative costs of neurocritical care: a cohort study.

OBJECTIVES: To validate risk prediction models for acute traumatic brain injury (TBI) and to use the best model to evaluate the optimum location and comparative costs of neurocritical care in the NHS. DESIGN: Cohort study. SETTING: Sixty-seven adult critical care units. PARTICIPANTS: Adult patients admitted to critical care following actual/suspected TBI with a Glasgow Coma Scale (GCS) score of < 15. INTERVENTIONS: Critical care delivered in a dedicated neurocritical care unit, a combined neuro/general critical care unit within a neuroscience centre or a general critical care unit outside a neuroscience centre. MAIN OUTCOME MEASURES: Mortality, Glasgow Outcome Scale - Extended (GOSE) questionnaire and European Quality of Life-5 Dimensions, 3-level version (EQ-5D-3L) questionnaire at 6 months following TBI. RESULTS: The final Risk Adjustment In Neurocritical care (RAIN) study data set contained 3626 admissions. After exclusions, 3210 patients with acute TBI were included. Overall follow-up rate at 6 months was 81%. Of 3210 patients, 101 (3.1%) had no GCS score recorded and 134 (4.2%) had a last pre-sedation GCS score of 15, resulting in 2975 patients for analysis. The most common causes of TBI were road traffic accidents (RTAs) (33%), falls (47%) and assault (12%). Patients were predominantly young (mean age 45 years overall) and male (76% overall). Six-month mortality was 22% for RTAs, 32% for falls and 17% for assault. Of survivors at 6 months with a known GOSE category, 44% had severe disability, 30% moderate disability and 26% made a good recovery. Overall, 61% of patients with known outcome had an unfavourable outcome (death or severe disability) at 6 months. Between 35% and 70% of survivors reported problems across the five domains of the EQ-5D-3L. Of the 10 risk models selected for validation, the best discrimination overall was from the International Mission for Prognosis and Analysis of Clinical Trials in TBI Lab model (IMPACT) (c-index 0.779 for mortality, 0.713 for unfavourable outcome). The model was well calibrated for 6-month mortality but substantially underpredicted the risk of unfavourable outcome at 6 months. Baseline patient characteristics were similar between dedicated neurocritical care units and combined neuro/general critical care units. In lifetime cost-effectiveness analysis, dedicated neurocritical care units had higher mean lifetime quality-adjusted life-years (QALYs) at small additional mean costs with an incremental cost-effectiveness ratio (ICER) of £14,000 per QALY and incremental net monetary benefit (INB) of £17,000. The cost-effectiveness acceptability curve suggested that the probability that dedicated compared with combined neurocritical care units are cost-effective is around 60%. There were substantial differences in case mix between the 'early' (within 18 hours of presentation) and 'no or late' (after 24 hours) transfer groups. After adjustment, the 'early' transfer group reported higher lifetime QALYs at an additional cost with an ICER of £11,000 and INB of £17,000. CONCLUSIONS: The risk models demonstrated sufficient statistical performance to support their use in research but fell below the level required to guide individual patient decision-making. The results suggest that management in a dedicated neurocritical care unit may be cost-effective compared with a combined neuro/general critical care unit (although there is considerable statistical uncertainty) and support current recommendations that all patients with severe TBI would benefit from transfer to a neurosciences centre, regardless of the need for surgery. We recommend further research to improve risk prediction models; consider alternative approaches for handling unobserved confounding; better understand long-term outcomes and alternative pathways of care; and explore equity of access to postcritical care support for patients following acute TBI. FUNDING: The National Institute for Health Research Health Technology Assessment programme

Investigating Brain Functional Networks in a Riemannian Framework

The brain is a complex system of several interconnected components which can be categorized at different Spatio-temporal levels, evaluate the physical connections and the corresponding functionalities. To study brain connectivity at the macroscale, Magnetic Resonance Imaging (MRI) technique in all the different modalities has been exemplified to be an important tool. In particular, functional MRI (fMRI) enables to record the brain activity either at rest or in different conditions of cognitive task and assist in mapping the functional connectivity of the brain. The information of brain functional connectivity extracted from fMRI images can be defined using a graph representation, i.e. a mathematical object consisting of nodes, the brain regions, and edges, the link between regions. With this representation, novel insights have emerged about understanding brain connectivity and providing evidence that the brain networks are not randomly linked. Indeed, the brain network represents a small-world structure, with several different properties of segregation and integration that are accountable for specific functions and mental conditions. Moreover, network analysis enables to recognize and analyze patterns of brain functional connectivity characterizing a group of subjects. In recent decades, many developments have been made to understand the functioning of the human brain and many issues, related to the biological and the methodological perspective, are still need to be addressed. For example, sub-modular brain organization is still under debate, since it is necessary to understand how the brain is functionally organized. At the same time a comprehensive organization of functional connectivity is mostly unknown and also the dynamical reorganization of functional connectivity is appearing as a new frontier for analyzing brain dynamics. Moreover, the recognition of functional connectivity patterns in patients affected by mental disorders is still a challenging task, making plausible the development of new tools to solve them. Indeed, in this dissertation, we proposed novel methodological approaches to answer some of these biological and neuroscientific questions. We have investigated methods for analyzing and detecting heritability in twin's task-induced functional connectivity profiles. in this approach we are proposing a geodesic metric-based method for the estimation of similarity between functional connectivity, taking into account the manifold related properties of symmetric and positive definite matrices. Moreover, we also proposed a computational framework for classification and discrimination of brain connectivity graphs between healthy and pathological subjects affected by mental disorder, using geodesic metric-based clustering of brain graphs on manifold space. Within the same framework, we also propose an approach based on the dictionary learning method to encode the high dimensional connectivity data into a vectorial representation which is useful for classification and determining regions of brain graphs responsible for this segregation. We also propose an effective way to analyze the dynamical functional connectivity, building a similarity representation of fMRI dynamic functional connectivity states, exploiting modular properties of graph laplacians, geodesic clustering, and manifold learning

Respiratory Sound Analysis for the Evidence of Lung Health

Significant changes have been made on audio-based technologies over years in several different fields along with healthcare industry. Analysis of Lung sounds is a potential source of noninvasive, quantitative information along with additional objective on the status of the pulmonary system. To do that medical professionals listen to sounds heard over the chest wall at different positions with a stethoscope which is known as auscultation and is important in diagnosing respiratory diseases. At times, possibility of inaccurate interpretation of respiratory sounds happens because of clinician’s lack of considerable expertise or sometimes trainees such as interns and residents misidentify respiratory sounds. We have built a tool to distinguish healthy respiratory sound from non-healthy ones that come from respiratory infection carrying patients. The audio clips were characterized using Linear Predictive Cepstral Coefficient (LPCC)-based features and the highest possible accuracy of 99.22% was obtained with a Multi-Layer Perceptron (MLP)- based classifier on the publicly available ICBHI17 respiratory sounds dataset [1] of size 6800+ clips. The system also outperformed established works in literature and other machine learning techniques. In future we will try to use larger dataset with other acoustic techniques along with deep learning-based approaches and try to identify the nature and severity of infection using respiratory sounds

Trends in Cerebrovascular Surgery and Interventions

This is an open access proceeding book of 9th European-Japanese Cerebrovascular Congress at Milan 2018. Since many experts from Europe and Japan had very important and fruitful discussion on the management of Cerebrovascular diseases, the proceeding book is very attractive for the physician and scientists of the area

Artificial Intelligence and Bank Soundness: Between the Devil and the Deep Blue Sea - Part 2

Banks have experienced chronic weaknesses as well as frequent crisis over the years. As bank failures are costly and affect global economies, banks are constantly under intense scrutiny by regulators. This makes banks the most highly regulated industry in the world today. As banks grow into the 21st century framework, banks are in need to embrace Artificial Intelligence (AI) to not only to provide personalized world class service to its large database of customers but most importantly to survive. The chapter provides a taxonomy of bank soundness in the face of AI through the lens of CAMELS where C (Capital), A(Asset), M(Management), E(Earnings), L(Liquidity), S(Sensitivity). The taxonomy partitions challenges from the main strand of CAMELS into distinct categories of AI into 1(C), 4(A), 17(M), 8 (E), 1(L), 2(S) categories that banks and regulatory teams need to consider in evaluating AI use in banks. Although AI offers numerous opportunities to enable banks to operate more efficiently and effectively, at the same time banks also need to give assurance that AI ‘do no harm’ to stakeholders. Posing many unresolved questions, it seems that banks are trapped between the devil and the deep blue sea for now

Analysis of workers\u27 compensation claims data for improving safety outcomes in agribusiness industries

Occupational injuries continue to be a major issue for non-farm agricultural workplaces such as commercial grain elevators and ethanol plants. For preventing these injuries and improving workplace safety outcomes requires learning from past incidents, and identify the most significant causes and implement targeted prevention strategies. However, obtaining detailed records of past incidents is a challenge acknowledged by investigators across several industrial sectors including agribusiness. Previous researchers suggest workers ’ compensation claims as an excellent data source to address the existing informational gaps about safety incidents and injuries in the workplace. In this study, workers’ compensation claims obtained from a leading private insurance company were investigated using statistical techniques such as chi-square tests, regression analysis, and data mining techniques such as decision trees. The study objective was to analyze these claims, identify injury causes, risks, and problem areas so supervisors and safety professionals can make decisions needed to improve safety outcomes in the workplace. The findings of this study are documented in three separate manuscripts. Since safety incidents that cause injuries and fatalities have a widespread impact, therefore mitigating these incidents using a proactive data-driven approach rather than just compliance can benefit the worker, the organization, and society-at-large

Artificial Intelligence of Things Applied to Assistive Technology: A Systematic Literature Review

According to the World Health Organization, about 15% of the world’s population has some form of disability. Assistive Technology, in this context, contributes directly to the overcoming of difficulties encountered by people with disabilities in their daily lives, allowing them to receive education and become part of the labor market and society in a worthy manner. Assistive Technology has made great advances in its integration with Artificial Intelligence of Things (AIoT) devices. AIoT processes and analyzes the large amount of data generated by Internet of Things (IoT) devices and applies Artificial Intelligence models, specifically, machine learning, to discover patterns for generating insights and assisting in decision making. Based on a systematic literature review, this article aims to identify the machine-learning models used across different research on Artificial Intelligence of Things applied to Assistive Technology. The survey of the topics approached in this article also highlights the context of such research, their application, the IoT devices used, and gaps and opportunities for further development. The survey results show that 50% of the analyzed research address visual impairment, and, for this reason, most of the topics cover issues related to computational vision. Portable devices, wearables, and smartphones constitute the majority of IoT devices. Deep neural networks represent 81% of the machine-learning models applied in the reviewed research.N/

Atypical work patterns and their associations with depressive symptoms, mental wellbeing, and sleep: findings from a UK population-based study

Objective: To examine whether, in the UK, relative to typical work patterns (open-ended, permanent, full-time, daytime, weekday employment, usually conducted at the employer’s premises), atypical work patterns (conceptualised as precarious, temporal and spatial work patterns) relate to worse or better mental health and sleep. Methods: Analysis of data from working men and women aged 16 and over, in Understanding Society, the UK Household Longitudinal Study. Regression models, adjusted for potential confounders and work conditions, estimated depressive symptoms (measured by GHQ-12), mental wellbeing (i.e., feeling good and functioning well measured by SWEMWBS), sleep duration (hours/night) and disturbance (difficulty falling and staying asleep and perceived poor quality sleep), across the three groups of atypical work patterns. Precarious was operationalised as temporary work and self-employment; spatial as remote working (including homeworking); and temporal as weekly work hours (e.g., part-time [<35 hours/week], long hours [41-54 hours/week], extra-long hours [≥55 hours/week]), nonstandard schedules (i.e., shifts and working outside 9am-5pm), and (some and most/all) weekends. Results: Cross-sectional analyses found part-time, extra-long hours, nonstandard schedules, and weekends were associated with elevated depressive symptoms, and self-employment with fewer symptoms. Mental wellbeing was poorer among nonstandard schedules and weekend workers, and better among self-employed and remote workers. Cumulative episodes of working nonstandard schedules and most/all weekends related to subsequent elevations of depressive symptoms, whilst cumulative episodes of self-employment related to fewer symptoms. Cross-sectional analysis found relative to sleeping 7-8 hours/night, remote, part-time and self-employed workers slept longer, individuals working ≥41 hours/week slept less, and those working weekends and nonstandard schedules slept both longer and shorter durations. Temporary work and all the atypical temporal work patterns were associated with sleep disturbance, whereas self-employment and remote working were inversely associated with sleep disturbance. Conclusion: Several atypical patterns, especially temporal ones, may contribute to poorer mental health and sleep; conversely self-employment and remote working may have a protective effect