Personalized modeling for real-time pressure ulcer prevention in sitting posture

, Ischial pressure ulcer is an important risk for every paraplegic person and a major public health issue. Pressure ulcers appear following excessive compression of buttock's soft tissues by bony structures, and particularly in ischial and sacral bones. Current prevention techniques are mainly based on daily skin inspection to spot red patches or injuries. Nevertheless, most pressure ulcers occur internally and are difficult to detect early. Estimating internal strains within soft tissues could help to evaluate the risk of pressure ulcer. A subject-specific biomechanical model could be used to assess internal strains from measured skin surface pressures. However, a realistic 3D non-linear Finite Element buttock model, with different layers of tissue materials for skin, fat and muscles, requires somewhere between minutes and hours to compute, therefore forbidding its use in a real-time daily prevention context. In this article, we propose to optimize these computations by using a reduced order modeling technique (ROM) based on proper orthogonal decompositions of the pressure and strain fields coupled with a machine learning method. ROM allows strains to be evaluated inside the model interactively (i.e. in less than a second) for any pressure field measured below the buttocks. In our case, with only 19 modes of variation of pressure patterns, an error divergence of one percent is observed compared to the full scale simulation for evaluating the strain field. This reduced model could therefore be the first step towards interactive pressure ulcer prevention in a daily setup. Highlights-Buttocks biomechanical modelling,-Reduced order model,-Daily pressure ulcer prevention

Pemilihan kerjaya di kalangan pelajar aliran perdagangan sekolah menengah teknik : satu kajian kes

This research is a survey to determine the career chosen of form four student in commerce streams. The important aspect of the career chosen has been divided into three, first is information about career, type of career and factor that most influence students in choosing a career. The study was conducted at Sekolah Menengah Teknik Kajang, Selangor Darul Ehsan. Thirty six form four students was chosen by using non-random sampling purpose method as respondent. All information was gather by using questionnaire. Data collected has been analyzed in form of frequency, percentage and mean. Results are performed in table and graph. The finding show that information about career have been improved in students career chosen and mass media is the main factor influencing students in choosing their career

Personalized modeling for prediction with decision-path models

Deriving predictive models in medicine typically relies on a population approach where a single model is developed from a dataset of individuals. In this paper we describe and evaluate a personalized approach in which we construct a new type of decision tree model called decision-path model that takes advantage of the particular features of a given person of interest. We introduce three personalized methods that derive personalized decision-path models. We compared the performance of these methods to that of Classification And Regression Tree (CART) that is a population decision tree to predict seven different outcomes in five medical datasets. Two of the three personalized methods performed statistically significantly better on area under the ROC curve (AUC) and Brier skill score compared to CART. The personalized approach of learning decision path models is a new approach for predictive modeling that can perform better than a population approach

Tissue-scale, personalized modeling and simulation of prostate cancer growth

Recently, mathematical modeling and simulation of diseases and their treatments have enabled the prediction of clinical outcomes and the design of optimal therapies on a personalized (i.e., patient-specific) basis. This new trend in medical research has been termed “predictive medicine.” Prostate cancer (PCa) is a major health problem and an ideal candidate to explore tissue-scale, personalized modeling of cancer growth for two main reasons: First, it is a small organ, and, second, tumor growth can be estimated by measuring serum prostate-specific antigen (PSA, a PCa biomarker in blood), which may enable in vivo validation. In this paper, we present a simple continuous model that reproduces the growth patterns of PCa. We use the phase-field method to account for the transformation of healthy cells to cancer cells and use diffusion-reaction equations to compute nutrient consumption and PSA production. To accurately and efficiently compute tumor growth, our simulations leverage isogeometric analysis (IGA). Our model is shown to reproduce a known shape instability from a spheroidal pattern to fingered growth. Results of our computations indicate that such shift is a tumor response to escape starvation, hypoxia, and, eventually, necrosis. Thus, branching enables the tumor to minimize the distance from inner cells to external nutrients, contributing to cancer survival and further development. We have also used our model to perform tissue-scale, personalized simulation of a PCa patient, based on prostatic anatomy extracted from computed tomography images. This simulation shows tumor progression similar to that seen in clinical practice.Peer ReviewedPostprint (published version

Personalized modeling for medical decision support

Personalized modeling is an emerging approach, in which a model is created for every new input vector of the problem space based on its nearest neighbors using transductive reasoning (Kasabov, 2007c; Vapnik, 1998). The underlying philosophy of this approach when applied to medicine is that each patient is an individual. Therefore, each patient requires and deserves a personalized treatment model that predicts the best possible outcomes for the patient. This study proposes a novel integrated evolving framework and system for personalized modeling (evoPM); an extension of a model proposed by Kasabov and Hu (Kasabov & Hu, 2011). By allowing users to select the most important features, optimize nearest neighbors and model parameters, the model provides higher accuracy and personalized knowledge than global and local modeling approaches. The evoPM creates a personalized model for each test sample with unique optimal sets of features, neighborhood and model parameters. In addition, the system keeps evolving and is adaptable to any new incoming data vectors. The already created personalized model can be further evolved on new data entering in the neighborhood. Currently, the amount of available spatio-temporal data (STD) is growing exponentially, thus suitable techniques to e ectively and e ciently analyze and process this vast quantity information are urgently needed. Evolving spiking neural networks (eSNN), an extension of spiking neural networks (SNN), is an emerging computational technique for STD analysis. Evolving SNNs learn STD by rst converting temporal changes in the input variables into spike trains, then applying learning procedures to map spatio-temporal patterns detected in the data into temporal spiking activity of spatially located neurons. This study introduces two recently proposed methods for spatio-temporal pattern recognition, the extended eSNN framework (EESNN) (Hamed, Kasabov, Shamsuddin, Widiputra & Dhoble, 2011) and the recurrent network reservoir structure of eSNN (reSNN) using liquid state machine (LSM) (Schliebs, Hamed & Kasabov, 2011). Both methods are the rst time applied to evaluate the spatio-temporal weather and stroke occurrence data as a case study. The evoPM is applied as a classi er to learn the responses from the reSNN model. The novel evoPM framework and system brings several advances over existing per sonalized modeling methods. These are summarized below: The integrated evolving personalized modeling system is developed based on an emerging novel technology namely eSNN; A recently developed population-based heuristic optimization approach called gravitational search algorithm (GSA) is applied to improve the robustness and general disability of feature selection, neighborhood, model and its parameters optimization for classification, diagnostic and prognostic problems; The standard diseased classification system is replaced by personalized risk evaluation. The evoPM system and framework is novel applied to stroke data as case studies. The novel method is validated on several benchmark cancer gene expression datasets and stroke data. The model outputs are compared with those of traditional global, local and personalized modeling methods. The results of all studies show that evoPM performs consistently better than the traditional methods. In particular, it develops more useful knowledge discovery for medical decision support for cancer diagnosis and prognosis due to it selects the optimal sets of genes and disease classification parameters for each individual patient

Information methods for predicting risk and outcome of stroke

Stroke is a major cause of disability and mortality in most economically developed countries. It is the second leading cause of death worldwide (after cancer and heart disease) [55.1, 2] and a major cause of disability in adults in developed countries [55.3]. Personalized modeling is an emerging effective computational approach, which has been applied to various disciplines, such as in personalized drug design, ecology, business, and crime prevention; it has recently become more prominent in biomedical applications. Biomedical data on stroke risk factors and prognostic data are available in a large volume, but the data are complex and often difficult to apply to a specific person. Individualizing stroke risk prediction and prognosis will allow patients to focus on risk factors specific to them, thereby reducing their stroke risk and managing stroke outcomes more effectively. This chapter reviews various methods–conventional statistical methods and computational intelligent modeling methods for predicting risk and outcome of stroke

Personalized neuromusculoskeletal modeling to improve treatment of mobility impairments: a perspective from European research sites

Mobility impairments due to injury or disease have a significant impact on quality of life. Consequently, development of effective treatments to restore or replace lost function is an important societal challenge. In current clinical practice, a treatment plan is often selected from a standard menu of options rather than customized to the unique characteristics of the patient. Furthermore, the treatment selection process is normally based on subjective clinical experience rather than objective prediction of post-treatment function. The net result is treatment methods that are less effective than desired at restoring lost function. This paper discusses the possible use of personalized neuromusculoskeletal computer models to improve customization, objectivity, and ultimately effectiveness of treatments for mobility impairments. The discussion is based on information gathered from academic and industrial research sites throughout Europe, and both clinical and technical aspects of personalized neuromusculoskeletal modeling are explored. On the clinical front, we discuss the purpose and process of personalized neuromusculoskeletal modeling, the application of personalized models to clinical problems, and gaps in clinical application. On the technical front, we discuss current capabilities of personalized neuromusculoskeletal models along with technical gaps that limit future clinical application. We conclude by summarizing recommendations for future research efforts that would allow personalized neuromusculoskeletal models to make the greatest impact possible on treatment design for mobility impairments