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We have developed a web-based tool to predict lung cancer patient's survival probability using previously developed survivability prediction software architecture. Four statistical models are included in this version, three for non-small cell lung cancer and one for limited-stage small cell lung cancer. To make the software tool more accessible and convenient for doctors and patients in a clinical...
Hemorrhagic shock is the cause of one third of deaths resulting from injury in the world. Early diagnosis of hemorrhagic shock makes it possible for physicians to treat patients successfully. The objective of this study was to select an optimal survival prediction model using physiological parameters from rats during our hemorrhagic experiment. These physiological parameters were used for the training...
The cardiac action potential (AP) is produced by the orchestrated functions of ion channel dynamics. The coordinated functions can be simulated by computational cardiac cell models, which could not only overcome the practical and ethical limitations in physical experiments but also provide predictive insights on the underlying mechanisms. This investigation is aimed at modeling the variations of cardiac...
Bivalirudin is direct thrombin inhibitor used in patients with heparin-induced thrombocytopenia. A pharmacokinetic and — dynamic model that predicts the partial thromboplastin time (PTT) based on the past infusion rates of bivalirudin following dose adjustment would be useful to guide optimal therapy. In this retrospective study we randomized 132 patients to a derivation and a validation cohort, and...
Machine learning has been largely applied to analyze data in various domains, but it is still new to personalized medicine, especially dose individualization. In this paper, we focus on the prediction of drug concentrations using Support Vector Machines (S VM) and the analysis of the influence of each feature to the prediction results. Our study shows that SVM-based approaches achieve similar prediction...
A multiple dataset model fitting approach for improving parameter reliability in action potential modeling is presented. A robust generic cardiac ionic model employing membrane currents based on two-gate Hodgkin-Huxley kinetics is described. Its generic nature allows it to accurately reproduce action potential waveforms in heterogeneous cardiac tissue by optimizing parameters governing ion channel...
A model-based approach that integrates known portion of the cardiovascular system and unknown portion through a parameter estimation to predict evolution of the mean arterial pressure is considered. The unknown portion corresponds to the neural portion that acts like a controller that takes corrective actions to regulate the arterial blood pressure at a constant level. The input to the neural part...
This paper proposes an adaptive neuro-fuzzy model to study the pathophysiology of essential hypertension. Using diverse inputs such as risk factors, physical relations and medical interventions, and states that include both transient and resting states for key physiological variables (blood pressure, total peripheral resistance), it can roughly predict both real-time and long-term blood pressure change...
A balance control model was applied to interpret how subjects with a severe vestibular loss (VL) used vibrotactile information from a balance prosthesis to enhance balance control. Experimental data were from 5 VL subjects standing with eyes closed and responding to continuous pseudorandom surface tilts of the stance platform. Results showed that vibrotactile feedback information reduced sway at frequencies...
The impact of gravity during head-up tilt, a test often used in the clinic to diagnose patients who suffer from dizziness or frequent episodes of syncope, is not well described. This study uses mathematical modeling to analyze experimental blood pressure data measured at the level of the aorta and the carotid sinuses in a healthy volunteer. During head-up tilt the head is lifted above the heart stimulating...
A computational mutagenesis methodology founded upon a structure-dependent and knowledge-based four-body statistical potential is utilized in generating feature vectors that characterize over 8500 individual amino acid substitutions occurring in seven proteins, each mutant having been experimentally ascertained for its relative effect on native protein activity. The proteins are diverse with respect...
To develop hippocampal prosthetic devices that can restore the memory-dependent cognitive functions lost in diseases or injuries, it is essential to build a computational model that sufficiently captures the transformations of multiple memories performed by hippocampal sub-regions. A universal model with a single set of coefficients for all memories is desirable, since it can transform the memories...
We present a novel methodology for modeling the interactions between neuronal ensembles that utilizes the concept of Principal Dynamic Modes (PDM) and their associated nonlinear functions (ANF). This new approach seeks to reduce the complexity of the multi-input/multi-output (MIMO) model of the interactions between neuronal ensembles — an issue of critical practical importance in scaling up the MIMO...
Gene expression and genome wide association data have provided researchers the opportunity to study many complex traits and diseases. When designing prognostic and predictive models capable of phenotypic classification in this area, significant reduction of dimensionality through stringent filtering and/or feature selection is often deemed imperative. Here, this work challenges this presumption through...
Stem cell expansion culture aims to generate sufficient number of clinical-grade cells for cell-based therapies. One challenge for ex vivo expansion is to decide the appropriate time to perform subculture. Traditionally, this decision has been reliant on human estimation of cell confluency and predicting when confluency will approach a desired threshold. However, the use of human operators results...
Recent studies have highlighted the importance of system identification as an approach for assessing sensory processing in humans using electroencephalography (EEG). These studies typically use linear impulse response estimates of visual and, more recently, auditory function. These methods, which are known as the VESPA and AESPA (Visual/Auditory Evoked Spread Spectrum Analysis) respectively, have...
We describe a method for performing modeling and simulation to predict the strain and stress experienced by tissues resulting from reconstructive cardiothoracic surgery. Stress computation is an important predictor of the quality and longevity of a repair and can therefore be used as guidance by a surgeon when deciding between various repair options. This paper uses the mitral valve repair as a use...
Local volume averaging method and local mass (drug) equilibrium were used for developing a mathematical model for transient drug transport and elimination in the liver. Taking into account the liver porosity and tortuosity, physio-chemical properties of the drug, the drug effective diffusivity, dispersion, convection, local plasma-hepatocyte equilibrium and hepatocellular drug metabolism, the governing...
Mathematical models are used extensively in studies of cardiac electrophysiology and arrhythmia mechanisms. Models can generate novel predictions, suggest experiments, and provide a quantitative understanding of underlying mechanisms. Limitations of present modeling approaches, however, include non-uniqueness of both parameters and the models themselves, and difficulties in accounting for experimental...
During biopsies, breast tissue is subjected to displacement upon needle indentation, puncture, and penetration. Thus, accurate needle placement requires pre-operative predictions of the target motions. In this paper, we used ultrasound elastography measurements to non-invasively predict elastic properties of breast tissue phantoms. These properties were used in finite element (FE) models of indentation...
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