arXiv subjects

Ph. D

Publications and source records attributed to Ph. D.

27 records · Page 2Linked to original sources

arXiv2020

A Robust Statistical method to Estimate the Intervention Effect with Longitudinal Data

Segmented regression is a standard statistical procedure used to estimate the effect of a policy intervention on time series outcomes. This statistical method assumes the normality of the outcome variable, a large sample size, no autocorrelation in the observations, and a linear trend over time. Also, segmented regression is very sensitive to outliers. In a small sample study, if the outcome variable does not follow a Gaussian distribution, then using segmented regression to estimate the intervention effect leads to incorrect inferences. To address the small sample problem and non-normality in the outcome variable, including outliers, we describe and develop a robust statistical method to estimate the policy intervention effect in a series of longitudinal data. A simulation study is conducted to demonstrate the effect of outliers and non-normality in the outcomes by calculating the power of the test statistics with the segmented regression and the proposed robust statistical methods. Moreover, since finding the sampling distribution of the proposed robust statistic is analytically difficult, we use a nonparametric bootstrap technique to study the properties of the sampling distribution and make statistical inferences. Simulation studies show that the proposed method has more power than the standard t-test used in segmented regression analysis under the non-normality error distribution. Finally, we use the developed technique to estimate the intervention effect of the Istanbul Declaration on illegal organ activities. The robust method detected more significant effects compared to the standard method and provided shorter confidence intervals.

stat.AP↗

arXiv2020

Assessment of Amazon Comprehend Medical: Medication Information Extraction

In November 27, 2018, Amazon Web Services (AWS) released Amazon Comprehend Medical (ACM), a deep learning based system that automatically extracts clinical concepts (which include anatomy, medical conditions, protected health information (PH)I, test names, treatment names, and medical procedures, and medications) from clinical text notes. Uptake and trust in any new data product relies on independent validation across benchmark datasets and tools to establish and confirm expected quality of results. This work focuses on the medication extraction task, and particularly, ACM was evaluated using the official test sets from the 2009 i2b2 Medication Extraction Challenge and 2018 n2c2 Track 2: Adverse Drug Events and Medication Extraction in EHRs. Overall, ACM achieved F-scores of 0.768 and 0.828. These scores ranked the lowest when compared to the three best systems in the respective challenges. To further establish the generalizability of its medication extraction performance, a set of random internal clinical text notes from NYU Langone Medical Center were also included in this work. And in this corpus, ACM garnered an F-score of 0.753.

cs.CL↗

arXiv2019

Intracranial Hemorrhage Segmentation Using Deep Convolutional Model

Traumatic brain injuries could cause intracranial hemorrhage (ICH). ICH could lead to disability or death if it is not accurately diagnosed and treated in a time-sensitive procedure. The current clinical protocol to diagnose ICH is examining Computerized Tomography (CT) scans by radiologists to detect ICH and localize its regions. However, this process relies heavily on the availability of an experienced radiologist. In this paper, we designed a study protocol to collect a dataset of 82 CT scans of subjects with traumatic brain injury. Later, the ICH regions were manually delineated in each slice by a consensus decision of two radiologists. Recently, fully convolutional networks (FCN) have shown to be successful in medical image segmentation. We developed a deep FCN, called U-Net, to segment the ICH regions from the CT scans in a fully automated manner. The method achieved a Dice coefficient of 0.31 for the ICH segmentation based on 5-fold cross-validation. The dataset is publicly available online at PhysioNet repository for future analysis and comparison.

eess.IV↗

arXiv2019

A Single Differential Equation Description of Membrane Properties Underlying the Action Potential and the Axon Electric Field

In a succession of articles published over 65 years ago, Sir Alan Lloyd Hodgkin and Sir Andrew Fielding Huxley established what now forms our physical understanding of excitation in nerve, and how the axon conducts the action potential. They uniquely quantified the movement of ions in the nerve cell during the action potential, and demonstrated that the action potential is the result of a depolarizing event across the cell membrane. They confirmed that a complete depolarization event is followed by an abrupt increase in voltage that propagates longitudinally along the axon, accompanied by considerable increases in membrane conductance. In an elegant theoretical framework, they rigorously described fundamental properties of the Na+ and K+ conductances intrinsic to the action potential. Notwithstanding the elegance of Hodgkin and Huxley's incisive and explicative series of discoveries, their model is mathematically complex, relies on no small number of stochastic factors, and has no analytical solution. Solving for the membrane action potential and the ionic currents requires integrations approximated using numerical methods. In this article I present an analytical formalism of the nerve action potential, Vm and that of the accompanying cell membrane electric field, Em. To conclude, I present a novel description of Vm in terms of a single, nonlinear differential equation. This is an original stand-alone article: the major contribution is the latter, and how this description coincides with the cell membrane electric field. This work has necessitated unifying information from two preceding papers, each being concerned with the development of closed-form descriptions of the nerve action potential.

physics.bio-ph↗

arXiv2018

Wearable-based Mediation State Detection in Individuals with Parkinson's Disease

One of the most prevalent complaints of individuals with mid-stage and advanced Parkinson's disease (PD) is the fluctuating response to their medication (i.e., ON state with maximum benefit from medication and OFF state with no benefit from medication). In order to address these motor fluctuations, the patients go through periodic clinical examination where the treating physician reviews the patients' self-report about duration in different medication states and optimize therapy accordingly. Unfortunately, the patients' self-report can be unreliable and suffer from recall bias. There is a need to a technology-based system that can provide objective measures about the duration in different medication states that can be used by the treating physician to successfully adjust the therapy. In this paper, we developed a medication state detection algorithm to detect medication states using two wearable motion sensors. A series of significant features are extracted from the motion data and used in a classifier that is based on a support vector machine with fuzzy labeling. The developed algorithm is evaluated using a dataset with 19 PD subjects and a total duration of 1,052.24 minutes (17.54 hours). The algorithm resulted in an average classification accuracy of 90.5%, sensitivity of 94.2%, and specificity of 85.4%.

cs.CV↗

arXiv2018

Geared Rotationally Identical and Invariant Convolutional Neural Network Systems

Theorems and techniques to form different types of transformationally invariant processing and to produce the same output quantitatively based on either transformationally invariant operators or symmetric operations have recently been introduced by the authors. In this study, we further propose to compose a geared rotationally identical CNN system (GRI-CNN) with a small step angle by connecting networks of participated processes at the first flatten layer. Using an ordinary CNN structure as a base, requirements for constructing a GRI-CNN include the use of either symmetric input vector or kernels with an angle increment that can form a complete cycle as a "gearwheel". Four basic GRI-CNN structures were studied. Each of them can produce quantitatively identical output results when a rotation angle of the input vector is evenly divisible by the step angle of the gear. Our study showed when an input vector rotated with an angle does not match to a step angle, the GRI-CNN can also produce a highly consistent result. With a design of using an ultra-fine gear-tooth step angle (e.g., 1 degree or 0.1 degree), all four GRI-CNN systems can be constructed virtually isotropically.

cs.NE↗

arXiv2015

Structure-Constrained Basis Pursuit for Compressed Sensing

In compressive sensing (CS) theory, as the number of samples is decreased below a minimum threshold, the average error of the recovery increases. Sufficient sampling is either required for quality reconstruction or the error is resignedly accepted. However, most CS work has not taken advantage of the inherent structure in a variety of signals relevant to engineering applications. Hence, this paper proposes a new method of recovery built on basis pursuit (BP), called Structure-Constrained Basis Pursuit (SCBP), that constrains signals based on known structure rather than through extra sampling. Preliminary assessments of this method on TIMIT recordings of the speech phoneme /aa/ show a substantial decrease in error: with a fixed 5:1 compression ratio the average recovery error is 23.8% lower versus vanilla BP. More significantly, this method can be applied to any CS application that samples structured data, such as FSK waveforms, speech, and tones. In these cases, higher compression ratios can be reached with comparable error.

cs.IT↗

arXiv2009

Order Lambda**3 Parameterization of Neutrino (e,muon, tau, tau(prime)) Flavor Oscillations in a Simplified SM4 Model and Associated VCKM, VMNS, and VBIMAX Matrixes

Arguments are presented based on particle phenomenology and the requirement for Unitarity for a complex valued postulated four generation CKM Matrix (VCKM ) based on a Sequential Fourth Generation Model (sometimes named SM4). A modified four generation QCD Standard Model Lagrangian is utilized per SM4. A four generation neutrino mass mixing MNS Matrix, (VMNS) is estimated utilizing a Unitary (to Order (Lambda**k), k = 1, 2, 3, 4, etc) 4 x 4 Bimaximal Matrix, VBIMAX. The Unitary VBIMAX is based on a weighted 3 x 3 VBIMAX scheme and is studied in conjunction with the postulated four generation VCKM complex Unitary Matrix. A single parameter has been utilized in our analysis along with three complex DELTA(i,j) phases. A four generation Wolfenstein Parameterization of VCKM is deduced which is valid for order Lambda**3. Experimental implications of the model are discussed. The issues of Baryogenesis in the context of Leptogenesis associated with MNS Matrix neutrino mixing and Baryogenesis associated with CKM Matrix quark mixing are discussed. These issues are studied in the context of the postulated complex four generation CKM Matrix and resulting complex MNS Matrix, predicted CP violating parameters, and a Fourth Generation Neutrino mass bound of equal to or greater than 50 GeV. Our analysis is valid to and includes order Lambda**3 terms. Our work is a mathematical analysis of the consequences of such model. Classification Codes (PACS-1996): 14.60.Pq, 23.40.Bw, 98.80.Cq 14.60.Pq Neutrino mass and mixing 23.40.Bw Weak-interaction and lepton (including neutrino) aspects 98.80.Cq Particle-theory and field- theory models of the early Universe Keywords: Neutrino Oscillations, Neutrino Flavor Oscillations, Fourth Generation,Unitarity, Baryogenesis, Leptogenesis, CP Violations

physics.gen-ph↗

arXiv2000

System-Level Genetic Codes: An Explanation for Biological Complexity

Complex systems with tightly coadapted parts frequently appear in living systems and are difficult to account for through Darwinian evolution, that is random variation and natural selection, if the constituent parts are independently coded in the genetic code. If the parts are independently coded, multiple simultaneous mutations appear necessary to create or modify these systems. It is generally believed that most proteins are independently coded. The textbook rule is one gene for one enzyme. Thus, biochemical systems with tightly coadapted parts such as the blood clotting cascade pose a difficulty for Darwinian evolution. This problem can be overcome if the current understanding of the genetic code is incomplete and a system-level genetic code in which seemingly independent proteins are encoded in an interdependent, highly correlated manner exists. The methods by which human beings design and fabricate complex systems of tightly coadapted parts are explored for insights into the requirements for a system-level genetic code. Detailed examples of system-level codes for networks of matching parts are presented. The implications of identifying and deciphering the system-level genetic code if it exists for the prevention, treatment, and cure of heart disease, cancer, immune disorders, and for rational drug design are discussed.

nlin.AO↗