Morteza Noshad

University of Michigan | U-M · Department of Electrical Engineering and Computer Science (EECS)

Objective: To determine whether novel measures of contextual factors from multi-site electronic health record (EHR) audit log data can explain variation in clinical process outcomes. Materials and methods: We selected one widely-used process outcome: emergency department (ED)-based team time to deliver tissue plasminogen activator (tPA) to patie...

Background The Centers for Disease Control and Prevention identify antibiotic prescribing stewardship as the most important action to combat increasing antibiotic resistance. Clinicians balance broad empiric antibiotic coverage vs. precision coverage targeting only the most likely pathogens. We investigate the utility of machine learning-based clin...

Advancing diagnostic testing capabilities such as clinical next generation sequencing methods offer the potential to diagnose, risk stratify, and guide specialized treatment, but must be balanced against the escalating costs of healthcare to identify patient cases most likely to benefit from them. Heme-STAMP (Stanford Actionable Mutation Panel for...

Objective Mapping real-world practice patterns vs. deviations from intended guidelines and protocols is necessary to identify and improve the quality of care for emergent medical conditions like acute ischemic stroke. Most status-quo process identification relies on expert opinion or direct observation, which can be biased or limited in scalability...

Advances in medical science simultaneously benefit patients while contributing to an over-whelming complexity of medicine with a decision space of thousands of possible diagnoses, tests, and treatment options. Medical expertise becomes the most important scarce health-care resource, reflected in tens of millions in the US alone with deficient acces...

Data-driven innovation is propelled by recent scientific advances, rapid technological progress, substantial reductions of manufacturing costs, and significant demands for effective decision support systems. This has led to efforts to collect massive amounts of heterogeneous and multisource data, however, not all data is of equal quality or equally...

High quality patient care through timely, precise and efficacious management depends not only on the clinical presentation of a patient, but the context of the care environment to which they present. Understanding and improving factors that affect streamlined workflow, such as provider or department busyness or experience, are essential to improvin...

The growing demand for key healthcare resources such as clinical expertise and facilities has motivated the emergence of artificial intelligence (AI) based decision support systems. We address the problem of predicting clinical workups for specialty referrals. As an alternative for manually-created clinical checklists, we propose a data-driven mode...

Bounding the best achievable error probability for binary classification problems is relevant to many applications including machine learning, signal processing, and information theory. Many bounds on the Bayes binary classification error rate depend on information divergences between the pair of class distributions. Recently, the Henze–Penrose (HP...

We address the problem of learning to benchmark the best achievable classifier performance. In this problem the objective is to establish statistically consistent estimates of the Bayes misclassification error rate without having to learn a Bayes-optimal classifier. Our learning to benchmark framework improves on previous work on learning bounds on...

The Mutual Information (MI) is an often used measure of dependency between two random variables utilized in information theory, statistics and machine learning. Recently several MI estimators have been proposed that can achieve paramet-ric MSE convergence rate. However, most of the previously proposed estimators have high computational complexity o...

Thanks to EDGE, a recently proposed scalable and optimal estimator of Mutual Information, It is shown that the Information Bottleneck theory of deep learning, proposed by Naftali Tishby, applies for a wide range of activations such as ReLU and Maxpooling! https://arxiv.org/pdf/1801.09125.pdf

Bounding the best achievable error probability for binary classification problems is relevant to many applications including machine learning, signal processing, and information theory. Many bounds on the Bayes binary classification error rate depend on information divergences between the pair of class distributions. Recently, the Henze-Penrose (HP...

We propose a unified method for empirical non-parametric estimation of general Mutual Information (MI) function between the random vectors in $\mathbb{R}^d$ based on $N$ i.i.d. samples. The proposed low complexity estimator is based on a bipartite graph, referred to as dependence graph. The data points are mapped to the vertices of this graph using...

We propose a scalable divergence estimation method based on hashing. Consider two continuous random variables $X$ and $Y$ whose densities have bounded support. We consider a particular locality sensitive random hashing, and consider the ratio of samples in each hash bin having non-zero numbers of Y samples. We prove that the weighted average of the...

Meta learning of optimal classifier error rates allows an experimenter to empirically estimate the intrinsic ability of any estimator to discriminate between two populations, circumventing the difficult problem of estimating the optimal Bayes classifier. To this end we propose a weighted nearest neighbor (WNN) graph estimator for a tight bound on t...

We propose a direct estimation method for R\'{e}nyi and f-divergence measures based on a new graph theoretical interpretation. Suppose that we are given two sample sets $X$ and $Y$, respectively with $N$ and $M$ samples, where $\eta:=M/N$ is a constant value. Considering the $k$-nearest neighbor ($k$-NN) graph of $Y$ in the joint data set $(X,Y)$,...

Information theoretic measures (e.g. the Kullback Liebler divergence and Shannon mutual information) have been used for exploring possibly nonlinear multivariate dependencies in high dimension. If these dependencies are assumed to follow a Markov factor graph model, this exploration process is called structure discovery. For discrete-valued samples...

The generalized belief propagation (GBP), introduced by Yedidia et al., is an extension of the belief propagation (BP) algorithm, which is widely used in different problems involved in calculating exact or approximate marginals of probability distributions. In many problems, it has been observed that the accuracy of GBP considerably outperforms tha...

In this paper, we derive a theoretical model for the higher-order four-wave mixing (FWM) power in wavelength division multiplexing networks with non-zero dispersion shifted fibers for the first time. We have investigated the higher-order FWM power theoretically and by numerical simulations. Dividing the fiber into a finite number of elements and ap...

In this paper, we derive a theoretical model for the higher order FWM power in WDM networks with NZDSF fibers for the first time. We have investigated the higher order FWM power theoretically and by numerical simulations. Dividing the fiber into finite number of elements and applying the boundary conditions, allow us derive an expression for second...

In this article we propose a novel mechano-optical switch and dual channel transmitter based on photonic crystal. The device consists of two waveguides and an elliptical cavity in a square lattice structure. Two optical signals at separate wavelengths are inserted in the input waveguide. The elliptical cavity can be rotated using a mechanical force...

In this work, we present a heterostructure All Optical Flip-Flop configuration based on all optical switching with Kerr nonlinear photonic crystal. In this Square-Hexagonal structure, we propose three different schemes for the cavities in order to show the tradeoff between switching time and triggering power. Loss in the system is reasonably low be...

We investigate the dependence of resonance frequency of micro-cavity on its geometry for T shaped-beam splitter using finite difference time domain (FDTD) method. We derive resonance frequencies and output powers for elliptical cavity with various radiuses in both vertical and horizontal directions( and direction). The results show that resonance f...

Photonic crystal, Nonlinear material, All optical switching, Resonance frequency, Logical gate.