Engineering topics

Cuellar, Leticia

Publications and source records attributed to Cuellar, Leticia.

3 records

DOE OSTI2018

CAT: computer aided triage improving upon the Bayes risk through ε-refusal triage rules

Manual extraction of information from electronic pathology (epath) reports to populate the Surveillance, Epidemiology, and End Result (SEER) database is labor intensive. Systematizing the data extraction automatically using machine-learning (ML) and natural language processing (NLP) is desirable to reduce the human labor required to populate the SEER database and to improve the timeliness of the data. This enables scaling up registry efficiency and collection of new data elements. To ensure the integrity, quality, and continuity of the SEER data, the misclassification error of ML and NPL algorithms needs to be negligible. Current algorithms fail to achieve the precision of human experts who can bring additional information in their assessments. Differences in registry format and the desire to develop a common information extraction platform further complicate the ML/NLP tasks. The purpose of our study is to develop triage rules to partially automate registry workflow to improve the precision of the auto-extracted information. This paper presents a mathematical framework to improve the precision of a classifier beyond that of the Bayes classifier by selectively classifying item that are most likely to be correct. This results in a triage rule that only classifies a subset of the item. We characterize the optimal triage rule and demonstrate its usefulness in the problem of classifying cancer site from electronic pathology reports to achieve a desired precision. From the mathematical formalism, we propose a heuristic estimate for triage rule based on post-processing the soft-max output from standard machine learning algorithms. We show, in test cases, that the triage rule significantly improve the classification accuracy.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2012

2012 PATRIOT SCRIPT User's Guide

PATRIOT Script is an application that executes Patriot batch runs. This document provides a description of this application and how to run it. The basic user access tool PATRIOT Client allows a user to generate several most reliable paths in one run: one can specify a list of sources (origins) and targets, and PATRIOT finds for a given architecture option and one choice of device all the most reliable paths between all these sources and targets. The main objective of PATRIOT Script is to provide a tool for making automatic PATRIOT runs not only for a prespecified set of sources and targets, but also for a pre-specified set of devices and various architecture options. Running PATRIOT Script requires two basic steps that will be explained in more detail next: (1) Pre-preparation of an excel spreadsheet with the information about the desired runs; and (2) Opening the PATRIOT Script application, reading in the excel-spreadsheet and running the desired scenarios. Sections 1 and 2 explain each of these steps, and section 3 describes the output of the PATRIOT Script. For a detail description of the models and data behind PATRIOT and a detailed explanation of all the architecture options see [1]. For instructions of how to run PATRIOT Client see [2].

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗

DOE OSTI2012

Uncertainty Quantification of Hypothesis Testing for the Integrated Knowledge Engine

The Integrated Knowledge Engine (IKE) is a tool of Bayesian analysis, based on Bayesian Belief Networks or Bayesian networks for short. A Bayesian network is a graphical model (directed acyclic graph) that allows representing the probabilistic structure of many variables assuming a localized type of dependency called the Markov property. The Markov property in this instance makes any node or random variable to be independent of any non-descendant node given information about its parent. A direct consequence of this property is that it is relatively easy to incorporate new evidence and derive the appropriate consequences, which in general is not an easy or feasible task. Typically we use Bayesian networks as predictive models for a small subset of the variables, either the leave nodes or the root nodes. In IKE, since most applications deal with diagnostics, we are interested in predicting the likelihood of the root nodes given new observations on any of the children nodes. The root nodes represent the various possible outcomes of the analysis, and an important problem is to determine when we have gathered enough evidence to lean toward one of these particular outcomes. This document presents criteria to decide when the evidence gathered is sufficient to draw a particular conclusion or decide in favor of a particular outcome by quantifying the uncertainty in the conclusions that are drawn from the data. The material in this document is organized as follows: Section 2 presents briefly a forensics Bayesian network, and we explore evaluating the information provided by new evidence by looking first at the posterior distribution of the nodes of interest, and then at the corresponding posterior odds ratios. Section 3 presents a third alternative: Bayes Factors. In section 4 we finalize by showing the relation between the posterior odds ratios and Bayes factors and showing examples these cases, and in section 5 we conclude by providing clear guidelines of how to use these for the type of Bayesian networks used in IKE.

97 MATHEMATICS AND COMPUTING↗