Quantifying Uncertainty in Deep Learning Classification with Noise in Discrete Inputs for Risk-Based Decision Making

The use of Deep Neural Network (DNN) models in risk-based decision-making has attracted extensive attention with broad applications in medical, finance, manufacturing, and quality control. To mitigate prediction-related risks in decision making, prediction confidence or uncertainty should be assessed alongside the overall performance of algorithms. Recent studies on Bayesian deep learning helps quantify prediction uncertainty arises from input noises and model parameters. However, the normality assumption of input noise in these models limits their applicability to problems involving categorical and discrete feature variables in tabular datasets. In this paper, we propose a mathematical framework to quantify prediction uncertainty for DNN models. The prediction uncertainty arises from errors in predictors that follow some known finite discrete distribution. We then conducted a case study using the framework to predict treatment outcome for tuberculosis patients during their course of treatment. The results demonstrate under a certain level of risk, we can identify risk-sensitive cases, which are prone to be misclassified due to error in predictors. Comparing to the Monte Carlo dropout method, our proposed framework is more aware of misclassification cases. Our proposed framework for uncertainty quantification in deep learning can support risk-based decision making in applications when discrete errors in predictors are present.Comment: 31 pages, 9 figure

Traffic-Related Air Pollution and All-Cause Mortality during Tuberculosis Treatment in California.

BackgroundAmbient air pollution and tuberculosis (TB) have an impact on public health worldwide, yet associations between the two remain uncertain.ObjectiveWe determined the impact of residential traffic on mortality during treatment of active TB.MethodsFrom 2000-2012, we enrolled 32,875 patients in California with active TB and followed them throughout treatment. We obtained patient data from the California Tuberculosis Registry and calculated traffic volumes and traffic densities in 100- to 400-m radius buffers around residential addresses. We used Cox models to determine mortality hazard ratios, controlling for demographic, socioeconomic, and clinical potential confounders. We categorized traffic exposures as quintiles and determined trends using Wald tests.ResultsParticipants contributed 22,576 person-years at risk. There were 2,305 deaths during treatment for a crude mortality rate of 1,021 deaths per 10,000 person-years. Traffic volumes and traffic densities in all buffers around patient residences were associated with increased mortality during TB treatment, although the findings were not statistically significant in all buffers. As the buffer size decreased, fifth-quintile mortality hazards increased, and trends across quintiles of traffic exposure became more statistically significant. Increasing quintiles of nearest-road traffic volumes in the 100-m buffer were associated with 3%, 14%, 19%, and 28% increased risk of death during TB treatment [first quintile, referent; second quintile hazard ratio (HR)=1.03 [95% confidence interval (CI): 0.86, 1.25]; third quintile HR=1.14 (95% CI: 0.95, 1.37); fourth quintile HR=1.19 (95% CI: 0.99, 1.43); fifth quintile HR=1.28 (95% CI: 1.07, 1.53), respectively; p-trend=0.002].ConclusionsResidential proximity to road traffic volumes and traffic density were associated with increased all-cause mortality in patients undergoing treatment for active tuberculosis even after adjusting for multiple demographic, socioeconomic, and clinical factors, suggesting that TB patients are susceptible to the adverse health effects of traffic-related air pollution. https://doi.org/10.1289/EHP1699

Shedding light on the performance of a pyrosequencing assay for drug-resistant tuberculosis diagnosis

BACKGROUND: Rapid molecular diagnostics, with their ability to quickly identify genetic mutations associated with drug resistance in Mycobacterium tuberculosis clinical specimens, have great potential as tools to control multi- and extensively drug-resistant tuberculosis (M/XDR-TB). The Qiagen PyroMark Q96 ID system is a commercially available pyrosequencing (PSQ) platform that has been validated for rapid M/XDR-TB diagnosis. However, the details of the assay’s diagnostic and technical performance have yet to be thoroughly investigated in diverse clinical environments. METHODS: This study evaluates the diagnostic performance of the PSQ assay for 1128 clinical specimens from patients from three areas of high TB burden. We report on the diagnostic performance of the PSQ assay between the three sites and identify variables associated with poor PSQ technical performance. RESULTS: In India, the sensitivity of the PSQ assay ranged from 89 to 98 % for the detection of phenotypic resistance to isoniazid, rifampicin, fluoroquinolones, and the injectables. In Moldova, assay sensitivity ranged from 7 to 94 %, and in South Africa, assay sensitivity ranged from 71 to 92 %. Specificity was high (94–100 %) across all sites. The addition of eis promoter sequencing information greatly improved the sensitivity of kanamycin resistance detection in Moldova (7 % to 79 %). Nearly all (89.4 %) sequencing reactions conducted on smear-positive, culture-positive specimens and most (70.8 %) reactions conducted on smear-negative, culture-positive specimens yielded valid PSQ reads. An investigation into the variables influencing sequencing failures indicated smear negativity, culture negativity, site (Moldova), and sequencing of the rpoB, gyrA, and rrs genes were highly associated with poor PSQ technical performance (adj. OR > 2.0). CONCLUSIONS: This study has important implications for the global implementation of PSQ as a molecular TB diagnostic, as it demonstrates how regional factors may impact PSQ diagnostic performance, while underscoring potential gene targets for optimization to improve overall PSQ assay technical performance. TRIAL REGISTRATION: ClinicalTrials.gov (#NCT02170441). Registered 12 June 2014. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12879-016-1781-y) contains supplementary material, which is available to authorized users

A search for non-pulsating, chemically normal stars in the Scuti instability strip using Kepler data

We identify stars in the δ Sct instability strip that do not pulsate in p modes at the 50-μmag limit, using Kepler data. Spectral classification and abundance analyses from high-resolution spectroscopy allow us to identify chemically peculiar stars, in which the absence of pulsations is not surprising. The remaining stars are chemically normal, yet they are not δ Sct stars. Their lack of observed p modes cannot be explained through any known mechanism. However, they are mostly distributed around the edges of the δ Sct instability strip, which allows for the possibility that they actually lie outside the strip once the uncertainties are taken into account.We investigated the possibility that the non-pulsators inside the instability strip could be unresolved binary systems, having components that both lie outside the instability strip. If misinterpreted as single stars, we found that such binaries could generate temperature discrepancies of ∼300 K – larger than the spectroscopic uncertainties, and fully consistent with the observations. After these considerations, there remains one chemically normal nonpulsator that lies in the middle of the instability strip. This star is a challenge to pulsation theory. However, its existence as the only known star of its kind indicates that such stars are rare. We conclude that the δ Sct instability strip is pure, unless pulsation is shut down by diffusion or another mechanism, which could be interaction with a binary companion

Genetic Mutations Associated with Isoniazid Resistance in <i>Mycobacterium tuberculosis</i>: A Systematic Review

<div><p>Background</p><p>Tuberculosis (TB) incidence and mortality are declining worldwide; however, poor detection of drug-resistant disease threatens to reverse current progress toward global TB control. Multiple, rapid molecular diagnostic tests have recently been developed to detect genetic mutations in <i>Mycobacterium tuberculosis (Mtb)</i> genes known to confer first-line drug resistance. Their utility, though, depends on the frequency and distribution of the resistance associated mutations in the pathogen population. Mutations associated with rifampicin resistance, one of the two first-line drugs, are well understood and appear to occur in a single gene region in >95% of phenotypically resistant isolates. Mutations associated with isoniazid, the other first-line drug, are more complex and occur in multiple <i>Mtb</i> genes.</p><p>Objectives/Methodology</p><p>A systematic review of all published studies from January 2000 through August 2013 was conducted to quantify the frequency of the most common mutations associated with isoniazid resistance, to describe the frequency at which these mutations co-occur, and to identify the regional differences in the distribution of these mutations. Mutation data from 118 publications were extracted and analyzed for 11,411 <i>Mtb</i> isolates from 49 countries.</p><p>Principal Findings/Conclusions</p><p>Globally, 64% of all observed phenotypic isoniazid resistance was associated with the <i>kat</i>G315 mutation. The second most frequently observed mutation, <i>inhA</i>-15, was reported among 19% of phenotypically resistant isolates. These two mutations, <i>katG</i>315 and <i>inhA</i>-15, combined with ten of the most commonly occurring mutations in the <i>inhA</i> promoter and the <i>ahpC-oxyR</i> intergenic region explain 84% of global phenotypic isoniazid resistance. Regional variation in the frequency of individual mutations may limit the sensitivity of molecular diagnostic tests. Well-designed systematic surveys and whole genome sequencing are needed to identify mutation frequencies in geographic regions where rapid molecular tests are currently being deployed, providing a context for interpretation of test results and the opportunity for improving the next generation of diagnostics.</p></div

Evaluation of genetic mutations associated with Mycobacterium tuberculosis resistance to amikacin, kanamycin and capreomycin: a systematic review.

BackgroundRapid molecular diagnostics for detecting multidrug-resistant and extensively drug-resistant tuberculosis (M/XDR-TB) primarily identify mutations in Mycobacterium tuberculosis (Mtb) genes associated with drug resistance. Their accuracy, however, is dependent largely on the strength of the association between a specific mutation and the phenotypic resistance of the isolate with that mutation, which is not always 100%. While this relationship is well established and reliable for first-line anti-TB drugs, rifampin and isoniazid, it is less well-studied and understood for second-line, injectable drugs, amikacin (AMK), kanamycin (KAN) and capreomycin (CAP).Methodology/principal findingsWe conducted a systematic review of all published studies evaluating Mtb mutations associated with resistance to AMK, KAN, CAP in order to characterize the diversity and frequency of mutations as well as describe the strength of the association between specific mutations and phenotypic resistance in global populations. Our objective was to determine the potential utility and reliability of these mutations as diagnostic markers for detecting AMK, KAN and CAP resistance. Mutation data was reviewed for 1,585 unique clinical isolates from four continents and over 18 countries. Mutations in the rrs, tlyA, eis promoter and gidB genes were associated with AMK, KAN and/or CAP resistance.Conclusions/significanceThe rrs A1401G mutation was present in the majority of AMK, KAN and CAP resistant Mtb strains reviewed, but was also found in 7% of CAP susceptible strains. The 1401 mutation alone, however, was not found with sufficient frequency to detect more than 70-80% of global Mtb strains resistant to AMK and CAP, and 60% of strains resistant to KAN. Additional mutations in the rrs, eis promoter, tlyA and gidB genes appear to be associated with resistance and could improve sensitivity and specificity of future diagnostics