scholarly journals Smart Pooling: AI-Powered COVID-19 Informative Group Testing

2021 ◽  
Author(s):  
Maria Escobar ◽  
Guillaume Jeanneret ◽  
Laura Bravo-Sánchez ◽  
Angela Castillo ◽  
Catalina Gómez ◽  
...  
Keyword(s):  
Machine Learning ◽  
False Negative ◽  
Group Testing ◽  
Error Rates ◽  
Molecular Testing ◽  
Proof Of Concept ◽  
Efficiency Gains ◽  
Automated Method ◽  
Sociodemographic Data ◽  
False Negative Error

Abstract Massive molecular testing for COVID-19 has been pointed out as fundamental to moderate the spread of the pandemic. Pooling methods can enhance testing efficiency, but they are viable only at low incidences of the disease. We propose Smart Pooling, a machine learning method that uses clinical and sociodemographic data from patients to increase the efficiency of informed Dorfman testing for COVID-19 by arranging samples into all-negative pools. To do this, we ran an automated method to train numerous machine learning models on a retrospective dataset from more than 8,000 patients tested for SARS-CoV-2 from April to July 2020 in Bogotá, Colombia. We estimated the efficiency gains of using the predictor to support Dorfman testing by simulating the outcome of tests. We also computed the attainable efficiency gains of non-adaptive pooling schemes mathematically. Moreover, we measured the false-negative error rates in detecting the ORF1ab and N genes of the virus in RT-qPCR dilutions. Finally, we presented the efficiency gains of using our proposed pooling scheme on proof-of-concept pooled tests. We believe Smart Pooling will be efficient for optimizing massive testing of SARS-CoV-2.

Error Rates in Competency Testing When Test Retaking Is Permitted

1990 ◽  
Vol 15 (1) ◽  
pp. 39-52 ◽  
Author(s):  
Huynh Huynh
Keyword(s):  
Conditional Probability ◽  
False Positive ◽  
False Negative ◽  
Error Rates ◽  
Rasch Models ◽  
Positive Error ◽  
False Positive Error ◽  
Competency Testing ◽  
Negative Error ◽  
False Negative Error

False positive and false negative error rates are studied for competency testing where examinees are permitted to retake the test if they fail to pass. Formulae are provided for the beta-binomial and Rasch models, and estimates based on these two models are compared for several typical situations. Although Rasch estimates are expected to be more accurate than beta-binomial estimates, differences among them are found not to be substantial in a number of practical situations. Under relatively general conditions and when test retaking is permitted, the probability of making a false negative error is zero. Under the same situation, and given that an examinee is a true nonmaster, the conditional probability of making a false positive error for this examinee is one.

Errors in prostate core biopsy diagnosis in an era of specialisation and double reporting

2020 ◽  
pp. jclinpath-2020-206726
Author(s):  
Cornelia Margaret Szecsei ◽  
Jon D Oxley
Keyword(s):  
Error Rate ◽  
False Positive ◽  
Core Biopsy ◽  
False Negative ◽  
Diagnostic Error ◽  
Error Rates ◽  
False Positive Error ◽  
Biopsy Diagnosis ◽  
False Negative Error ◽  
False Positive Error Rate

AimTo examine the effects of specialist reporting on error rates in prostate core biopsy diagnosis.MethodBiopsies were reported by eight specialist uropathologists over 3 years. New cancer diagnoses were double-reported and all biopsies were reviewed for the multidisciplinary team (MDT) meeting. Diagnostic alterations were recorded in supplementary reports and error rates were compared with a decade previously.Results2600 biopsies were reported. 64.1% contained adenocarcinoma, a 19.7% increase. The false-positive error rate had reduced from 0.4% to 0.06%. The false-negative error rate had increased from 1.5% to 1.8%, but represented fewer absolute errors due to increased cancer incidence.ConclusionsSpecialisation and double-reporting have reduced false-positive errors. MDT review of negative cores continues to identify a very low number of false-negative errors. Our data represents a ‘gold standard’ for prostate biopsy diagnostic error rates. Increased use of MRI-targeted biopsies may alter error rates and their future clinical significance.

scholarly journals High resolution analysis of cervical cells--a progress report.

1977 ◽  
Vol 25 (7) ◽  
pp. 689-695 ◽  
Author(s):  
R S Poulsen ◽  
L H Oliver ◽  
R L Cahn ◽  
C Louis ◽  
G Toussaint
Keyword(s):  
High Resolution ◽  
False Positive ◽  
False Negative ◽  
Error Rates ◽  
Cell Segmentation ◽  
Negative Error ◽  
False Negative Error ◽  
High Resolution Analysis ◽  
Resolution Analysis ◽  
Cervical Cells

This paper presents preliminary results of research toward the development of a high resolution analysis stage for a dual resolution image processing-based prescreening device for cervical cytology. Experiments using both manual and automatic methods for cell segmentation are described. In both cases, 1500 cervical cells were analyzed and classified as normal or abnormal (dysplastic or malignant) using a minimum Mahalanobis distance classifier with eight subclasses of normal cells, and five subclasses of abnormal cells. With manual segmentation, false positive and false negative error rates of 2.98 and 7.73% were obtained. Similar experiments using automatic cell segmentation methods yielded false positive and false negative error rates of 3.90 and 11.56%, respectively. In both cases, independent training and testing data were used.

scholarly journals Estimating false positive and false negative error rates in cervical cell classification.

1977 ◽  
Vol 25 (7) ◽  
pp. 696-701 ◽  
Author(s):  
L H Oliver ◽  
R S Poulsen ◽  
G T Toussaint
Keyword(s):  
False Negative ◽  
Recognition System ◽  
Classification Performance ◽  
Error Rates ◽  
Cell Classification ◽  
Cervical Cell ◽  
Performance Curves ◽  
False Negative Error ◽  
A Cell ◽  
Cervical Cells

The performance of a cell recognition system on unknown data is often estimated in terms of its error rates on a test set. This paper investigates methods for producing estimates of error rates in cervical cell classification. Classification performance curves calculated using these methods are given for several classification schemes used to classify 1500 cervical cells.

scholarly journals Grandparent inference from genetic data: The potential for parentage-based tagging programs to identify offspring of hatchery strays

2021 ◽  
Author(s):  
Thomas A Delomas ◽  
Matthew Campbell
Keyword(s):  
False Negative ◽  
Genetic Data ◽  
R Package ◽  
Error Rates ◽  
Nucleotide Polymorphisms ◽  
Negative Effects ◽  
Single Nucleotide ◽  
Natural Origin ◽  
False Negative Error ◽  
In The Wild

Fisheries managers routinely use hatcheries to increase angling opportunity. Many hatcheries operate as segregated programs where hatchery-origin fish are not intended to spawn with natural-origin conspecifics in order to prevent potential negative effects on the natural-origin population. Currently available techniques to monitor the frequency with which hatchery-origin strays successfully spawn in the wild rely on either genetic differentiation between the hatchery- and natural-origin fish or extensive sampling of fish on the spawning grounds. We present a method to infer grandparent-grandchild trios using only genotypes from two putative grandparents and one putative grandchild. We developed estimators of false positive and false negative error rates and showed that genetic panels containing 500 - 700 single nucleotide polymorphisms or 200 - 300 microhaplotypes are expected to allow application of this technique for monitoring segregated hatchery programs. We discuss the ease with which this technique can be implemented by pre-existing parentage-based tagging programs and provide an R package that applies the method.

scholarly journals Smart Pooling: AI-powered COVID-19 testing

2020 ◽  
Author(s):  
María Escobar ◽  
Guillaume Jeanneret ◽  
Laura Bravo-Sánchez ◽  
Angela Castillo ◽  
Catalina Gómez ◽  
...  
Keyword(s):  
Machine Learning ◽  
World War Ii ◽  
Optimal Strategies ◽  
Respiratory Illness ◽  
Molecular Testing ◽  
Efficiency Gains ◽  
Two Stage ◽  
Learning Methods ◽  
Pooled Testing ◽  
Machine Learning Methods

Background: COVID-19 is an acute respiratory illness caused by the novel coronavirusSARS-CoV-2. The disease has rapidly spread to most countries and territories and hascaused 14.2 million confirmed infections and 602,037 deaths as of July 19th 2020. Massive molecular testing for COVID-19 has been pointed as fundamental to moderate the spread of the disease. Pooling methods can enhance the efficiency of testing, but they are viable only at very low incidences of the disease. We propose Smart Pooling, a machine learning method that uses clinical and sociodemographic data from patients to increase the efficiency of pooled molecular testing for COVID-19 by arranging samples into all-negative pools. Methods: We developed machine learning methods that estimate the probability that a sample will test positive for SARS-Cov-2 based on complementary information from the sample. We use these predictions to exclude samples predicted as positive from pools. We trained our machine learning methods on a dataset of 2000 patients tested for SARS-Cov-2 from April to July in Bogota, Colombia. Findings: Our method, Smart Pooling, shows efficiency of 306% at a disease prevalence of 5% and efficiency of 107% at disease a prevalence of up to 50%, a regime in which two-stage pooling offers marginal efficiency gains compared to individual testing. Additionally, we calculate the possible efficiency gains of one- and two-dimensional two-stage pooling strategies, and present the optimal strategies for disease prevalences up to 25%. We discuss practical limitations to conduct pooling in the laboratory. Interpretation: Pooled testing has been a theoretically alluring option to increase the coverage of diagnostics since its proposition by Dorfmann during World War II. Although there are examples of successfully using pooled testing to reduce the cost of diagnostics, its applicability has remained limited because efficiency drops rapidly as prevalence increases. Not only does our method provide a cost-effective solution to increase the coverage of testing amid the COVID-19 pandemic, but it also demonstrates that artificial intelligence can be used complementary with well-established techniques in the medical praxis.

scholarly journals Do forests ‘fall silent’ following aerial applications of 1080 poison? Development and application of bird monitoring methods using automated sound recording devices

2021 ◽  
Author(s):  
◽  
Asher Cook
Keyword(s):  
New Zealand ◽  
False Negative ◽  
False Negative Rate ◽  
Bird Species ◽  
Quantitative Information ◽  
Error Rates ◽  
Type I ◽  
Monitoring Methods ◽  
Manual Method ◽  
Automated Method

<p>Electronic bioacoustic techniques are providing new and effective ways of monitoring birds and have a number of advantages over other traditional monitoring methods. Given the increasing popularity of bioacoustic methods, and the difficulties associated with automated analyses (e.g. high Type I error rates), it is important that the most effective ways of scoring audio recordings are investigated. In Chapter Two I describe a novel sub-sampling and scoring technique (the ‘10 in 60 sec’ method) which estimates the vocal conspicuousness of bird species through the use of repeated presence-absence counts and compare its performance with a current manual method. The ‘10 in 60 sec’ approach reduced variability in estimates of vocal conspicuousness, significantly increased the number of species detected per count and reduced temporal autocorrelation. I propose that the ‘10 in 60 sec’ method will have greater overall ability to detect changes in underlying birdsong parameters and hence provide more informative data to scientists and conservation managers.  It is often anecdotally suggested that forests ‘fall silent’ and are devoid of birdsong following aerial 1080 operations. However, it is difficult to objectively assess the validity of this claim without quantitative information that addresses the claim specifically. Therefore in Chapter Three I applied the methodological framework outlined in Chapter Two to answer a controversial conservation question: Do New Zealand forests ‘fall silent’ after aerial 1080 operations? At the community level I found no evidence for a reduction in birdsong after the 1080 operation and eight out of the nine bird taxa showed no evidence for a decline in vocal conspicuousness. Only one species, tomtit (Petroica macrocephala), showed evidence for a decline in vocal conspicuousness, though this effect was non-significant after applying a correction for multiple tests.  In Chapter Four I used tomtits as a case study species to compare manual and automated approaches to: (1) estimating vocal conspicuousness and (2) determine the feasibility of using an automated detector on a New Zealand passerine. I found that data from the automated method were significantly positively correlated with the manual method although the relationship was not particularly strong (Pearson’s r = 0.62, P < 0.0001). The automated method suffered from a relatively high false negative rate and the data it produced did not reveal a decline in tomtit call rates following the 1080 drop. Given the relatively poor performance of the automated method, I propose that the automatic detector developed in this thesis requires further refinement before it is suitable for answering management-level questions for tomtit populations. However, as pattern recognition technology continues to improve automated methods are likely to become more viable in the future.</p>

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