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DOCTOR: A Simple Method for Detecting Misclassification Errors [article]

Federica Granese, Marco Romanelli, Daniele Gorla, Catuscia Palamidessi, Pablo Piantanida
2021 arXiv   pre-print
In this work, we propose DOCTOR, a simple method that aims to identify whether the prediction of a DNN classifier should (or should not) be trusted so that, consequently, it would be possible to accept  ...  DOCTOR can be applied to any pre-trained model, it does not require prior information about the underlying dataset and is as simple as the simplest available methods in the literature.  ...  Summary and Concluding Remarks We introduced a simple and effective method to detect misclassification errors, i.e., whether a prediction of a classifier should or should not be trusted.  ... 
arXiv:2106.02395v2 fatcat:zxgpguawkfh6nosgghy6hd7n4m

How Many Patients? How Many Doctors?

J. I. E. Hoffman
2010 Circulation  
They collected information about survivors and nonsurvivors, and given the expertise of the involved cardiologists, few errors of misclassification can be expected.  ...  Because the incidence of specific types of CHD is less important for medical planning, they classified ACHD into three groups by severity 2,3 : Simple, which can be cared for by a well trained physician  ... 
doi:10.1161/circulationaha.110.989350 pmid:21147727 fatcat:4qzcgjohlngijewag5wzlumgza

Patient passports would alert doctors to previous bone marrow transplantation

A. Smith, H. Somerville
2001 BMJ (Clinical Research Edition)  
In addition, a parallel quota of socioeconomic status was imposed. A standardised schedule was used when respondents were interviewed.  ...  In contrast, by far the highest proportion of women who were high risk drinkers (consuming >35 units a week) was among those aged only 18-24. (A unit is defined as 1 cl, or 7.9 g absolute alcohol.)  ...  when our goal was to provide a simple, easily usable tool.  ... 
doi:10.1136/bmj.323.7322.1185 pmid:11711417 pmcid:PMC1121655 fatcat:3yiepntxx5b33dy2bfmna4fgtu

A supervised machine learning approach to trace doctorate recipients' employment trajectories

Dominik P. Heinisch, Johannes Koenig, Anne Otto
2019 Quantitative Science Studies  
With the current information base, graduate students cannot make an informed decision whether to start a doctorate or not (Benderly, 2018; Blank, 2017).  ...  The machine learning algorithms are trained on a synthetic training and evaluation dataset.  ...  of this research work, as well as Judith Heinisch for her helpful comments.  ... 
doi:10.1162/qss_a_00001 fatcat:ocsd3566prhz7mln74nk3pv4cq

Estimating the Prevalence of Opioid Diversion by "Doctor Shoppers" in the United States

Douglas C. McDonald, Kenneth E. Carlson, Laxmaiah Manchikanti
2013 PLoS ONE  
Methods and Findings: The sample included records for 146.1 million opioid prescriptions dispensed during 2008 by 76% of US retail pharmacies.  ...  Conclusions: Our data did not provide information to make a clinical diagnosis of individuals. Very few of these patients can be classified with certainty as diverting drugs for nonmedical purposes.  ...  Disclaimer: Authors had full access to all of the data in the study and are responsible for the integrity of the data and the accuracy of the analyses.  ... 
doi:10.1371/journal.pone.0069241 pmid:23874923 pmcid:PMC3714248 fatcat:ib5zjcr7onarpmd5kyqwtw7e2q

Schools' experience of league tables should make doctors think again

P. Tymms, A. Wiggins
2000 BMJ (Clinical Research Edition)  
Christopher J Cold doctor, department of pathology Marshfield Clinic, Marshfield, Wisconsin, USA Michelle R Storms family practitioner Hazelhurst, Wisconsin, USA John D Dalton researcher and archiver,  ...  No improvement has been seen for lung or laryngeal cancers because they are either difficult to detect or aggressive and hence survival is less than five years at detection.  ...  "Avoided deaths" may not be useful for predicting mortality reductions from cancer Editor-Richards et al in their paper suggest a method for estimating the number of deaths from cancer avoided as a result  ... 
doi:10.1136/bmj.321.7274.1467 pmid:11187941 pmcid:PMC1119176 fatcat:6oxftiaczjbbvbic4tey7yenzi

Episiotomy preferences, indication, and classification - a survey among Nordic doctors

Kathrine Fodstad, Anne C. Staff, Katariina Laine
2016 Acta Obstetricia et Gynecologica Scandinavica  
Danish and Swedish doctors in perception of clinical indications for episiotomy.  ...  The findings from Study 3 also revealed a large non-classifiable episiotomy group in addition to large misclassification rates within and across the Nordic countries.  ... 
doi:10.1111/aogs.12856 pmid:26814151 fatcat:vwohqy4d6jdutlaaychwuioqmi

Failure Detection in Medical Image Classification: A Reality Check and Benchmarking Testbed [article]

Melanie Bernhardt, Fabio De Sousa Ribeiro, Ben Glocker
2022 arXiv   pre-print
Failure detection in automated image classification is a critical safeguard for clinical deployment.  ...  This paper provides a reality check, establishing the performance of in-domain misclassification detection methods, benchmarking 9 confidence scores on 6 medical imaging datasets with different imaging  ...  We can see that Expected Calibration Error (ECE) is not necessarily a good metric for measuring the misclassification detection performance of a given confidence score. labels for any given point.  ... 
arXiv:2205.14094v1 fatcat:ihyreztg65f4tgcyld4dhmsu4q

Exploring Geographic Variation of Mental Health Risk and Service Utilization of Doctors and Hospitals in Toronto: A Shared Component Spatial Modeling Approach

Jane Law, Christopher Perlman
2018 International Journal of Environmental Research and Public Health  
We adopted a shared component spatial modeling approach that allows simultaneous analysis of two main health service utilizations: doctor visits and hospitalizations related to mental health conditions  ...  Based on the evidence found, we discuss intervention strategies, focusing on the hotspots and provision of health services about doctors and hospitals, to improve mental health for the neighbourhoods.  ...  Data of MH risk in each neighbourhood from either source may involve issues of underreporting caused by detection bias and neighbourhood misclassification caused by geocoding errors, for instance.  ... 
doi:10.3390/ijerph15040593 pmid:29587426 pmcid:PMC5923635 fatcat:a3zi5rj5ibhohcfppl3mvdpany

Luminance Adaptive Biomarker Detection in Digital Pathology Images

Jingxin Liu, Guoping Qiu, Linlin Shen
2016 Procedia Computer Science  
Based on this, we propose two novel luminance adaptive biomarker detection methods.  ...  To realise the full potential of digital pathology, accurate and robust computer techniques for automatically detecting biomarkers play an important role.  ...  Technology Bureau (Project No 2012B10055 and 2013D10 008), the Natural Science Foundation of China (61272050), Natural Science Foundation of Guangdong Province (2014A030313556) and the International Doctoral  ... 
doi:10.1016/j.procs.2016.07.032 fatcat:sps4r5e7jvdcfisr4xcymu77sy

Estimation With Cox Models

Bart Van Rompaye, Shabbar Jaffar, Els Goetghebeur
2012 Epidemiology  
Our method avoids bias in effect estimates at low cost in variance, thus offering a perspective for better-informed decision making.  ...  The ratio of different cause-specific hazards can be estimated flexibly for this purpose. It thus complements an all-cause analysis.  ...  The study was sized to yield a power of 80% for detecting this effect on the cause-1-specific hazard at the 5% significance level (a sample size calculation is provided in eAppendix).  ... 
doi:10.1097/ede.0b013e3182454cad pmid:22317803 pmcid:PMC3903130 fatcat:hvjswz7elbadniebkhadzal4ou

Comparison of computer systems and ranking criteria for automatic melanoma detection in dermoscopic images

Kajsa Møllersen, Maciel Zortea, Thomas R. Schopf, Herbert Kirchesch, Fred Godtliebsen, Nikolas K. Haass
2017 PLoS ONE  
From a clinical perspective, the misclassification of a melanoma as benign has far greater cost than the misclassification of a benign lesion.  ...  The variability in diagnostic accuracy for different classifier algorithms was larger than the variability for segmentation methods, and suggests a focus for future investigations.  ...  Acknowledgments We wish to thank the reviewers for valuable feedback which has improved the manuscript.  ... 
doi:10.1371/journal.pone.0190112 pmid:29267358 pmcid:PMC5739481 fatcat:ibqj3riozfa37kztoznp7xb7ua

Cognitive Radios: Discriminant Analysis for Automatic Signal Detection in Measured Power Spectra

Lee Gonzales-Fuentes, Kurt Barbe, Wendy Van Moer, Niclas Bjorsell
2013 IEEE Transactions on Instrumentation and Measurement  
In this paper, we optimize the methodology for signal detection for cognitive radios such that the probability that a spectral component was incorrectly classified is minimized.  ...  A novel method proposed the segmentation of the measured spectra into regions where the flatness condition is approximately valid.  ...  Initial detection method for signal and noise components By a simple visual inspection, one can already have a rough idea of which parts of the spectrum contain signal, and which contain noise.  ... 
doi:10.1109/tim.2013.2265607 fatcat:p4u6gj3i3bd3fnugxiefgn5jvy

Predicting and Diagnosing of Heart Disease Using Machine Learning Algorithms

Sanjay Kumar Sen
2017 International Journal Of Engineering And Computer Science  
In order to reduce the large scale of deaths from heart diseases, a quick and efficient detection technique is to be discovered.  ...  Prediction and diagnosing of heart disease become a challenging factor faced by doctors and hospitals both in India and abroad.  ...  The matter become a headache for all doctors both in India and abroad.  ... 
doi:10.18535/ijecs/v6i6.14 fatcat:wo2efa5x3ng3jk2foba34z35fa

Annotations on the Relationship Among Discriminant Functions

Awogbemi Clement Adeyeye
2020 Pure and Applied Mathematics Journal  
A linear relationship is also established between W and Z classification statistics.  ...  Violation of condition of equal variance covariance matrix for Linear Discriminant Function (LDF) results to Quadratic Discriminant Function (QDF).  ...  Function (FLDF); efforts to reduce as much as possible, the derivatives of errors of misclassification; efforts to get permissible methods that minimize probabilities of misclassification and also as  ... 
doi:10.11648/j.pamj.20200906.14 fatcat:6elnohruirg3vhlso42rxrhuvm
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