scholarly journals Automated detection of amperometric spikes resulting from quantal exocytosis and estimation of spike and pre-spike foot signal parameters

2018 ◽  
Author(s):  
◽  
Supriya Balaji Ramachandran
Keyword(s):  
Time Course ◽  
Matched Filter ◽  
False Positive Rate ◽  
Area Under The Curve ◽  
Signal Parameters ◽  
Curve Fit ◽  
Least Squares Fit ◽  
Positive Rate ◽  
Filter Approach ◽  
Two Phases

Electrochemical microelectrodes can detect single-vesicle release events as "spikes" of amperometric current. We developed a template based "matched-filter" approach that performs least squares fit of a library of templates to the data and identifies a spike when a detection criterion score given by the ratio of amplitude to the standard error exceeds a minimum threshold. This method outperformed existing approaches and detected >95% of true spikes for a mere 2% false positive rate as evidenced by receiver operating characteristic plots of sensitivity vs specificity. The next step is estimation of spike parameters like peak amplitude (Imax), half-maximal width (t50) and area under the curve (Q) which inform maximal flux, flux duration and charge respectively. Closely successive overlapping spikes are ambiguous to estimate as they may not decay back to baseline and should be rejected. Matched filter approach not only provided robust spike detection but also parameter seed values to reject overlapping spikes and also perform iterative curve fitting of spikes. The remaining well-separated spikes were iteratively fit in two phases, first by fitting rising and decaying phases separately and second by fitting the entire time course using seed values from the matched filter template parameters. Using curve-fit parameters, Imax, t50 and Q were calculated. Histograms of these parameters had bi-modal Gaussian distributions with centers and spreads within 12% and 4% of histograms created using manually analyzed data. The pre-spike baseline was estimated using a novel application of the matched-filter criterion scores and the estimation of pre-spike foot signal parameters such as charge (Qfoot) and duration (tfoot) yielded means, and medians within 10% of manually computed parameters.

Ascertaining an efficient eligibility cut-off for extended Medicare items for eating disorders

2021 ◽  
pp. 103985622110286
Author(s):  
Tracey Wade ◽  
Jamie-Lee Pennesi ◽  
Yuan Zhou
Keyword(s):  
Eating Disorders ◽  
Eating Disorder ◽  
False Positive Rate ◽  
Area Under The Curve ◽  
Rate Sensitivity ◽  
True Positive Rate ◽  
Eating Disorder Examination Questionnaire ◽  
Eating Disorder Examination ◽  
Positive Rate ◽  
The Relationship

Objective: Currently eligibility for expanded Medicare items for eating disorders (excluding anorexia nervosa) require a score ⩾ 3 on the 22-item Eating Disorder Examination-Questionnaire (EDE-Q). We compared these EDE-Q “cases” with continuous scores on a validated 7-item version of the EDE-Q (EDE-Q7) to identify an EDE-Q7 cut-off commensurate to 3 on the EDE-Q. Methods: We utilised EDE-Q scores of female university students ( N = 337) at risk of developing an eating disorder. We used a receiver operating characteristic (ROC) curve to assess the relationship between the true-positive rate (sensitivity) and the false-positive rate (1-specificity) of cases ⩾ 3. Results: The area under the curve showed outstanding discrimination of 0.94 (95% CI: .92–.97). We examined two specific cut-off points on the EDE-Q7, which included 100% and 87% of true cases, respectively. Conclusion: Given the EDE-Q cut-off for Medicare is used in conjunction with other criteria, we suggest using the more permissive EDE-Q7 cut-off (⩾2.5) to replace use of the EDE-Q cut-off (⩾3) in eligibility assessments.

Considerations on the region of interest in the ROC space

2021 ◽  
pp. 096228022110605
Author(s):  
Luigi Lavazza ◽  
Sandro Morasca
Keyword(s):  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
False Positive Rate ◽  
Area Under The Curve ◽  
Region Of Interest ◽  
True Positive Rate ◽  
True Positive ◽  
Receiver Operating Characteristic Curves ◽  
Positive Rate ◽  
Receiver Operating

Receiver Operating Characteristic curves have been widely used to represent the performance of diagnostic tests. The corresponding area under the curve, widely used to evaluate their performance quantitatively, has been criticized in several respects. Several proposals have been introduced to improve area under the curve by taking into account only specific regions of the Receiver Operating Characteristic space, that is, the plane to which Receiver Operating Characteristic curves belong. For instance, a region of interest can be delimited by setting specific thresholds for the true positive rate or the false positive rate. Different ways of setting the borders of the region of interest may result in completely different, even opposing, evaluations. In this paper, we present a method to define a region of interest in a rigorous and objective way, and compute a partial area under the curve that can be used to evaluate the performance of diagnostic tests. The method was originally conceived in the Software Engineering domain to evaluate the performance of methods that estimate the defectiveness of software modules. We compare this method with previous proposals. Our method allows the definition of regions of interest by setting acceptability thresholds on any kind of performance metric, and not just false positive rate and true positive rate: for instance, the region of interest can be determined by imposing that [Formula: see text] (also known as the Matthews Correlation Coefficient) is above a given threshold. We also show how to delimit the region of interest corresponding to acceptable costs, whenever the individual cost of false positives and false negatives is known. Finally, we demonstrate the effectiveness of the method by applying it to the Wisconsin Breast Cancer Data. We provide Python and R packages supporting the presented method.

scholarly journals The Role of Native T1 Mapping in the Diagnosis of Myocarditis in a Real-World Setting

2020 ◽  
Vol 9 (12) ◽  
pp. 3810
Author(s):  
Johannes Kersten ◽  
Tobias Heck ◽  
Laura Tuchek ◽  
Wolfgang Rottbauer ◽  
Dominik Buckert
Keyword(s):  
T1 Mapping ◽  
Acute Myocarditis ◽  
False Positive Rate ◽  
Area Under The Curve ◽  
Cutoff Value ◽  
Single Center Study ◽  
Positive Rate ◽  
The Impact ◽  
Consensus Expert

Background: This prospective single-center study sought to investigate the impact of cardiovascular magnetic resonance (CMR) on the diagnosis of myocarditis, with special attention given to absolute T1 values and defined cutoff values. Methods: All patients referred to our center with the suspicion of an inflammatory myocardial disease were diagnosed by a consensus expert consortium blinded to CMR findings. Classical Lake Louise criteria were then used to confirm or change the diagnosis. Results: Of a total of 149 patients, 15 were diagnosed with acute myocarditis without taking CMR findings into account. Acute myocarditis was excluded in 91 patients, whereas 42 cases were unclear. Using classical Lake Louise criteria, an additional 35 clear diagnoses were made, either confirming or excluding myocarditis. In the remaining patients, there was no further increase in definitive diagnoses using T1 measurements. The diagnostic performance of T1 mapping in distinguishing acute myocarditis patients from healthy controls was good (area under the curve (AUC) 0.835, cutoff value 1019 ms, sensitivity 73.7%, specificity 72.4%). In the group of patients with suspected and then excluded myocarditis, the cutoff value had a false-positive rate of 56.6%. Conclusions: Acute myocarditis should be diagnosed on the basis of clinical and imaging factors, whereas T1 mapping could be helpful, especially for excluding acute myocarditis.

scholarly journals Classification of Breast Cancer in Mammograms with Deep Learning Adding a Fifth Class

2021 ◽  
Vol 11 (23) ◽  
pp. 11398
Author(s):  
Salvador Castro-Tapia ◽  
Celina Lizeth Castañeda-Miranda ◽  
Carlos Alberto Olvera-Olvera ◽  
Héctor A. Guerrero-Osuna ◽  
José Manuel Ortiz-Rodriguez ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Imaging Techniques ◽  
False Positive Rate ◽  
Area Under The Curve ◽  
Fine Tuning ◽  
Cad System ◽  
Leading Role ◽  
Operational Characteristics ◽  
Positive Rate

Breast cancer is one of the diseases of most profound concern, with the most prevalence worldwide, where early detections and diagnoses play the leading role against this disease achieved through imaging techniques such as mammography. Radiologists tend to have a high false positive rate for mammography diagnoses and an accuracy of around 82%. Currently, deep learning (DL) techniques have shown promising results in the early detection of breast cancer by generating computer-aided diagnosis (CAD) systems implementing convolutional neural networks (CNNs). This work focuses on applying, evaluating, and comparing the architectures: AlexNet, GoogLeNet, Resnet50, and Vgg19 to classify breast lesions after using transfer learning with fine-tuning and training the CNN with regions extracted from the MIAS and INbreast databases. We analyzed 14 classifiers, involving 4 classes as several researches have done it before, corresponding to benign and malignant microcalcifications and masses, and as our main contribution, we also added a 5th class for the normal tissue of the mammary parenchyma increasing the correct detection; in order to evaluate the architectures with a statistical analysis based on the received operational characteristics (ROC), the area under the curve (AUC), F1 Score, accuracy, precision, sensitivity, and specificity. We generate the best results with the CNN GoogLeNet trained with five classes on a balanced database with an AUC of 99.29%, F1 Score of 91.92%, the accuracy of 91.92%, precision of 92.15%, sensitivity of 91.70%, and specificity of 97.66%, concluding that GoogLeNet is optimal as a classifier in a CAD system to deal with breast cancer.

scholarly journals Analysis and Mapping of Rainfall-Induced Landslide Susceptibility in A Luoi District, Thua Thien Hue Province, Vietnam

Water ◽  
2018 ◽  
Vol 11 (1) ◽  
pp. 51 ◽  
Author(s):  
Nguyen Long ◽  
Florimond De Smedt
Keyword(s):  
Landslide Susceptibility ◽  
Factor Model ◽  
Characteristic Curve ◽  
False Positive Rate ◽  
Area Under The Curve ◽  
True Positive Rate ◽  
Landslide Susceptibility Mapping ◽  
Certainty Factor ◽  
Validation Test ◽  
Positive Rate

Rainfall-induced landslides form an important natural threat in Vietnam. The purpose of this study is to explore regional landslide susceptibility mapping in the mountainous district of A Luoi in Thua Thien Hue Province, where data on the occurrence and causes of landslides are very limited. Three methods are applied to examine landslide susceptibility: statistical index, logistic regression and certainty factor. Nine causative factors are considered: elevation, slope, geological strata, fault density, geomorphic landforms, weathering crust, land use, distance to rivers and annual precipitation. The reliability of the landslide susceptibility maps is evaluated by a receiver operating characteristic curve and the area under the curve is used to quantify and compare the prediction accuracy of the models. The certainty factor model performs best. This model is optimized by maximizing the difference between the true positive rate and the false positive rate. The optimal model correctly identifies 84% of the observed landslides. The results are verified with a validation test, whereby the model is calibrated with 75% randomly selected observed landslides, while the remaining 25% of the observed landslides are used for validation. The validation test correctly identifies 81% of the observed landslides in the training set and 73% of the observed landslides in the validation set.

Lactate dehydrogenase isoenzyme-1: changes during the first day after acute myocardial infarction.

1981 ◽  
Vol 27 (11) ◽  
pp. 1821-1823 ◽  
Author(s):  
D E Bruns ◽  
J C Emerson ◽  
S Intemann ◽  
R Bertholf ◽  
K E Hill ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Lactate Dehydrogenase ◽  
Time Course ◽  
False Positive Rate ◽  
Diagnostic Value ◽  
Immunochemical Method ◽  
Dehydrogenase Isoenzyme ◽  
Positive Rate ◽  
Hospital Days

Abstract We studied the time course of change of lactate dehydrogenase isoenzyme-1 (LD-1) in serum of patients suspected of having had an acute myocardial infarction. LD-1 was measured at intervals of 4-8 h during the first and second hospital days, by an immunochemical method. Of the 65 patients in this study, 26 had acute myocardial infarctions by traditional criteria. The ratio of LD-1 to total LD had greater diagnostic value than did LD-1 alone. In 90% of patients with myocardial infarction this ratio was increased within 12 h of admission, and all had increased ratios within 24 h. The false-positive rate was less than 1%, and an increased LD-1/total LD ratio had a predictive value of 96% for myocardial infarction. These results suggest that LD-1 is useful in the diagnosis of myocardial infarction on the first day of hospitalization.

scholarly journals Prevention of Crypto-Ransomware Using a Pre-Encryption Detection Algorithm

Computers ◽  
2019 ◽  
Vol 8 (4) ◽  
pp. 79 ◽  
Author(s):  
S. Kok ◽  
Azween Abdullah ◽  
NZ Jhanjhi ◽  
Mahadevan Supramaniam
Keyword(s):  
Machine Learning ◽  
Phase I ◽  
Phase Ii ◽  
Learning Algorithm ◽  
False Positive Rate ◽  
Application Programming Interface ◽  
Detection Algorithm ◽  
Positive Rate ◽  
New Type ◽  
Two Phases

Ransomware is a relatively new type of intrusion attack, and is made with the objective of extorting a ransom from its victim. There are several types of ransomware attacks, but the present paper focuses only upon the crypto-ransomware, because it makes data unrecoverable once the victim’s files have been encrypted. Therefore, in this research, it was proposed that machine learning is used to detect crypto-ransomware before it starts its encryption function, or at the pre-encryption stage. Successful detection at this stage is crucial to enable the attack to be stopped from achieving its objective. Once the victim was aware of the presence of crypto-ransomware, valuable data and files can be backed up to another location, and then an attempt can be made to clean the ransomware with minimum risk. Therefore we proposed a pre-encryption detection algorithm (PEDA) that consisted of two phases. In, PEDA-Phase-I, a Windows application programming interface (API) generated by a suspicious program would be captured and analyzed using the learning algorithm (LA). The LA can determine whether the suspicious program was a crypto-ransomware or not, through API pattern recognition. This approach was used to ensure the most comprehensive detection of both known and unknown crypto-ransomware, but it may have a high false positive rate (FPR). If the prediction was a crypto-ransomware, PEDA would generate a signature of the suspicious program, and store it in the signature repository, which was in Phase-II. In PEDA-Phase-II, the signature repository allows the detection of crypto-ransomware at a much earlier stage, which was at the pre-execution stage through the signature matching method. This method can only detect known crypto-ransomware, and although very rigid, it was accurate and fast. The two phases in PEDA formed two layers of early detection for crypto-ransomware to ensure zero files lost to the user. However in this research, we focused upon Phase-I, which was the LA. Based on our results, the LA had the lowest FPR of 1.56% compared to Naive Bayes (NB), Random Forest (RF), Ensemble (NB and RF) and EldeRan (a machine learning approach to analyze and classify ransomware). Low FPR indicates that LA has a low probability of predicting goodware wrongly.

Automated linkage of patient records from disparate sources

2016 ◽  
Vol 27 (1) ◽  
pp. 172-184
Author(s):  
Xiaochun Li ◽  
Huiping Xu ◽  
Changyu Shen ◽  
Shaun Grannis
Keyword(s):  
Data Quality ◽  
Model Simulation ◽  
False Positive Rate ◽  
Area Under The Curve ◽  
Automated Method ◽  
Discriminating Power ◽  
Lower False Positive Rate ◽  
Positive Rate ◽  
The Face ◽  
Biased Estimates

We introduce an automated method of record linkage that has two key features, automated selection of match field interactions to include in the model for estimation and automated threshold determination for classifying record pairs to matches or non-matches. We applied our method to two real-world examples. The first example demonstrated results consistent with our earlier work: When data quality is adequate and the match field discriminating power is high, matching algorithms exhibit similar performance. The second example demonstrated that our method yields a lower false positive rate and higher positive predictive value than the Fellegi-Sunter model in the face of low data quality. When compared to the Fellegi-Sunter model, simulation studies suggest that our method exhibits better overall performance as indicated by higher area under the curve, and less biased estimates for both the match prevalence rate and the m- and u-probabilities over a range of data scenarios, especially when the match prevalence is extreme. Computationally, our method is as efficient as the Fellegi-Sunter model. We recommend this method in situations that an unsupervised linking algorithm is needed.

scholarly journals Voxel-level Classification of Prostate Cancer Using a Four-Compartment Restriction Spectrum Imaging Model

2020 ◽  
Author(s):  
Christine H Feng ◽  
Christopher Charles Conlin ◽  
Kanha Batra ◽  
Ana Rodriguez-Soto ◽  
Roshan Karunamuni ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
False Positive ◽  
False Positive Rate ◽  
Area Under The Curve ◽  
Roc Curves ◽  
Imaging Data ◽  
Pelvic Mri ◽  
Positive Rate ◽  
Model Coefficient ◽  
Spectrum Imaging

Purpose: Diffusion MRI is integral to detection of prostate cancer (PCa), but conventional ADC cannot capture the complexity of prostate tissues. A four-compartment restriction spectrum imaging (RSI4) model was recently found to optimally characterize pelvic diffusion signals, and the model coefficient for the slowest diffusion compartment, RSI4-C1, yielded greatest tumor conspicuity. In this study, RSI4-C1 was evaluated as a quantitative voxel-level classifier of PCa. Methods: This was a retrospective analysis of 46 men who underwent an expanded-acquisition pelvic MRI for suspected PCa. Twenty-three men had no detectable cancer on biopsy or clinical follow-up; the other 23 had biopsy-proven PCa corresponding to a lesion on MRI (PI-RADS category 3-5). High-confidence cancer voxels were delineated by expert consensus, using imaging data and biopsy results. The entire prostate was considered benign in patients with no detectable cancer. Diffusion images were used to calculate RSI4-C1 and conventional ADC. Voxel-level discrimination of PCa from benign prostate tissue was assessed via receiver operating characteristic (ROC) curves generated by bootstrapping with patient-level case resampling. Specifically, we compared RSI4-C1 and conventional ADC on mean (and 95% CI) for two metrics: area under the curve (AUC) and false-positive rate for a sensitivity of 90% (FPR90). Classifier images were also compared. Results: RSI4-C1 outperformed conventional ADC, with greater AUC [0.977 (0.951-0.991) vs. 0.921 (0.873-0.949)] and lower FPR90 [0.033 (0.009-0.083) vs. 0.201 (0.131-0.300)]. Conclusion: RSI4-C1 yielded a quantitative, voxel-level classifier of PCa that was superior to conventional ADC. RSI classifier images with a low false-positive rate might improve PCa detection.

scholarly journals Performance of Fetal Medicine Foundation Software for Pre-Eclampsia Prediction Upon Marker Customization: Cross-Sectional Study (Preprint)

2019 ◽  
Author(s):  
Karina Bilda De Castro Rezende ◽  
Antonio José Ledo Alves Cunha ◽  
Joffre Amim Jr ◽  
Wescule De Moraes Oliveira ◽  
Maria Eduarda Belloti Leão ◽  
...  
Keyword(s):  
High Risk ◽  
False Positive ◽  
False Positive Rate ◽  
Area Under The Curve ◽  
Screening Tests ◽  
Risk Scores ◽  
Cross Sectional ◽  
Fetal Medicine ◽  
Maternal Characteristics ◽  
Positive Rate

BACKGROUND FMF2012 is an algorithm developed by the Fetal Medicine Foundation (FMF) to predict pre-eclampsia on the basis of maternal characteristics combined with biophysical and biochemical markers. Afro-Caribbean ethnicity is the second risk factor, in magnitude, found in populations tested by FMF, which was not confirmed in a Brazilian setting. OBJECTIVE This study aimed to analyze the performance of pre-eclampsia prediction software by customization of maternal ethnicity. METHODS This was a cross-sectional observational study, with secondary evaluation of data from FMF first trimester screening tests of singleton pregnancies. Risk scores were calculated from maternal characteristics and biophysical markers, and they were presented as the risk for early pre-eclampsia (PE34) and preterm pre-eclampsia (PE37). The following steps were followed: (1) identification of women characterized as black ethnicity; (2) calculation of early and preterm pre-eclampsia risk, reclassifying them as white, which generated a new score; (3) comparison of the proportions of women categorized as high risk between the original and new scores; (4) construction of the receiver operator characteristic curve; (5) calculation of the area under the curve, sensitivity, and false positive rate; and (6) comparison of the area under the curve, sensitivity, and false positive rate of the original with the new risk by chi-square test. RESULTS A total of 1531 cases were included in the final sample, with 219 out of 1531 cases (14.30; 95% CI 12.5-16.0) and 182 out of 1531 cases (11.88%; 95% CI 10.3-13.5) classified as high risk for pre-eclampsia development, originally and after recalculating the new risk, respectively. The comparison of FMF2012 predictive model performance between the originally estimated risks and the estimated new risks showed that the difference was not significant for sensitivity and area under the curve, but it was significant for false positive rate. CONCLUSIONS We conclude that black ethnicity classification of Brazilian pregnant women by the FMF2012 algorithm increases the false positive rate. Suppressing ethnicity effect did not improve the test sensitivity. By modifying demographic characteristics, it is possible to improve some performance aspects of clinical prediction tests.

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