Utility of SNOMED CT in automated expansion of clinical terms in discharge summaries: Testing issues of coverage

2020 ◽  
pp. 183335832093452
Author(s):  
Aleksandar Zivaljevic ◽  
Koray Atalag ◽  
James Warren
Keyword(s):  
False Negative ◽  
False Negative Rate ◽  
True Positive Rate ◽  
Knowledge Extraction ◽  
Snomed Ct ◽  
Automated Method ◽  
Statistical Measures ◽  
Rest Api ◽  
The Individual ◽  
Discharge Summaries

Objective: This study tests coverage of SNOMED CT as an expansion source in the process of automated expansion of clinical terms found in discharge summaries. Term expansion is commonly used as a technique in knowledge extraction, query formulation and semantic modelling among other applications. However, characteristics of the sources might affect credibility of outputs, and coverage is one of them. Method: We developed an automated method for testing coverage of more than one source at a time. We used several methods to clean our corpus of discharge summaries before we extracted text fragments as candidates for clinical concepts. We then used Unified Medical Language System (UMLS) sources and UMLS REST API to filter concepts from the pool of text fragments. Statistical measures like true positive rate and false negative rate were used to decide on the coverage of the source. We also tested the coverage of the individual SNOMED CT hierarchies using the same methods. Results: Findings suggest that a combination of four terminologies tested (SNOMED CT, NCI, LNC and MSH) achieves over 90% of coverage for term expansion. We also found that the SNOMED CT hierarchies that hold clinically relevant concepts provided 60% of coverage. Conclusion: We believe that our findings and the method we developed will be of use to both scientists and practitioners working in the domain of knowledge extraction.

scholarly journals Pretherapeutic Imaging for Axillary Staging in Breast Cancer: A Systematic Review and Meta-Analysis of Ultrasound, MRI and FDG PET

2021 ◽  
Vol 10 (7) ◽  
pp. 1543
Author(s):  
Morwenn Le Boulc’h ◽  
Julia Gilhodes ◽  
Zara Steinmeyer ◽  
Sébastien Molière ◽  
Carole Mathelin
Keyword(s):  
Systematic Review ◽  
Sensitivity And Specificity ◽  
Meta Analysis ◽  
False Negative ◽  
Significant Proportion ◽  
False Negative Rate ◽  
True Positive Rate ◽  
Axillary Staging ◽  
Positron Emission ◽  
Positive Rate

Background: This systematic review aimed at comparing performances of ultrasonography (US), magnetic resonance imaging (MRI), and fluorodeoxyglucose positron emission tomography (PET) for axillary staging, with a focus on micro- or micrometastases. Methods: A search for relevant studies published between January 2002 and March 2018 was conducted in MEDLINE database. Study quality was assessed using the QUality Assessment of Diagnostic Accuracy Studies checklist. Sensitivity and specificity were meta-analyzed using a bivariate random effects approach; Results: Across 62 studies (n = 10,374 patients), sensitivity and specificity to detect metastatic ALN were, respectively, 51% (95% CI: 43–59%) and 100% (95% CI: 99–100%) for US, 83% (95% CI: 72–91%) and 85% (95% CI: 72–92%) for MRI, and 49% (95% CI: 39–59%) and 94% (95% CI: 91–96%) for PET. Interestingly, US detects a significant proportion of macrometastases (false negative rate was 0.28 (0.22, 0.34) for more than 2 metastatic ALN and 0.96 (0.86, 0.99) for micrometastases). In contrast, PET tends to detect a significant proportion of micrometastases (true positive rate = 0.41 (0.29, 0.54)). Data are not available for MRI. Conclusions: In comparison with MRI and PET Fluorodeoxyglucose (FDG), US is an effective technique for axillary triage, especially to detect high metastatic burden without upstaging majority of micrometastases.

scholarly journals Algoritma Naïve Bayes Untuk Memprediksi Kredit Macet Pada Koperasi Simpan Pinjam

2019 ◽  
Vol 4 (2) ◽  
Author(s):  
Diah Puspitasari ◽  
Syifa Sintia Al Khautsar ◽  
Wida Prima Mustika
Keyword(s):  
Data Mining ◽  
Predictive Value ◽  
Naive Bayes ◽  
False Negative ◽  
False Negative Rate ◽  
True Positive Rate ◽  
Naïve Bayes ◽  
Data Mining Technique ◽  
Application Form ◽  
Using Data

Cooperatives are a forum that can help people, especially small and medium-sized communities. Cooperatives play an important role in the economic growth of the community such as the price of basic commodities which are relatively cheap and there are also cooperatives that offer borrowing and storing money for the community. Constraints that have been felt by this cooperative are that borrowers find it difficult to repay loan installments, causing bad credit. Because the cooperative in conducting credit analysis is carried out in a personal manner, namely by filling out the loan application form along with the requirements and conducting a field survey. Therefore there is a need for an evaluation to be carried out in lending to borrowers. To minimize these problems, it is necessary to detect customer criteria that are used to predict bad loans and to determine whether or not the elites are eligible to take credit using data mining. The data mining technique used is classification with the Naive Bayes method. Based on testing the accuracy of the resulting model obtained accuracy level of 59%, sensitivity (True Positive Rate (TP Rate) or Recall) of 46.80%, specificity (False Negative Rate (FN Rate or Precision) of 69.81%, Positive Predictive Value (PPV) of 57.89%, and Negative Predictive Value (NPV) of 59.67%.

scholarly journals Macromolecule Particle Picking and Segmentation of a KLH Database by Unsupervised Cryo-EM Image Processing

Biomolecules ◽  
2019 ◽  
Vol 9 (12) ◽  
pp. 809
Author(s):  
Miguel Carrasco ◽  
Patricio Toledo ◽  
Nicole D. Tischler
Keyword(s):  
Signal To Noise Ratio ◽  
False Positive Rate ◽  
False Negative ◽  
False Negative Rate ◽  
Keyhole Limpet Hemocyanin ◽  
True Positive Rate ◽  
Cryo Electron Microscopy ◽  
Positive Rate ◽  
Top View ◽  
Non Negative Matrix Factorization

Segmentation is one of the most important stages in the 3D reconstruction of macromolecule structures in cryo-electron microscopy. Due to the variability of macromolecules and the low signal-to-noise ratio of the structures present, there is no generally satisfactory solution to this process. This work proposes a new unsupervised particle picking and segmentation algorithm based on the composition of two well-known image filters: Anisotropic (Perona–Malik) diffusion and non-negative matrix factorization. This study focused on keyhole limpet hemocyanin (KLH) macromolecules which offer both a top view and a side view. Our proposal was able to detect both types of views and separate them automatically. In our experiments, we used 30 images from the KLH dataset of 680 positive classified regions. The true positive rate was 95.1% for top views and 77.8% for side views. The false negative rate was 14.3%. Although the false positive rate was high at 21.8%, it can be lowered with a supervised classification technique.

scholarly journals An Enhanced Multimodal Stacking Scheme for Online Pornographic Content Detection

2020 ◽  
Vol 10 (8) ◽  
pp. 2943
Author(s):  
Kwangho Song ◽  
Yoo-Sung Kim
Keyword(s):  
Average Rate ◽  
False Negative ◽  
False Negative Rate ◽  
Serial Order ◽  
True Positive Rate ◽  
Online Detection ◽  
Convolutional Network ◽  
Signal Change ◽  
Positive Rate ◽  
Auditory Features

An enhanced multimodal stacking scheme is proposed for quick and accurate online detection of harmful pornographic contents on the Internet. To accurately detect harmful contents, the implicative visual features (auditory features) are extracted using a bi-directional RNN (recurrent neural network) with VGG-16 (a multilayered dilated convolutional network) to implicitly express the signal change patterns over time within each input. Using only the implicative visual and auditory features, a video classifier and an audio classifier are trained, respectively. By using both features together, one fusion classifier is also trained. Then, these three component classifiers are stacked in the enhanced ensemble scheme to reduce the false negative errors in a serial order of the fusion classifier, video classifier, and audio classifier for a quick online detection. The proposed multimodal stacking scheme yields an improved true positive rate of 95.40% and a false negative rate of 4.60%, which are superior values to previous studies. In addition, the proposed stacking scheme can accurately detect harmful contents up to 74.58% and an average rate of 62.16% faster than the previous stacking scheme. Therefore, the proposed enhanced multimodal stacking scheme can be used to quickly and accurately filter out harmful contents in the online environments.

Tuning IMS station processing parameters and detection thresholds to increase detection precision and decrease detection miss rate

2020 ◽  
Author(s):  
Christos Saragiotis ◽  
Ivan Kitov
Keyword(s):  
False Discovery Rate ◽  
Binary Classification ◽  
False Negative ◽  
Detection Threshold ◽  
False Negative Rate ◽  
Processing Parameters ◽  
Detection Algorithm ◽  
Detection Scheme ◽  
Statistical Measures ◽  
False Discovery

<p>Two principal performance measures of the International Monitoring System (IMS) stations detection capability are the rate of automatic detections associated with events in the Reviewed Event Bulletin (REB) and the rate of detections manually added to the REB. These two metrics roughly correspond to the precision (which is the complement of the false-discovery rate) and miss rate or false-negative rate statistical measures of a binary classification test, respectively. The false-discovery and miss rates are clearly significantly influenced by the number of phases detected by the detection algorithm, which in turn depends on prespecified slowness-, frequency- and azimuth- dependent threshold values used in the short-term average over long-term average ratio detection scheme of the IMS stations. In particular, the lower the threshold, the more the detections and therefore the lower the miss rate but the higher the false discovery rate; the higher the threshold, the less the detections and therefore the higher the miss rate but also the lower the false discovery rate. In that sense decreasing both the false-discovery rate and the miss rate are conflicting goals that need to be balanced. On one hand, it is essential that the miss rate is as low as possible since no nuclear explosion should go unnoticed by the IMS. On the other hand, a high false-discovery rate compromises the quality of the automatically generated event lists and adds heavy and unnecessary workload to the seismic analysts during the interactive processing stage.</p><p>A previous study concluded that a way to decrease both the miss and false-discovery rates as well as the analyst workload is to increase the retiming interval, i.e., the maximum allowable time that an analyst is allowed to move an arrival pick without having to declare a new arrival. Indeed, when a detection needs to be moved by an interval larger than the retiming interval, not only is this a much more time-consuming task for the analyst than just retiming it, but it also affects negatively both the associated rate (the automatic detection is deleted and therefore not associated to an event) and the added rate (a new arrival has to be added to arrival list). The International Data Centre has increased the retiming interval from 4 s to 10 s since October 2018. We show how this change affected the associated-detections and added-detections rates and how the values of these metrics can be further improved by tuning the detection threshold levels.</p>

scholarly journals A statistical analysis of TIR anomalies extracted by RSTs in relation to an earthquake in the Sichuan area using MODIS LST data

2019 ◽  
Vol 19 (3) ◽  
pp. 535-549 ◽  
Author(s):  
Ying Zhang ◽  
Qingyan Meng
Keyword(s):  
Earthquake Prediction ◽  
Land Surface ◽  
Parametric Method ◽  
False Negative ◽  
False Negative Rate ◽  
Geographic Area ◽  
True Positive Rate ◽  
Robust Estimator ◽  
Earthquake Catalog ◽  
Sichuan Region

Abstract. Research in the field of earthquake prediction has a long history, but the inadequacies of traditional approaches to the study of seismic threats have become increasingly evident. Remote sensing and Earth observation technology, an emerging method that can rapidly capture information concerning anomalies associated with seismic activity across a wide geographic area, has for some time been believed to be the key to overcoming the bottleneck in earthquake prediction studies. However, a multi-parametric method appears to be the most promising approach for increasing the reliability and precision of short-term seismic hazard forecasting, and thermal infrared (TIR) anomalies are important earthquake precursors. While several studies have investigated the correlation among TIR anomalies identified by the robust satellite techniques (RSTs) methodology and single earthquakes, few studies have extracted TIR anomalies over a long period within a large study area. Moreover, statistical analyses are required to determine whether TIR anomalies are precursors to earthquakes. In this paper, RST data analysis and the Robust Estimator of TIR Anomalies (RETIRA) index were used to extract the TIR anomalies from 2002 to 2018 in the Sichuan region using Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) data, while the earthquake catalog was used to ascertain the correlation between TIR anomalies and earthquake occurrences. Most TIR anomalies corresponded to earthquakes, and statistical methods were used to verify the correlation between the extracted TIR anomalies and earthquakes. This is the first time that the ability to predict earthquakes has been evaluated based on the positive predictive value (PPV), false discovery rate (FDR), true-positive rate (TPR), and false-negative rate (FNR). The statistical results indicate that the prediction potential of RSTs with use of MODIS is limited with regard to the Sichuan region.

scholarly journals A KNIME-Based Analysis of the Zebrafish Photomotor Response Clusters the Phenotypes of 14 Classes of Neuroactive Molecules

2015 ◽  
Vol 21 (5) ◽  
pp. 427-436 ◽  
Author(s):  
Daniëlle Copmans ◽  
Thorsten Meinl ◽  
Christian Dietz ◽  
Matthijs van Leeuwen ◽  
Julia Ortmann ◽  
...  
Keyword(s):  
Drug Discovery ◽  
False Negative ◽  
False Negative Rate ◽  
Automated Analysis ◽  
True Positive Rate ◽  
Zebrafish Embryos ◽  
True Positive ◽  
Neuroactive Compounds ◽  
Analysis Workflow ◽  
Positive Rate

Recently, the photomotor response (PMR) of zebrafish embryos was reported as a robust behavior that is useful for high-throughput neuroactive drug discovery and mechanism prediction. Given the complexity of the PMR, there is a need for rapid and easy analysis of the behavioral data. In this study, we developed an automated analysis workflow using the KNIME Analytics Platform and made it freely accessible. This workflow allows us to simultaneously calculate a behavioral fingerprint for all analyzed compounds and to further process the data. Furthermore, to further characterize the potential of PMR for mechanism prediction, we performed PMR analysis of 767 neuroactive compounds covering 14 different receptor classes using the KNIME workflow. We observed a true positive rate of 25% and a false negative rate of 75% in our screening conditions. Among the true positives, all receptor classes were represented, thereby confirming the utility of the PMR assay to identify a broad range of neuroactive molecules. By hierarchical clustering of the behavioral fingerprints, different phenotypical clusters were observed that suggest the utility of PMR for mechanism prediction for adrenergics, dopaminergics, serotonergics, metabotropic glutamatergics, opioids, and ion channel ligands.

The compliance, true positive and false negative rates of the Charing Cross protocol for magnetic resonance imaging screening for cerebellopontine angle lesions

2007 ◽  
Vol 122 (3) ◽  
pp. 255-258
Author(s):  
J D Snelling ◽  
M Krywawych ◽  
A Majithia ◽  
J P Harcourt
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Cerebellopontine Angle ◽  
Compliance Rate ◽  
False Negative ◽  
False Negative Rate ◽  
True Positive Rate ◽  
True Positive ◽  
Resonance Imaging ◽  
Negative Rate

AbstractObjectives:To assess the effectiveness and determine the compliance to a local protocol for requesting magnetic resonance imaging scans to screen for the presence of cerebellopontine angle lesions.Methods:A combined retrospective study of all patients who had magnetic resonance imaging scans requested six months prior to and one year following introduction of the protocol and assessment of the true positive and false negative rate of the protocol by assessment of its sensitivity in cases referred from outside the department.Results:Comparison of the number of scans in each period showed a reduction in annualised rate of 142 to 46. The incidence of positive scans was the same in both periods, increasing the true positive rate from 1.4 to 4.3 per cent. The false negative rate was 1.1 per cent.Conclusions:The Charing Cross protocol has a good compliance rate within the department, has reduced the cost of screening for cerebellopontine angle lesions and has an acceptable true positive and false negative rate.

Image Quality Assessment and Outliers Filtering in an Image-Based Animal Supervision System

Biometrics ◽  
2017 ◽  
pp. 1241-1257
Author(s):  
Ehsan Khoramshahi ◽  
Juha Hietaoja ◽  
Anna Valros ◽  
Jinhyeon Yun ◽  
Matti Pastell
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Real Time ◽  
Image Quality Assessment ◽  
False Negative ◽  
False Negative Rate ◽  
True Positive Rate ◽  
Supervision System ◽  
Positive Rate ◽  
Validation Set

This paper presents a probabilistic framework for the image quality assessment (QA), and filtering of outliers, in an image-based animal supervision system (asup). The proposed framework recognizes asup's imperfect frames in two stages. The first stage deals with the similarity analysis of the same-class distributions. The objective of this stage is to maximize the separability measures by defining a set of similarity indicators (SI) under the condition that the number of permissible values for them is restricted to be relatively low. The second stage, namely faulty frame recognition (FFR), deals with asup's QA training and real-time quality assessment (RTQS). In RTQS, decisions are made based on a real-time quality assessment mechanism such that the majority of the defected frames are removed from the consecutive sub routines that calculate the movements. The underlying approach consists of a set of SI indexes employed in a simple Bayesian inference model. The results confirm that a significant amount of defected frames can be efficiently classified by this approach. The performance of the proposed technique is demonstrated by the classification on a cross-validation set of mixed high and low quality frames. The classification shows a true positive rate of 88.6% while the false negative rate is only about 2.5%.

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