scholarly journals Predicting Vancomycin-Resistant Enterococci (VRE) and Carbapenem-Resistant Organism (CRO) Colonization in the Intensive Care Unit

2020 ◽  
Vol 41 (S1) ◽  
pp. s45-s45
Author(s):  
Çaǧlar Çaǧlayan ◽  
Scott Levin ◽  
Aaron Michael Milstone ◽  
Pranita Tamma ◽  
Patricia Simner ◽  
...  
Keyword(s):  
Intensive Care ◽  
Sensitivity And Specificity ◽  
False Positive ◽  
False Positive Rate ◽  
Machine Learning Algorithms ◽  
Surgical Intensive Care ◽  
Term Care ◽  
Icu Admission ◽  
Positive Rate ◽  
Program Grant

Background: Rapidly identifying patients colonized with multidrug-resistant organisms (MDROs) upon ICU admission is critical to control and prevent the spread of these pathogens in healthcare facilities. Electronic health records (EHR) provide a rich source of data to predict the likelihood of MDRO colonization at admission, whereas surveillance methods are resource intensive and results are not immediately available. Our objectives were (1) to predict VRE and CRO colonization at ICU admission and (2) to identify patient subpopulations at higher risk for colonization with these MDROs. Methods: We conducted a retrospective analysis of patients aged ≥16 years admitted to any of 6 medical or surgical intensive care units (ICU) in the Johns Hopkins Hospital from July 1, 2016, through June 30, 2018. Perirectal swabs were collected at ICU unit admission and were tested for VRE and CRO. Patient demographic data, prior hospitalizations, and preadmission clinical data, including prior medication administration, prior diagnoses, and prior procedures, were extracted to develop prediction models. We employed the machine-learning algorithms logistic regression (LR), random forest (RF), and XGBoost (XG). The sum of sensitivity and specificity (ie, Youden’s index) was selected as the performance metric. Results: In total, 5,033 separate ICU visits from 3,385 patients were included, where 555 (11%) and 373 (7%) admissions tested positive for VRE and CRO, respectively. The sensitivity and specificity of our models for VRE were 78% and 80% with LR, 80% and 82% with RF, and 77% and 87% with XG. Predictions for CRO were not as precise, with LR at 73% and 53%, RF at 81% and 48%, and XG at 69% and 61%. The XG algorithm was the best-performing algorithm for both VRE and CRO. Prior VRE colonization, recent (<180 days) long-term care facility stay, and prior hospitalization >60 days were the key predictors for VRE, whereas the primary predictor for CRO colonization was prior carbapenem use. Conclusions: We demonstrated that EHR data can be used to predict >75% of VRE positive cases with a <15% false-positive rate and ~70% of CRO cases with a <40% false-positive rate. Future studies using larger sample sizes may improve the prediction accuracy and inform model generalizability across sites and thus reduce the risk of transmission of MDROs by rapidly identifying MDRO-colonized patients.Funding: This work was funded by the Centers for Disease Control and Prevention (CDC) Epicenters Program (Grant Number 1U54CK000447) and the CDC MInD-Healthcare Program (Grant Number 1U01CK000536).Disclosures: Aaron Milstone, BD (consulting)

Machine Learning for Automated Polyp Detection in Computed Tomography Colonography

2012 ◽  
pp. 830-850
Author(s):  
Abhilash Alexander Miranda ◽  
Olivier Caelen ◽  
Gianluca Bontempi
Keyword(s):  
Machine Learning ◽  
Computed Tomography ◽  
False Positive ◽  
False Positive Rate ◽  
Learning Algorithms ◽  
Colorectal Polyps ◽  
Machine Learning Algorithms ◽  
Computed Tomography Colonography ◽  
Positive Rate ◽  
Independent Features

This chapter presents a comprehensive scheme for automated detection of colorectal polyps in computed tomography colonography (CTC) with particular emphasis on robust learning algorithms that differentiate polyps from non-polyp shapes. The authors’ automated CTC scheme introduces two orientation independent features which encode the shape characteristics that aid in classification of polyps and non-polyps with high accuracy, low false positive rate, and low computations making the scheme suitable for colorectal cancer screening initiatives. Experiments using state-of-the-art machine learning algorithms viz., lazy learning, support vector machines, and naïve Bayes classifiers reveal the robustness of the two features in detecting polyps at 100% sensitivity for polyps with diameter greater than 10 mm while attaining total low false positive rates, respectively, of 3.05, 3.47 and 0.71 per CTC dataset at specificities above 99% when tested on 58 CTC datasets. The results were validated using colonoscopy reports provided by expert radiologists.

Machine Learning for Automated Polyp Detection in Computed Tomography Colonography

2010 ◽  
pp. 54-77
Author(s):  
Abhilash Alexander Miranda ◽  
Olivier Caelen ◽  
Gianluca Bontempi
Keyword(s):  
Machine Learning ◽  
Computed Tomography ◽  
False Positive ◽  
False Positive Rate ◽  
Learning Algorithms ◽  
Colorectal Polyps ◽  
Machine Learning Algorithms ◽  
Computed Tomography Colonography ◽  
Positive Rate ◽  
Independent Features

This chapter presents a comprehensive scheme for automated detection of colorectal polyps in computed tomography colonography (CTC) with particular emphasis on robust learning algorithms that differentiate polyps from non-polyp shapes. The authors’ automated CTC scheme introduces two orientation independent features which encode the shape characteristics that aid in classification of polyps and non-polyps with high accuracy, low false positive rate, and low computations making the scheme suitable for colorectal cancer screening initiatives. Experiments using state-of-the-art machine learning algorithms viz., lazy learning, support vector machines, and naïve Bayes classifiers reveal the robustness of the two features in detecting polyps at 100% sensitivity for polyps with diameter greater than 10 mm while attaining total low false positive rates, respectively, of 3.05, 3.47 and 0.71 per CTC dataset at specificities above 99% when tested on 58 CTC datasets. The results were validated using colonoscopy reports provided by expert radiologists.

scholarly journals Detecting Smoking Events Using Accelerometer Data Collected Via Smartwatch Technology: Validation Study (Preprint)

2017 ◽  
Author(s):  
Casey A Cole ◽  
Dien Anshari ◽  
Victoria Lambert ◽  
James F Thrasher ◽  
Homayoun Valafar
Keyword(s):  
Machine Learning ◽  
False Positive ◽  
False Positive Rate ◽  
Smoking Behavior ◽  
Observation Time ◽  
Machine Learning Algorithms ◽  
Accelerometer Data ◽  
Convenience Sample ◽  
Study Protocols ◽  
Positive Rate

BACKGROUND Smoking is the leading cause of preventable death in the world today. Ecological research on smoking in context currently relies on self-reported smoking behavior. Emerging smartwatch technology may more objectively measure smoking behavior by automatically detecting smoking sessions using robust machine learning models. OBJECTIVE This study aimed to examine the feasibility of detecting smoking behavior using smartwatches. The second aim of this study was to compare the success of observing smoking behavior with smartwatches to that of conventional self-reporting. METHODS A convenience sample of smokers was recruited for this study. Participants (N=10) recorded 12 hours of accelerometer data using a mobile phone and smartwatch. During these 12 hours, they engaged in various daily activities, including smoking, for which they logged the beginning and end of each smoking session. Raw data were classified as either smoking or nonsmoking using a machine learning model for pattern recognition. The accuracy of the model was evaluated by comparing the output with a detailed description of a modeled smoking session. RESULTS In total, 120 hours of data were collected from participants and analyzed. The accuracy of self-reported smoking was approximately 78% (96/123). Our model was successful in detecting 100 of 123 (81%) smoking sessions recorded by participants. After eliminating sessions from the participants that did not adhere to study protocols, the true positive detection rate of the smartwatch based-detection increased to more than 90%. During the 120 hours of combined observation time, only 22 false positive smoking sessions were detected resulting in a 2.8% false positive rate. CONCLUSIONS Smartwatch technology can provide an accurate, nonintrusive means of monitoring smoking behavior in natural contexts. The use of machine learning algorithms for passively detecting smoking sessions may enrich ecological momentary assessment protocols and cessation intervention studies that often rely on self-reported behaviors and may not allow for targeted data collection and communications around smoking events.

scholarly journals Hadoop based Parallel Machine Learning Algorithms for Intrusion Detection System

2019 ◽  
Vol 9 (1) ◽  
pp. 1152-1156
Keyword(s):  
Machine Learning ◽  
False Positive ◽  
Naive Bayes ◽  
False Positive Rate ◽  
True Positive Rate ◽  
Naïve Bayes ◽  
Machine Learning Algorithms ◽  
True Positive ◽  
Positive Rate ◽  
Bayes Algorithm

Web use and digitized information are getting expanded each day. The measure of information created is likewise getting expanded. On the opposite side, the security assaults cause numerous security dangers in the system, sites and Internet. Interruption discovery in a fast system is extremely a hard undertaking. The Hadoop Implementation is utilized to address the previously mentioned test that is distinguishing interruption in a major information condition at constant. To characterize the strange bundle stream, AI methodologies are used. Innocent Bayes does grouping by a vector of highlight esteems produced using some limited set. Choice Tree is another Machine Learning classifier which is likewise an administered learning model. Choice tree is the stream diagram like tree structure. J48 and Naïve Bayes Algorithm are actualized in Hadoop MapReduce Framework for parallel preparing by utilizing the KDDCup Data Corrected Benchmark dataset records. The outcome acquired is 89.9% True Positive rate and 0.04% False Positive rate for Naive Bayes Algorithm and 98.06% True Positive rate and 0.001% False Positive rate for Decision Tree Algorithm.

scholarly journals Hybrid rule-based botnet detection approach using machine learning for analysing DNS traffic

2021 ◽  
Vol 7 ◽  
pp. e640
Author(s):  
Saif Al-mashhadi ◽  
Mohammed Anbar ◽  
Iznan Hasbullah ◽  
Taief Alaa Alamiedy
Keyword(s):  
Machine Learning ◽  
False Positive ◽  
False Positive Rate ◽  
Communication Protocols ◽  
Cyber Attacks ◽  
Machine Learning Algorithms ◽  
Detection Accuracy ◽  
Botnet Detection ◽  
Internet Service ◽  
Positive Rate

Botnets can simultaneously control millions of Internet-connected devices to launch damaging cyber-attacks that pose significant threats to the Internet. In a botnet, bot-masters communicate with the command and control server using various communication protocols. One of the widely used communication protocols is the ‘Domain Name System’ (DNS) service, an essential Internet service. Bot-masters utilise Domain Generation Algorithms (DGA) and fast-flux techniques to avoid static blacklists and reverse engineering while remaining flexible. However, botnet’s DNS communication generates anomalous DNS traffic throughout the botnet life cycle, and such anomaly is considered an indicator of DNS-based botnets presence in the network. Despite several approaches proposed to detect botnets based on DNS traffic analysis; however, the problem still exists and is challenging due to several reasons, such as not considering significant features and rules that contribute to the detection of DNS-based botnet. Therefore, this paper examines the abnormality of DNS traffic during the botnet lifecycle to extract significant enriched features. These features are further analysed using two machine learning algorithms. The union of the output of two algorithms proposes a novel hybrid rule detection model approach. Two benchmark datasets are used to evaluate the performance of the proposed approach in terms of detection accuracy and false-positive rate. The experimental results show that the proposed approach has a 99.96% accuracy and a 1.6% false-positive rate, outperforming other state-of-the-art DNS-based botnet detection approaches.

Improving diagnosis of acute appendicitis with atypical findings by Tc-99m HMPAO leukocyte scan

2002 ◽  
Vol 41 (01) ◽  
pp. 37-41 ◽  
Author(s):  
S. Shung-Shung ◽  
S. Yu-Chien ◽  
Y. Mei-Due ◽  
W. Hwei-Chung ◽  
A. Kao
Keyword(s):  
Acute Appendicitis ◽  
False Positive ◽  
False Positive Rate ◽  
Accurate Method ◽  
Clinical Findings ◽  
Pathological Findings ◽  
Lower Quadrant ◽  
Predictive Values ◽  
Positive Rate

Summary Aim: Even with careful observation, the overall false-positive rate of laparotomy remains 10-15% when acute appendicitis was suspected. Therefore, the clinical efficacy of Tc-99m HMPAO labeled leukocyte (TC-WBC) scan for the diagnosis of acute appendicitis in patients presenting with atypical clinical findings is assessed. Patients and Methods: Eighty patients presenting with acute abdominal pain and possible acute appendicitis but atypical findings were included in this study. After intravenous injection of TC-WBC, serial anterior abdominal/pelvic images at 30, 60, 120 and 240 min with 800k counts were obtained with a gamma camera. Any abnormal localization of radioactivity in the right lower quadrant of the abdomen, equal to or greater than bone marrow activity, was considered as a positive scan. Results: 36 out of 49 patients showing positive TC-WBC scans received appendectomy. They all proved to have positive pathological findings. Five positive TC-WBC were not related to acute appendicitis, because of other pathological lesions. Eight patients were not operated and clinical follow-up after one month revealed no acute abdominal condition. Three of 31 patients with negative TC-WBC scans received appendectomy. They also presented positive pathological findings. The remaining 28 patients did not receive operations and revealed no evidence of appendicitis after at least one month of follow-up. The overall sensitivity, specificity, accuracy, positive and negative predictive values for TC-WBC scan to diagnose acute appendicitis were 92, 78, 86, 82, and 90%, respectively. Conclusion: TC-WBC scan provides a rapid and highly accurate method for the diagnosis of acute appendicitis in patients with equivocal clinical examination. It proved useful in reducing the false-positive rate of laparotomy and shortens the time necessary for clinical observation.

Predicting Fetal Chromosome Anomalies in the First Trimester Using Pregnancy Associated Plasma Protein-A: A Comparison of Statistical Methods

1993 ◽  
Vol 32 (02) ◽  
pp. 175-179 ◽  
Author(s):  
B. Brambati ◽  
T. Chard ◽  
J. G. Grudzinskas ◽  
M. C. M. Macintosh
Keyword(s):  
Logistic Regression ◽  
General Population ◽  
Likelihood Ratio ◽  
False Positive ◽  
False Positive Rate ◽  
Ratio Method ◽  
Detection Rates ◽  
Gaussian Distributions ◽  
Positive Rate ◽  
Likelihood Ratio Method

Abstract:The analysis of the clinical efficiency of a biochemical parameter in the prediction of chromosome anomalies is described, using a database of 475 cases including 30 abnormalities. A comparison was made of two different approaches to the statistical analysis: the use of Gaussian frequency distributions and likelihood ratios, and logistic regression. Both methods computed that for a 5% false-positive rate approximately 60% of anomalies are detected on the basis of maternal age and serum PAPP-A. The logistic regression analysis is appropriate where the outcome variable (chromosome anomaly) is binary and the detection rates refer to the original data only. The likelihood ratio method is used to predict the outcome in the general population. The latter method depends on the data or some transformation of the data fitting a known frequency distribution (Gaussian in this case). The precision of the predicted detection rates is limited by the small sample of abnormals (30 cases). Varying the means and standard deviations (to the limits of their 95% confidence intervals) of the fitted log Gaussian distributions resulted in a detection rate varying between 42% and 79% for a 5% false-positive rate. Thus, although the likelihood ratio method is potentially the better method in determining the usefulness of a test in the general population, larger numbers of abnormal cases are required to stabilise the means and standard deviations of the fitted log Gaussian distributions.

Identification of and Correction for Publication Bias: Comment

2019 ◽  
Author(s):  
Amanda Kvarven ◽  
Eirik Strømland ◽  
Magnus Johannesson
Keyword(s):  
Publication Bias ◽  
False Positive ◽  
Large Scale ◽  
Meta Analysis ◽  
False Positive Rate ◽  
Effect Sizes ◽  
Replication Studies ◽  
Moderate Reduction ◽  
Positive Rate ◽  
Meta Analyses

Andrews &amp; Kasy (2019) propose an approach for adjusting effect sizes in meta-analysis for publication bias. We use the Andrews-Kasy estimator to adjust the result of 15 meta-analyses and compare the adjusted results to 15 large-scale multiple labs replication studies estimating the same effects. The pre-registered replications provide precisely estimated effect sizes, which do not suffer from publication bias. The Andrews-Kasy approach leads to a moderate reduction of the inflated effect sizes in the meta-analyses. However, the approach still overestimates effect sizes by a factor of about two or more and has an estimated false positive rate of between 57% and 100%.

scholarly journals PRATD: A Phased Remote Access Trojan Detection Method with Double-Sided Features

Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1894
Author(s):  
Chun Guo ◽  
Zihua Song ◽  
Yuan Ping ◽  
Guowei Shen ◽  
Yuhei Cui ◽  
...  
Keyword(s):  
False Positive ◽  
Detection Method ◽  
False Positive Rate ◽  
True Positive Rate ◽  
Remote Access ◽  
Detection Methods ◽  
Security Threats ◽  
True Positive ◽  
Trojan Detection ◽  
Positive Rate

Remote Access Trojan (RAT) is one of the most terrible security threats that organizations face today. At present, two major RAT detection methods are host-based and network-based detection methods. To complement one another’s strengths, this article proposes a phased RATs detection method by combining double-side features (PRATD). In PRATD, both host-side and network-side features are combined to build detection models, which is conducive to distinguishing the RATs from benign programs because that the RATs not only generate traffic on the network but also leave traces on the host at run time. Besides, PRATD trains two different detection models for the two runtime states of RATs for improving the True Positive Rate (TPR). The experiments on the network and host records collected from five kinds of benign programs and 20 famous RATs show that PRATD can effectively detect RATs, it can achieve a TPR as high as 93.609% with a False Positive Rate (FPR) as low as 0.407% for the known RATs, a TPR 81.928% and FPR 0.185% for the unknown RATs, which suggests it is a competitive candidate for RAT detection.

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