scholarly journals Using The Random Forest Algorithm To Detect The Activity of Thyroid-Associated Ophthalmopathy

2021 ◽  
Author(s):  
Minghui Wang ◽  
Hanqiao Zhang ◽  
Li Dong ◽  
Yang Li ◽  
Zhijia Hou ◽  
...  
Keyword(s):  
Random Forest ◽  
Predictive Value ◽  
Diagnostic Performance ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Random Forest Model ◽  
Random Forest Algorithm ◽  
Forest Model ◽  
Thyroid Associated Ophthalmopathy ◽  
Operating Characteristic Curve

Abstract Objective: The aim of this study is to establish a random forest model to detect active and quiescent phases of patients with Thyroid-associated ophthalmopathy (TAO) and to evaluate its diagnostic performance.Methods：A total of 146 patients (292 eyes) who were diagnosed with TAO and were treated in the Ophthalmology Outpatient Clinic of Beijing TongRen hospital were retrospectively included in the study. We took the clinical activity score of TAO as the target; took gender, age, smoking status, I-131 treatment history, thyroid nodules, thyromegaly, thyroid hormone and TSH-receptor antibodies (TRAb) as predictive characteristic variables to establish a random forest model. The proportion of the training group to the testing group was 7:3. We analyzed the model’s accuracy, precision, sensitivity, specificity, positive predictive value (PPV), negative predictive value (PPV), F1 score and out-of-bag (OOB) error, with the accuracy, the brier loss and the area under the receiver operating characteristic curve compared with logistic regression model.Results：Our model has an accuracy of 0.93, a sensitivity of 0.88, a specificity of 0.96, a positive predictive value of 0.94, a negative predictive value of 0.93, an F1 score of 0.91 and an OOB error of 0.12. The accuracy of the random forest model and the logistic regression model were 0.93 and 0.79, respectively, the brier loss were 0.06 and 0.20, and the area under the receiver operating characteristic curve were 0.95 and 0.86.Conclusion：By integrating these high-risk factors, the random forest algorithm can be used as a complementary diagnostic method to determine the activity of TAO, showing prominent diagnostic performance.

scholarly journals Machine learning for identification of surgeries with high risks of cancellation

2018 ◽  
Vol 26 (1) ◽  
pp. 141-155 ◽  
Author(s):  
Li Luo ◽  
Fengyi Zhang ◽  
Yao Yao ◽  
RenRong Gong ◽  
Martina Fu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Predictive Value ◽  
Operating Characteristic ◽  
Sampling Methods ◽  
Characteristic Curve ◽  
Support Vector ◽  
Chi Square ◽  
Stable Performance ◽  
Operating Characteristic Curve

Surgery cancellations waste scarce operative resources and hinder patients’ access to operative services. In this study, the Wilcoxon and chi-square tests were used for predictor selection, and three machine learning models – random forest, support vector machine, and XGBoost – were used for the identification of surgeries with high risks of cancellation. The optimal performances of the identification models were as follows: sensitivity − 0.615; specificity − 0.957; positive predictive value − 0.454; negative predictive value − 0.904; accuracy − 0.647; and area under the receiver operating characteristic curve − 0.682. Of the three models, the random forest model achieved the best performance. Thus, the effective identification of surgeries with high risks of cancellation is feasible with stable performance. Models and sampling methods significantly affect the performance of identification. This study is a new application of machine learning for the identification of surgeries with high risks of cancellation and facilitation of surgery resource management.

Core Genome Allelic Profiles of Clinical Klebsiella pneumoniae Strains Using a Random Forest Algorithm Based on Multilocus Sequence Typing Scheme for Hypervirulence Analysis

2020 ◽  
Vol 221 (Supplement_2) ◽  
pp. S263-S271 ◽  
Author(s):  
Peng Lan ◽  
Qiucheng Shi ◽  
Ping Zhang ◽  
Yan Chen ◽  
Rushuang Yan ◽  
...  
Keyword(s):  
Random Forest ◽  
Klebsiella Pneumoniae ◽  
Multilocus Sequence Typing ◽  
Operating Characteristic ◽  
Core Genome ◽  
Characteristic Curve ◽  
Random Forest Algorithm ◽  
The Core ◽  
Model Based ◽  
Operating Characteristic Curve

Abstract Background Hypervirulent Klebsiella pneumoniae (hvKP) infections can have high morbidity and mortality rates owing to their invasiveness and virulence. However, there are no effective tools or biomarkers to discriminate between hvKP and nonhypervirulent K. pneumoniae (nhvKP) strains. We aimed to use a random forest algorithm to predict hvKP based on core-genome data. Methods In total, 272 K. pneumoniae strains were collected from 20 tertiary hospitals in China and divided into hvKP and nhvKP groups according to clinical criteria. Clinical data comparisons, whole-genome sequencing, virulence profile analysis, and core genome multilocus sequence typing (cgMLST) were performed. We then established a random forest predictive model based on the cgMLST scheme to prospectively identify hvKP. The random forest is an ensemble learning method that generates multiple decision trees during the training process and each decision tree will output its own prediction results corresponding to the input. The predictive ability of the model was assessed by means of area under the receiver operating characteristic curve. Results Patients in the hvKP group were younger than those in the nhvKP group (median age, 58.0 and 68.0 years, respectively; P < .001). More patients in the hvKP group had underlying diabetes mellitus (43.1% vs 20.1%; P < .001). Clinically, carbapenem-resistant K. pneumoniae was less common in the hvKP group (4.1% vs 63.8%; P < .001), whereas the K1/K2 serotype, sequence type (ST) 23, and positive string tests were significantly higher in the hvKP group. A cgMLST-based minimal spanning tree revealed that hvKP strains were scattered sporadically within nhvKP clusters. ST23 showed greater genome diversification than did ST11, according to cgMLST-based allelic differences. Primary virulence factors (rmpA, iucA, positive string test result, and the presence of virulence plasmid pLVPK) were poor predictors of the hypervirulence phenotype. The random forest model based on the core genome allelic profile presented excellent predictive power, both in the training and validating sets (area under receiver operating characteristic curve, 0.987 and 0.999 in the training and validating sets, respectively). Conclusions A random forest algorithm predictive model based on the core genome allelic profiles of K. pneumoniae was accurate to identify the hypervirulent isolates.

scholarly journals A case-control clinical trial on the diagnostic performance for Alzheimer’s Disease of a deep learning-based classification system using brain magnetic resonance imaging

2021 ◽  
Author(s):  
Jong Bin Bae ◽  
Subin Lee ◽  
Hyunwoo Oh ◽  
Jinkyeong Sung ◽  
Dongsoo Lee ◽  
...  
Keyword(s):  
Predictive Value ◽  
Diagnostic Performance ◽  
Operating Characteristic ◽  
Control Clinical Trial ◽  
Characteristic Curve ◽  
Brain Mri ◽  
Case Control ◽  
Operating Characteristic Curve ◽  
Sensitivity Specificity ◽  
Normal Controls

Abstract Objective To investigate diagnostic performance of a deep learning-based classification system using structural brain MRI (DLCS) for Alzheimer’s disease (AD). Methods A single-center, case-control clinical trial was conducted. T1-weighted brain MRI scans of 188 patients with mild cognitive impairment or dementia due to AD and 162 cognitively normal controls were retrospectively collected. The patients were amyloid beta (Aβ)-positive, whereas the controls were Aβ-negative, on 18F-florbetaben positron emission tomography. Sensitivity, specificity, positive predictive value, negative predictive value, and area under the receiver operating characteristic curve were calculated to evaluate the performance of DLCS in the classification of Aβ-positive AD patients from Aβ-negative controls. Results The DLCS was excellent in classifying AD patients from normal controls; sensitivity, specificity, positive predictive value, negative predictive value, and area under the receiver operating characteristic curve for AD were 85.6% (95%CI, 79.8–90), 90.1% (95%CI, 84.5–94.2), 91.0% (95%CI, 86.3–94.1), 84.4% (95%CI, 79.2–88.5), and 0.937 (95%CI, 0.911–0.963), respectively. Conclusion The DLCS shows promise in clinical settings where it may improve early detection of AD in any individual who has undergone an MRI scan regardless of purpose. Trial registration: Korean Clinical Trials Registry, KCT0004758. Registered 21 February 2020, https://cris.nih.go.kr/cris/search/detailSearch.do/17665.

scholarly journals Identification of candidate biomarkers of liver hydatid disease via microarray profiling, bioinformatics analysis, and machine learning

2021 ◽  
Vol 49 (3) ◽  
pp. 030006052199398
Author(s):  
Jinwu Peng ◽  
Zhili Duan ◽  
Yamin Guo ◽  
Xiaona Li ◽  
Xiaoqin Luo ◽  
...  
Keyword(s):  
Random Forest ◽  
Hydatid Disease ◽  
Characteristic Curve ◽  
Receiver Operator Characteristic Curve ◽  
Random Forest Model ◽  
Hepatic Hydatid Disease ◽  
Forest Model ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Microarray Profiling

Objectives Liver echinococcosis is a severe zoonotic disease caused by Echinococcus (tapeworm) infection, which is epidemic in the Qinghai region of China. Here, we aimed to explore biomarkers and establish a predictive model for the diagnosis of liver echinococcosis. Methods Microarray profiling followed by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis was performed in liver tissue from patients with liver hydatid disease and from healthy controls from the Qinghai region of China. A protein–protein interaction (PPI) network and random forest model were established to identify potential biomarkers and predict the occurrence of liver echinococcosis, respectively. Results Microarray profiling identified 1152 differentially expressed genes (DEGs), including 936 upregulated genes and 216 downregulated genes. Several previously unreported biological processes and signaling pathways were identified. The FCGR2B and CTLA4 proteins were identified by the PPI networks and random forest model. The random forest model based on FCGR2B and CTLA4 reliably predicted the occurrence of liver hydatid disease, with an area under the receiver operator characteristic curve of 0.921. Conclusion Our findings give new insight into gene expression in patients with liver echinococcosis from the Qinghai region of China, improving our understanding of hepatic hydatid disease.

scholarly journals Predictive value of a novel lncRNA LINC02518 in evaluating the prognosis of patients with hepatocellular carcinoma

2021 ◽  
Author(s):  
Wei Cui ◽  
Jingzhi Huang ◽  
Ruiqi Wang ◽  
Yu Wang ◽  
Xiaoming Chen ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
Predictive Value ◽  
Long Noncoding Rna ◽  
Noncoding Rna ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Operating Characteristic Curve ◽  
Receiver Operating

Aim: The potential of long noncoding RNA in hepatocellular carcinoma (HCC) has led to promising insights into therapeutic intervention. The clinical significance of LINC02518 in HCC is unclear. This study aimed to evaluate the predictive value of a novel long noncoding RNA, LINC02518, for the prognosis of patients with HCC. Methods: Between December 2005 and November 2011, 125 and 75 HCC patients in the training and validation groups, respectively, who underwent liver surgery were included in our study. The LINC02518 expression of HCC and corresponding nontumor liver tissues was detected using microarray and reverse transcription quantitative polymerase chain reaction (RT-qPCR). These HCC patients were assigned into high and low LINC02518 expression groups based on the threshold of the receiver operating characteristic curve. Kaplan-Meier analysis was performed to determine the prognosis of HCC patients. Results: LINC02518 expression was upregulated in paired tumor samples compared with corresponding nontumor samples in the two groups. The area under the receiver operating characteristic curve for the levels of LINC02518 in the diagnosis of HCC was 0.66, 95% CI: 0.59–0.73. HCC patients with high LINC02518 expression had significantly worse tumor recurrence-free, metastasis-free, disease-free and overall survival than those with low LINC02518 expression. Conclusion: LINC02518 is negatively correlated with the prognosis of HCC and provides a promising strategy for the treatment and prognosis of HCC.

scholarly journals Clinical validation of urine 3-methoxytyramine as a biomarker of neuroblastoma and comparison with other catecholamine-related biomarkers

2016 ◽  
Vol 54 (2) ◽  
pp. 264-272 ◽  
Author(s):  
Leo Lam ◽  
Gerald A Woollard ◽  
Lochie Teague ◽  
James S Davidson
Keyword(s):  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
Diagnostic Performance ◽  
Homovanillic Acid ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Tumour Marker ◽  
Tumour Markers ◽  
Operating Characteristic Curve ◽  
Receiver Operating

Background Urinary dopamine, homovanillic acid and 4-hydroxy-3-methoxymandelic acid are established tests for diagnosis and monitoring of neuroblastic disease. We compared the diagnostic performance of total urinary 3-methoxytyramine, the O-methylated product of dopamine, to these three established tumour markers. Methods Urinary 3-methoxytyramine, dopamine, homovanillic acid and 4-hydroxy-3-methoxymandelic acid were measured by high-performance liquid chromatography with electrochemical detection on consecutive urine samples from histologically proven neuroblastic patients and controls. Patients with neuroblastic disease were further classified as untreated, advancing, residual or absent disease based on clinical and radiological criteria. Receiver operating characteristic curve analysis was used to compare the diagnostic performance of the four tumour markers. Results Urinary 3-methoxytyramine was well correlated with established tumour markers and its concentration correlated with disease activity. It was the most commonly elevated tumour marker in neuroblastic disease and showed similar sensitivity to dopamine and homovanillic acid. The diagnostic utility of urinary 3-methoxytyramine as measured by area under the receiver operating characteristic curve was similar to dopamine and homovanillic acid. Conclusion Our results support the use of urinary 3-methoxytyramine as a tumour marker in the diagnosis and the monitoring of neuroblastoma disease.

Prediction Rule for Scrub Typhus Meningoencephalitis in Children: Emerging Disease in North India

2020 ◽  
Vol 35 (12) ◽  
pp. 820-827
Author(s):  
Areesha Alam ◽  
Pranshi Agarwal ◽  
Jayanti Prabha ◽  
Amita Jain ◽  
Raj Kumar Kalyan ◽  
...  
Keyword(s):  
Cerebrospinal Fluid ◽  
Predictive Value ◽  
Operating Characteristic ◽  
Scrub Typhus ◽  
Characteristic Curve ◽  
Prediction Rule ◽  
Acute Encephalitis Syndrome ◽  
Acute Encephalitis ◽  
Operating Characteristic Curve ◽  
Cerebrospinal Fluid Protein

Objectives: To evaluate the proportion of scrub typhus meningoencephalitis among children with acute encephalitis syndrome and to outline its differentiating features. To develop a prediction rule for scrub typhus meningoencephalitis. Methods: A prospective cohort study was conducted at a tertiary care public hospital in Northern India. Consecutive patients of acute encephalitis syndrome who met our inclusion criteria were enrolled over 2 years. Standardized workup including serum IgM against Orientia tsutsugamushi was performed. Clinical and laboratory features were compared between IgM-positive and IgM-negative patients. The area under the receiver operating characteristic curve of the score derived from “independent predictors” was measured. Sensitivity, specificity, positive and negative predictive values, and positive and negative likelihood ratios were calculated at different cut-offs of the score. Results: Scrub typhus IgM enzyme-linked immunosorbent assay was positive in 66/352 patients (18.8%). Longer duration of fever and prodromal stage along with eschar, hepatomegaly, lymphadenopathy, and pneumonia were significantly more prevalent in scrub typhus meningoencephalitis. However, petechiae were frequent in non–scrub typhus patients. Leucocytosis, lymphocytosis, thrombocytopenia, hypoalbuminemia, and elevated levels of serum bilirubin, serum transaminases, and cerebrospinal fluid protein were associated with scrub typhus meningoencephalitis. Logistic regression revealed fever for >8 days, pneumonia, absence of petechiae, cerebrospinal fluid protein >1000 mg/L, and serum glutamic oxaloacetic transaminase >100 IU/L as independent “predictors” of scrub typhus meningoencephalitis. The area under the receiver operating characteristic curve (95% confidence interval) of the prediction score was 0.832 (0.78-0.89). Score at cutoff ≥1 had 91% sensitivity, 96.1% negative predictive value, and at cutoff ≥4 had 99.7% specificity, 88.9% positive predictive value, 83.1% negative predictive value, 40.3 positive likelihood ratio, 0.88 negative likelihood ratio for identifying scrub typhus meningoencephalitis. Conclusion: Prediction score may help physicians in peripheral areas to identify and treat scrub typhus meningoencephalitis, an emerging cause of acute encephalitis syndrome in India.

scholarly journals Usefulness of the 5MPB-Toledo model to predict bacteremia in patients with community-acquired pneumonia in the Emergency Department

2021 ◽  
Author(s):  
Agustín Julián-Jiménez ◽  
◽  
Juan González del Castillo ◽  
Eric Jorge García-Lamberechts ◽  
Rafael Rubio Díaz ◽  
...  
Keyword(s):  
Positive Predictive Value ◽  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
Predictive Value ◽  
Emergency Departments ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Community Acquired Pneumonia ◽  
Prognostic Performance ◽  
Operating Characteristic Curve

Objective. To analyse a new risk score to predict bacteremia in the patients with Community-acquired Pneumonia (CAP) in the emergency departments. Patients and methods. Prospective and multicenter observational cohort study of the blood cultures ordered in 74 Spanish emergency departments for patients with CAP seen from November 1, 2019, to March 31, 2020. The predictive ability of the model was analyzed with the area under the Receiver Operating Characteristic curve (AUC-ROC). The prognostic performance for true bacteremia was calculated with the chosen cut-off for getting the sensitivity, specificity, positive predictive value and negative predictive value. Results. A total of 1,020 blood samples wered cultured. True cases of bacteremia were confirmed in 162 (15.9%). The remaining 858 cultures (84.1%) wered negative. And, 59 (5.8%) were judged to be contaminated. The model´s area under the receiver operating characteristic curve was 0.915 (95% CI, 0.898-0.933). The prognostic performance with a model´s cut-off value of ≥ 5 points achieved 97.5% (95% CI, 95.1-99.9) sensitivity, 73.2% (95% CI, 70.2-76.2) specificity, 40.9% (95% CI, 36.4-45.1) positive predictive value and 99.4% (95% CI, 99.1-99.8) negative predictive value. Conclusion. The 5MPB-Toledo score is useful for predicting bacteremia in the patients with CAP seen in the emergency departments.

scholarly journals Laboratory and Clinical Performance of the ADVIA Centaur Anticyclic Citrullinated Peptide Assay for Rheumatoid Arthritis Diagnosis

2018 ◽  
Vol 142 (12) ◽  
pp. 1554-1559 ◽  
Author(s):  
Banseok Kim ◽  
Yongjung Park ◽  
Jin-Su Park ◽  
Kyoung Ja Jang ◽  
Hyo Jun Ahn ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
Diagnostic Performance ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Cutoff Values ◽  
Operating Characteristic Curve ◽  
Citrullinated Peptide ◽  
Receiver Operating

Context.— Anticyclic citrullinated peptide antibodies are important serologic markers for the diagnosis of rheumatoid arthritis. Several kinds of test reagents for automated immunoassay systems have been developed and used in recent years. Objective.— To evaluate the analytic and diagnostic performance of the new ADVIA Centaur anticyclic citrullinated peptide assay (Siemens Healthineers, Erlangen, Germany) compared with the Elecsys assay (Roche Diagnostics, Mannheim, Germany). Design.— A total of 576 serum samples were collected from subjects, including 156 patients (27%) with rheumatoid arthritis. Precision performance and analytical measurement range for the ADVIA assay were evaluated. Diagnostic performance of the 2 assays was compared based on sensitivity, specificity, and area under the receiver operating characteristic curves. Results.— The ADVIA assay showed a within-laboratory imprecision of 3.4% coefficient of variation for levels of 3.36 and 24.99 U/mL. This assay was demonstrated to be linear from 0.4 to 180.0 U/mL. With default cutoff values, sensitivity and specificity for diagnosing rheumatoid arthritis were 71.2% and 97.9%, respectively, for the ADVIA assay and 73.1% and 96.9%, respectively, for the Elecsys assay. With the best cutoff values from the analyses of the receiver operating characteristic curve, the sensitivity of the 2 assays was the same at 75.6%. However, the specificity of the ADVIA assay was 96.4%, whereas that of the Elecsys assay was 94.3%. The area under the receiver operating characteristic curve value for the ADVIA assay was 0.867, which was not significantly different from that of the Elecsys assay (0.865). Conclusions.— The ADVIA Centaur anticyclic citrullinated peptide assay showed good analytic and diagnostic performance in diagnosing rheumatoid arthritis.

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