Correction to: Early Colorectal Cancer Detected by Machine Learning Model Using Gender, Age, and Complete Blood Count Data

e15090 Background: Aberrant hyperactive Wnt/ß-catenin signaling is critical in colorectal cancer (CRC) tumorigenesis. Casitas B-lineage Lymphoma (c-Cbl) is a negative regulator of Wnt signaling, and functions as a tumor suppressor. The objective of this study was to evaluate c-Cbl expression as a predictive marker of survival in patients with metastatic CRC (mCRC). Methods: Patients with mCRC treated at Boston University Medical Center between 2004 and 2014 were analyzed. c-Cbl and nuclear ß-catenin expression was quantified in explanted biopsies using a customized color-based image segmentation pipeline. Quantification was normalized to the total tumor area in an image, and deemed ‘low’ or ‘high’ according to the mean normalized values of the cohort. A supervised machine-learning model based on bootstrap aggregating was constructed with c-Cbl expression as the input feature and 3-year survival as output. Results: Of the 72 subjects with mCRC, 52.78% had high and 47.22% had low c-Cbl expression. Patients with high c-Cbl had significantly better median overall survival than those with low c-Cbl expression (3.7 years vs. 1.8 years; p = 0.0026), and experienced superior 3-year survival (47.37% vs 20.59%; p = 0.017). Intriguingly, nuclear ß-catenin expression did not correlate with survival. No significant differences were detected between high and low c-Cbl groups in baseline characteristics (demographics, comorbidities), tumor-related parameters (primary tumor location, number of metastasis, molecular features) or therapy received (surgery, chemotherapy regimen). A 5-fold cross-validated machine-learning model associated with 3-year survival demonstrated an area under the curve of 0.729, supporting c-Cbl expression as a predictor of mCRC survival. Conclusions: Our results show that c-Cbl expression is associated with and predicts mCRC survival. Demonstration of these findings despite the small cohort size underscores the power of quantitative histology and machine-learning application. While further work is needed to validate c-Cbl as a novel biomarker of mCRC survival, this study supports c-Cbl as a regulator of Wnt/ß-catenin signaling and a suppressor of other oncogenes in CRC tumorigenesis.

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A Physician-in-the-Loop Approach by Means of Machine Learning for the Diagnosis of Lymphocytosis in the Clinical Laboratory

Archives of Pathology & Laboratory Medicine ◽

10.5858/arpa.2021-0044-oa ◽

2021 ◽

Author(s):

Laura Bigorra ◽

Iciar Larriba ◽

Ricardo Gutiérrez-Gallego

Keyword(s):

Machine Learning ◽

Clinical Laboratory ◽

Complete Blood Count ◽

Learning Model ◽

Cost Ratio ◽

Proof Of Concept ◽

Data Set ◽

Diagnostic Impact ◽

Diagnostic Categories ◽

Machine Learning Model

Context.— The goal of the lymphocytosis diagnosis approach is its classification into benign or neoplastic categories. Nevertheless, a nonnegligible percentage of laboratories fail in that classification. Objective.— To design and develop a machine learning model by using objective data from the DxH 800 analyzer, including cell population data, leukocyte and absolute lymphoid counts, hemoglobin concentration, and platelet counts, besides age and sex, with classification purposes for lymphocytosis diagnosis. Design.— A total of 1565 samples were included from 10 different lymphoid categories grouped into 4 diagnostic categories: normal controls (458), benign causes of lymphocytosis (567), neoplastic lymphocytosis (399), and spurious causes of lymphocytosis (141). The data set was distributed in a 60-20-20 scheme for training, testing, and validation stages. Six machine learning models were built and compared, and the selection of the final model was based on the minimum generalization error and 10-fold cross validation accuracy. Results.— The selected neural network classifier rendered a global 10-class classification validation accuracy corresponding to 89.9%, which, considering the aforementioned 4 diagnostic categories, presented a diagnostic impact accuracy corresponding to 95.8%. Finally, a prospective proof of concept was performed with 100 new cases with a global diagnostic accuracy corresponding to 91%. Conclusions.— The proposed machine learning model was feasible, with a high benefit-cost ratio, as the results were obtained within the complete blood count with differential. Finally, the diagnostic impact with high accuracies in both model validation and proof of concept encourages exploration of the model for real-world application on a daily basis.

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Machine Learning Model for Predicting Postoperative Survival of Patients with Colorectal Cancer

Cancer Research and Treatment ◽

10.4143/crt.2021.206 ◽

2021 ◽

Author(s):

Mohamed Hosny Osman ◽

Reham Hosny Mohamed ◽

Hossam Mohamed Sarhan ◽

Eun Jung Park ◽

Seung Hyuk Baik ◽

...

Keyword(s):

Colorectal Cancer ◽

Machine Learning ◽

Learning Model ◽

Postoperative Survival ◽

Machine Learning Model

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Design of Machine Learning Model for Urban Planning and Management Improvement

International Journal of Performability Engineering ◽

10.23940/ijpe.20.06.p14.958967 ◽

2020 ◽

Vol 16 (6) ◽

pp. 958 ◽

Cited By ~ 1

Author(s):

Zhou Jiafeng ◽

Liu Tian ◽

Zou Lin

Keyword(s):

Machine Learning ◽

Urban Planning ◽

Learning Model ◽

Planning And Management ◽

Machine Learning Model ◽

Urban Planning And Management ◽

Management Improvement

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A Novel Machine Learning Model for Early Operational Anomaly Detection Using LWD/MWD Data

10.2523/iptc-19230-ms ◽

2019 ◽

Author(s):

Mohammed Al-Ghazal ◽

Viranchi Vedpathak

Keyword(s):

Machine Learning ◽

Anomaly Detection ◽

Learning Model ◽

Machine Learning Model

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Machine Learning Accelerated Genetic Algorithms for Computational Materials Search

10.26434/chemrxiv.7411172 ◽

2018 ◽

Author(s):

Steen Lysgaard ◽

Paul C. Jennings ◽

Jens Strabo Hummelshøj ◽

Thomas Bligaard ◽

Tejs Vegge

Keyword(s):

Machine Learning ◽

Genetic Algorithm ◽

Genetic Algorithms ◽

Au Nanoparticles ◽

Learning Model ◽

Energy Calculations ◽

Atomic Distribution ◽

Machine Learning Model ◽

Fold Reduction ◽

Computational Materials

A machine learning model is used as a surrogate fitness evaluator in a genetic algorithm (GA) optimization of the atomic distribution of Pt-Au nanoparticles. The machine learning accelerated genetic algorithm (MLaGA) yields a 50-fold reduction of required energy calculations compared to a traditional GA.

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AN EFFICIENT MACHINE LEARNING MODEL FOR PREDICTION OF ACUTE MYOCARDIAL INFARCTION

Recent Advances in Computer Science and Communications ◽

10.2174/2666255813666200325104317 ◽

2020 ◽

Vol 13 ◽

Author(s):

Dhilsath Fathima.M ◽

S. Justin Samuel ◽

R. Hari Haran

Keyword(s):

Machine Learning ◽

Myocardial Infarction ◽

Acute Myocardial Infarction ◽

Logistic Regression ◽

Decision Tree ◽

Learning Model ◽

Training Dataset ◽

Data Set ◽

Machine Learning Model ◽

Proposed Model

Aim: This proposed work is used to develop an improved and robust machine learning model for predicting Myocardial Infarction (MI) could have substantial clinical impact. Objectives: This paper explains how to build machine learning based computer-aided analysis system for an early and accurate prediction of Myocardial Infarction (MI) which utilizes framingham heart study dataset for validation and evaluation. This proposed computer-aided analysis model will support medical professionals to predict myocardial infarction proficiently. Methods: The proposed model utilize the mean imputation to remove the missing values from the data set, then applied principal component analysis to extract the optimal features from the data set to enhance the performance of the classifiers. After PCA, the reduced features are partitioned into training dataset and testing dataset where 70% of the training dataset are given as an input to the four well-liked classifiers as support vector machine, k-nearest neighbor, logistic regression and decision tree to train the classifiers and 30% of test dataset is used to evaluate an output of machine learning model using performance metrics as confusion matrix, classifier accuracy, precision, sensitivity, F1-score, AUC-ROC curve. Results: Output of the classifiers are evaluated using performance measures and we observed that logistic regression provides high accuracy than K-NN, SVM, decision tree classifiers and PCA performs sound as a good feature extraction method to enhance the performance of proposed model. From these analyses, we conclude that logistic regression having good mean accuracy level and standard deviation accuracy compared with the other three algorithms. AUC-ROC curve of the proposed classifiers is analyzed from the output figure.4, figure.5 that logistic regression exhibits good AUC-ROC score, i.e. around 70% compared to k-NN and decision tree algorithm. Conclusion: From the result analysis, we infer that this proposed machine learning model will act as an optimal decision making system to predict the acute myocardial infarction at an early stage than an existing machine learning based prediction models and it is capable to predict the presence of an acute myocardial Infarction with human using the heart disease risk factors, in order to decide when to start lifestyle modification and medical treatment to prevent the heart disease.

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Smart-ML: A System for Machine Learning Model Exploration using Pipeline Graph

2020 IEEE International Conference on Big Data (Big Data) ◽

10.1109/bigdata50022.2020.9378082 ◽

2020 ◽

Author(s):

Dhaval Patel ◽

Shrey Shrivastava ◽

Wesley Gifford ◽

Stuart Siegel ◽

Jayant Kalagnanam ◽

...

Keyword(s):

Machine Learning ◽

Learning Model ◽

Machine Learning Model

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