Identification of Patient Profiles with High Risk of Hospital Re-Admissions for Acute COPD Exacerbations (AECOPD) in France Using a Machine Learning Model

Objective: We aimed to develop and validate a machine learning-based prediction model that could assess the risk of stroke-associated pneumonia (SAP) for individual patients with acute ischemic stroke (AIS). Methods: A machine-learning model incorporating A 2 DS 2 scores and clinical features (AN-ADCS 2 ) was developed to predict the risk of SAP in patients with AIS. Two independent datasets were used for model derivation and external validation. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) were estimated. The further analysis evaluated thresholds from the training set that identified patients as low-risk, intermediate-risk and high-risk, and performance at these thresholds was compared in the external validation set. Results: The AN-ADCS 2 model achieved favorable performance with a high AUC of 0.892 (95% confidence interval [CI] 0.885-0.898) in the test set and similar performance in the external validation set (AUC 0.813 [95% CI 0.812-0.814]). The AN-ADCS 2 threshold identifying low-risk was 0.03, with a NPV of 97.6% (97.2-97.9%) and sensitivity of 93.5% (92.5-94.5%). The AN-ADCS 2 threshold identifying high-risk was 0.65, with a PPV of 94.7% (93.9-95.6%) and specificity of 99.5% (99.5-99.6%). The AN-ADCS 2 model performed better than the A 2 DS 2 score (AUC 0.739, 95%CI [0.720-0.754]). Having a high risk of SAP classified by the AN-ADCS 2 was associated with unfavorable outcomes of mortality and in-hospital stroke recurrence. Conclusions: Using machine learning, the AN-ADCS 2 model provides an individualized risk prediction of SAP, which can be used as an indicator of clinical prognosis for patients with AIS.

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High-Risk Breast Lesions: A Machine Learning Model to Predict Pathologic Upgrade and Reduce Unnecessary Surgical Excision

Radiology ◽

10.1148/radiol.2017170549 ◽

2018 ◽

Vol 286 (3) ◽

pp. 810-818 ◽

Cited By ~ 49

Author(s):

Manisha Bahl ◽

Regina Barzilay ◽

Adam B. Yedidia ◽

Nicholas J. Locascio ◽

Lili Yu ◽

...

Keyword(s):

Machine Learning ◽

High Risk ◽

Surgical Excision ◽

Learning Model ◽

Breast Lesions ◽

Machine Learning Model ◽

High Risk Breast

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Risk Identification, Assessments, and Prediction for Mega Construction Projects: A Risk Prediction Paradigm Based on Cross Analytical-Machine Learning Model

Buildings ◽

10.3390/buildings11040172 ◽

2021 ◽

Vol 11 (4) ◽

pp. 172

Author(s):

Debalina Banerjee Chattapadhyay ◽

Jagadeesh Putta ◽

Rama Mohan Rao P

Keyword(s):

Machine Learning ◽

Risk Factors ◽

Risk Management ◽

High Risk ◽

Risk Prediction ◽

Learning Model ◽

Risk Identification ◽

Large Set ◽

High Risk Factors ◽

Machine Learning Model

Risk identification and management are the two most important parts of construction project management. Better risk management can help in determining the future consequences, but identifying possible risk factors has a direct and indirect impact on the risk management process. In this paper, a risk prediction system based on a cross analytical-machine learning model was developed for construction megaprojects. A total of 63 risk factors pertaining to the cost, time, quality, and scope of the megaproject and primary data were collected from industry experts on a five-point Likert scale. The obtained sample was further processed statistically to generate a significantly large set of features to perform K-means clustering based on high-risk factor and allied sub-risk component identification. Descriptive analysis, followed by the synthetic minority over-sampling technique (SMOTE) and the Wilcoxon rank-sum test was performed to retain the most significant features pertaining to cost, time, quality, and scope. Eventually, unlike classical K-means clustering, a genetic-algorithm-based K-means clustering algorithm (GA–K-means) was applied with dual-objective functions to segment high-risk factors and allied sub-risk components. The proposed model identified different high-risk factors and sub-risk factors, which cumulatively can impact overall performance. Thus, identifying these high-risk factors and corresponding sub-risk components can help stakeholders in achieving project success.

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Developing a Machine Learning Model to Predict Severe Chronic Obstructive Pulmonary Disease Exacerbations: Retrospective Cohort Study

Journal of Medical Internet Research ◽

10.2196/28953 ◽

2022 ◽

Vol 24 (1) ◽

pp. e28953

Author(s):

Siyang Zeng ◽

Mehrdad Arjomandi ◽

Yao Tong ◽

Zachary C Liao ◽

Gang Luo

Keyword(s):

Machine Learning ◽

Chronic Obstructive Pulmonary Disease ◽

High Risk ◽

Pulmonary Disease ◽

Care Management ◽

Chronic Obstructive ◽

Obstructive Pulmonary Disease ◽

Copd Exacerbations ◽

Machine Learning Model ◽

Severe Copd

Background Chronic obstructive pulmonary disease (COPD) poses a large burden on health care. Severe COPD exacerbations require emergency department visits or inpatient stays, often cause an irreversible decline in lung function and health status, and account for 90.3% of the total medical cost related to COPD. Many severe COPD exacerbations are deemed preventable with appropriate outpatient care. Current models for predicting severe COPD exacerbations lack accuracy, making it difficult to effectively target patients at high risk for preventive care management to reduce severe COPD exacerbations and improve outcomes. Objective The aim of this study is to develop a more accurate model to predict severe COPD exacerbations. Methods We examined all patients with COPD who visited the University of Washington Medicine facilities between 2011 and 2019 and identified 278 candidate features. By performing secondary analysis on 43,576 University of Washington Medicine data instances from 2011 to 2019, we created a machine learning model to predict severe COPD exacerbations in the next year for patients with COPD. Results The final model had an area under the receiver operating characteristic curve of 0.866. When using the top 9.99% (752/7529) of the patients with the largest predicted risk to set the cutoff threshold for binary classification, the model gained an accuracy of 90.33% (6801/7529), a sensitivity of 56.6% (103/182), and a specificity of 91.17% (6698/7347). Conclusions Our model provided a more accurate prediction of severe COPD exacerbations in the next year compared with prior published models. After further improvement of its performance measures (eg, by adding features extracted from clinical notes), our model could be used in a decision support tool to guide the identification of patients with COPD and at high risk for care management to improve outcomes. International Registered Report Identifier (IRRID) RR2-10.2196/13783

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A Machine Learning Model for the Identification of High risk Carotid Atherosclerotic Plaques

10.1109/embc46164.2021.9630654 ◽

2021 ◽

Author(s):

Vassiliki I. Kigka ◽

Antonis I. Sakellarios ◽

Michalis D. Mantzaris ◽

Vassilis D. Tsakanikas ◽

Vassiliki T. Potsika ◽

...

Keyword(s):

Machine Learning ◽

High Risk ◽

Learning Model ◽

Atherosclerotic Plaques ◽

Machine Learning Model

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CT imaging-based machine learning model: a potential modality for predicting low-risk and high-risk groups of thymoma: “Impact of surgical modality choice”

World Journal of Surgical Oncology ◽

10.1186/s12957-021-02259-6 ◽

2021 ◽

Vol 19 (1) ◽

Author(s):

Ayten Kayi Cangir ◽

Kaan Orhan ◽

Yusuf Kahya ◽

Hilal Özakıncı ◽

Betül Bahar Kazak ◽

...

Keyword(s):

Machine Learning ◽

High Risk ◽

Risk Groups ◽

Learning Model ◽

Low Risk ◽

Pathology Report ◽

Histopathological Diagnosis ◽

Gray Level ◽

Long Run ◽

Machine Learning Model

Abstract Introduction Radiomics methods are used to analyze various medical images, including computed tomography (CT), magnetic resonance, and positron emission tomography to provide information regarding the diagnosis, patient outcome, tumor phenotype, and the gene-protein signatures of various diseases. In low-risk group, complete surgical resection is typically sufficient, whereas in high-risk thymoma, adjuvant therapy is usually required. Therefore, it is important to distinguish between both. This study evaluated the CT radiomics features of thymomas to discriminate between low- and high-risk thymoma groups. Materials and methods In total, 83 patients with thymoma were included in this study between 2004 and 2019. We used the Radcloud platform (Huiying Medical Technology Co., Ltd.) to manage the imaging and clinical data and perform the radiomics statistical analysis. The training and validation datasets were separated by a random method with a ratio of 2:8 and 502 random seeds. The histopathological diagnosis was noted from the pathology report. Results Four machine-learning radiomics features were identified to differentiate a low-risk thymoma group from a high-risk thymoma group. The radiomics feature names were Energy, Zone Entropy, Long Run Low Gray Level Emphasis, and Large Dependence Low Gray Level Emphasis. Conclusions The results demonstrated that a machine-learning model and a multilayer perceptron classifier analysis can be used on CT images to predict low- and high-risk thymomas. This combination could be a useful preoperative method to determine the surgical approach for thymoma.

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CT Imaging-based Machine Learning Model: A Potential Modality for Predicting Low-Risk and High-Risk Groups of Thymoma “Impact of surgical modality choice”

10.21203/rs.3.rs-139108/v1 ◽

2021 ◽

Author(s):

Ayten KAYICANGIR ◽

Kaan ORHAN ◽

Yusuf KAHYA ◽

Hilal ÖZAKINCI ◽

Betül Bahar KAZAK ◽

...

Keyword(s):

Machine Learning ◽

High Risk ◽

Risk Groups ◽

Learning Model ◽

Low Risk ◽

Pathology Report ◽

Histopathological Diagnosis ◽

Gray Level ◽

Long Run ◽

Machine Learning Model

Abstract IntroductionRadiomics has become a hot issue in the medical imaging field, particularly in cancer imaging. Radiomics methods are used to analyze various medical images, including computed tomography (CT), magnetic resonance, and positron emission tomography to provide information regarding the diagnosis, patient outcome, tumor phenotype, and the gene-protein signatures of various diseases.This study evaluated the CT radiomics features of thymomas to discriminate between low- and high-risk thymoma groups.Materials and MethodsIn total, 83 patients with thymoma were included in this study between 2004 and 2019. We used the Radcloud platform (Huiying Medical Technology Co., Ltd.) to manage the imaging and clinical data and perform the radiomics statistical analysis. The training and validation datasets were separated by a random method with a ratio of 2:8 and 502 random seeds. The histopathological diagnosis was noted from the pathology report.ResultsFour machine learning radiomics features were identified to differentiate a low-risk thymoma group from a high-risk thymoma group. The radiomics feature names were Energy, Zone Entropy, Long Run Low Gray Level Emphasis, and Large Dependence Low Gray Level Emphasis.ConclusionsThe results demonstrated that a machine-learning model and a multilayer perceptron classifier analysis can be used on CT images to predict low- and high-risk thymomas. This combination could be a useful preoperative method to determine the surgical approach for thymoma.

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SURFACE ECG-BASED MACHINE LEARNING MODEL FOR PREDICTING PATIENT SUBGROUP AT A HIGH RISK FOR MAJOR ADVERSE CARDIAC EVENTS

Journal of the American College of Cardiology ◽

10.1016/s0735-1097(21)04582-4 ◽

2021 ◽

Vol 77 (18) ◽

pp. 3227

Author(s):

Heenaben Patel ◽

Naveena Yanamala ◽

Marton Tokodi ◽

Nobuyaki Kagiyama ◽

Marco Piccirilli ◽

...

Keyword(s):

Machine Learning ◽

High Risk ◽

Learning Model ◽

Major Adverse Cardiac Events ◽

Cardiac Events ◽

Patient Subgroup ◽

Machine Learning Model ◽

Adverse Cardiac Events ◽

Surface Ecg

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Developing a Machine Learning Model to Predict Severe Chronic Obstructive Pulmonary Disease Exacerbations: Retrospective Cohort Study (Preprint)

10.2196/preprints.28953 ◽

2021 ◽

Author(s):

Siyang Zeng ◽

Mehrdad Arjomandi ◽

Yao Tong ◽

Zachary C Liao ◽

Gang Luo

Keyword(s):

Machine Learning ◽

Chronic Obstructive Pulmonary Disease ◽

High Risk ◽

Pulmonary Disease ◽

Care Management ◽

Chronic Obstructive ◽

Obstructive Pulmonary Disease ◽

Copd Exacerbations ◽

Machine Learning Model ◽

Severe Copd

BACKGROUND Chronic obstructive pulmonary disease (COPD) poses a large burden on health care. Severe COPD exacerbations require emergency department visits or inpatient stays, often cause an irreversible decline in lung function and health status, and account for 90.3% of the total medical cost related to COPD. Many severe COPD exacerbations are deemed preventable with appropriate outpatient care. Current models for predicting severe COPD exacerbations lack accuracy, making it difficult to effectively target patients at high risk for preventive care management to reduce severe COPD exacerbations and improve outcomes. OBJECTIVE The aim of this study is to develop a more accurate model to predict severe COPD exacerbations. METHODS We examined all patients with COPD who visited the University of Washington Medicine facilities between 2011 and 2019 and identified 278 candidate features. By performing secondary analysis on 43,576 University of Washington Medicine data instances from 2011 to 2019, we created a machine learning model to predict severe COPD exacerbations in the next year for patients with COPD. RESULTS The final model had an area under the receiver operating characteristic curve of 0.866. When using the top 9.99% (752/7529) of the patients with the largest predicted risk to set the cutoff threshold for binary classification, the model gained an accuracy of 90.33% (6801/7529), a sensitivity of 56.6% (103/182), and a specificity of 91.17% (6698/7347). CONCLUSIONS Our model provided a more accurate prediction of severe COPD exacerbations in the next year compared with prior published models. After further improvement of its performance measures (eg, by adding features extracted from clinical notes), our model could be used in a decision support tool to guide the identification of patients with COPD and at high risk for care management to improve outcomes. INTERNATIONAL REGISTERED REPORT RR2-10.2196/13783

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