scholarly journals Machine-Learning Based Early Warning System for Prediction for Disseminated Intravascular Coagulation after Allogeneic Hematopoietic Stem Cell Transplantation: A Nationwide Multicenter Study

Blood ◽  
2021 ◽  
Vol 138 (Supplement 1) ◽  
pp. 2113-2113
Author(s):  
Zhuo-Yu An ◽  
Ye-Jun Wu ◽  
Yun He ◽  
Xiao-Lu Zhu ◽  
Yan Su ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Stem Cell ◽  
Validation Cohort ◽  
Lasso Regression ◽  
Learning Models ◽  
Good Discrimination ◽  
Peking University ◽  
Machine Learning Models ◽  
Good Calibration

Abstract Introduction Allogeneic haematopoietic stem cell transplantation (allo-HSCT) has been demonstrated to be the most effective therapy for various malignant as well as nonmalignant haematological diseases. The wide use of allo-HSCT has inevitably led to a variety of complications after transplantation, with bleeding complications such as disseminated intravascular coagulation (DIC). DIC accounts for a significant proportion of life-threatening bleeding cases occurring after allo-HSCT. However, information on markers for early identification remains limited, and no predictive tools for DIC after allo-HSCT are available. This research aimed to identify the risk factors for DIC after allo-HSCT and establish prediction models to predict the occurrence of DIC after allo-HSCT. Methods The definition of DIC was based on the International Society of Thrombosis and Hemostasis (ISTH) scoring system. Overall, 197 patients with DIC after allo-HSCT at Peking University People's Hospital and other 7 centers in China from January 2010 to June 2021 were retrospectively identified. Each patient was randomly matched to 3 controls based on the time of allo-HSCT (±3 months) and length of follow-up (±6 months). A lasso regression model was used for data dimension reduction, feature selection, and risk factor building. Multivariable logistic regression analysis was used to develop the prediction model. We incorporated the clinical risk factors, and this was presented with a nomogram. The performance of the nomogram was assessed with respect to its calibration, discrimination, and clinical usefulness. Internal and external validation was assessed. Various machine learning models were further used to perform machine learning modeling by attempting to complete the data sample classification task, including XGBClassifier, LogisticRegression, MLPClassifier, RandomForestClassifier, and AdaBoostClassifier. Results A total of 7280 patients received allo-HSCT from January 2010 to June 2021, and DIC occurred in 197 of these patients (incidence of 2.7%). The derivation cohort included 120 DIC patients received allo-HSCT and 360 patients received allo-HSCT from Peking University People's Hospital, and the validation cohort included the remaining 77 patients received allo-HSCT and 231 patients received allo-HSCT from the other 7 centers. The median time for DIC events was 99.0 (IQR, 46.8-220) days after allo-HSCT. The overall survival of patients with DIC was significantly reduced (P ＜ 0.0001). By Lasso regression, the 10 variables with the highest importance were found to be prothrombin time activity (PTA), shock, C-reactive protein, internationalization normalized ratio, bacterial infection, oxygenation, fibrinogen, blood creatinine, white blood cell count, and acute respiratory distress syndrome (from highest to lowest). In the multivariate analysis, the independent risk factors for DIC included PTA, bacterial infection and shock (P <0.001), and these predictors were included in the clinical prediction nomogram. The model showed good discrimination, with a C-index of 0.975 (95%CI, 0.939 to 0.987 through internal validation) and good calibration. Application of the nomogram in the validation cohort still gave good discrimination (C-index, 0.778 [95% CI, 0.759 to 0.766]) and good calibration. Decision curve analysis demonstrated that the nomogram was clinically useful. The predictive value ROC curves of different machine learning models show that XGBClassifier is the best performing model for this dataset, with an area under the curve of 0.86. Conclusions Risk factors for DIC after allo-HSCT were identified, and a nomogram model and various machine learning models were established to predict the occurrence of DIC after allo-HSCT. Combined, these can help recognize high-risk patients and provide timely treatment. In the future, we will further refine the prognostic model utilizing nationwide multicenter data and conduct prospective clinical trials to reduce the incidence of DIC after allo-HSCT and improve the prognosis. Disclosures No relevant conflicts of interest to declare.

scholarly journals Development and validation of a prognostic COVID-19 severity assessment (COSA) score and machine learning models for patient triage at a tertiary hospital

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Verena Schöning ◽  
Evangelia Liakoni ◽  
Christine Baumgartner ◽  
Aristomenis K. Exadaktylos ◽  
Wolf E. Hautz ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Stratification ◽  
Clinical Outcomes ◽  
Predictive Value ◽  
Validation Cohort ◽  
Tertiary Hospital ◽  
Learning Models ◽  
Clinical Risk ◽  
Machine Learning Models ◽  
Risk Stratification Score

Abstract Background Clinical risk scores and machine learning models based on routine laboratory values could assist in automated early identification of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) patients at risk for severe clinical outcomes. They can guide patient triage, inform allocation of health care resources, and contribute to the improvement of clinical outcomes. Methods In- and out-patients tested positive for SARS-CoV-2 at the Insel Hospital Group Bern, Switzerland, between February 1st and August 31st (‘first wave’, n = 198) and September 1st through November 16th 2020 (‘second wave’, n = 459) were used as training and prospective validation cohort, respectively. A clinical risk stratification score and machine learning (ML) models were developed using demographic data, medical history, and laboratory values taken up to 3 days before, or 1 day after, positive testing to predict severe outcomes of hospitalization (a composite endpoint of admission to intensive care, or death from any cause). Test accuracy was assessed using the area under the receiver operating characteristic curve (AUROC). Results Sex, C-reactive protein, sodium, hemoglobin, glomerular filtration rate, glucose, and leucocytes around the time of first positive testing (− 3 to + 1 days) were the most predictive parameters. AUROC of the risk stratification score on training data (AUROC = 0.94, positive predictive value (PPV) = 0.97, negative predictive value (NPV) = 0.80) were comparable to the prospective validation cohort (AUROC = 0.85, PPV = 0.91, NPV = 0.81). The most successful ML algorithm with respect to AUROC was support vector machines (median = 0.96, interquartile range = 0.85–0.99, PPV = 0.90, NPV = 0.58). Conclusion With a small set of easily obtainable parameters, both the clinical risk stratification score and the ML models were predictive for severe outcomes at our tertiary hospital center, and performed well in prospective validation.

scholarly journals Influence of social determinants of health and county vaccination rates on machine learning models to predict COVID-19 case growth in Tennessee

2021 ◽  
Vol 28 (1) ◽  
pp. e100439
Author(s):  
Lukasz S Wylezinski ◽  
Coleman R Harris ◽  
Cody N Heiser ◽  
Jamieson D Gray ◽  
Charles F Spurlock
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Social Determinants Of Health ◽  
Social Determinants ◽  
Determinants Of Health ◽  
Learning Models ◽  
Vaccination Rates ◽  
Data Framework ◽  
The Impact ◽  
Machine Learning Models

IntroductionThe SARS-CoV-2 (COVID-19) pandemic has exposed health disparities throughout the USA, particularly among racial and ethnic minorities. As a result, there is a need for data-driven approaches to pinpoint the unique constellation of clinical and social determinants of health (SDOH) risk factors that give rise to poor patient outcomes following infection in US communities.MethodsWe combined county-level COVID-19 testing data, COVID-19 vaccination rates and SDOH information in Tennessee. Between February and May 2021, we trained machine learning models on a semimonthly basis using these datasets to predict COVID-19 incidence in Tennessee counties. We then analyzed SDOH data features at each time point to rank the impact of each feature on model performance.ResultsOur results indicate that COVID-19 vaccination rates play a crucial role in determining future COVID-19 disease risk. Beginning in mid-March 2021, higher vaccination rates significantly correlated with lower COVID-19 case growth predictions. Further, as the relative importance of COVID-19 vaccination data features grew, demographic SDOH features such as age, race and ethnicity decreased while the impact of socioeconomic and environmental factors, including access to healthcare and transportation, increased.ConclusionIncorporating a data framework to track the evolving patterns of community-level SDOH risk factors could provide policy-makers with additional data resources to improve health equity and resilience to future public health emergencies.

scholarly journals Machine Learning Algorithm Using Electronic Chart-Derived Data to Predict Delirium After Elderly Hip Fracture Surgeries: A Retrospective Case-Control Study

2021 ◽  
Vol 8 ◽  
Author(s):  
Hong Zhao ◽  
Jiaming You ◽  
Yuexing Peng ◽  
Yi Feng
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Hip Fracture ◽  
Elderly Patients ◽  
Postoperative Delirium ◽  
Frailty Index ◽  
Fracture Repair ◽  
Learning Models ◽  
Electronic Chart ◽  
Machine Learning Models

Background: Elderly patients undergoing hip fracture repair surgery are at increased risk of delirium due to aging, comorbidities, and frailty. But current methods for identifying the high risk of delirium among hospitalized patients have moderate accuracy and require extra questionnaires. Artificial intelligence makes it possible to establish machine learning models that predict incident delirium risk based on electronic health data.Methods: We conducted a retrospective case-control study on elderly patients (≥65 years of age) who received orthopedic repair with hip fracture under spinal or general anesthesia between June 1, 2018, and May 31, 2019. Anesthesia records and medical charts were reviewed to collect demographic, surgical, anesthetic features, and frailty index to explore potential risk factors for postoperative delirium. Delirium was assessed by trained nurses using the Confusion Assessment Method (CAM) every 12 h during the hospital stay. Four machine learning risk models were constructed to predict the incidence of postoperative delirium: random forest, eXtreme Gradient Boosting (XGBoosting), support vector machine (SVM), and multilayer perception (MLP). K-fold cross-validation was deployed to accomplish internal validation and performance evaluation.Results: About 245 patients were included and postoperative delirium affected 12.2% (30/245) of the patients. Multiple logistic regression revealed that dementia/history of stroke [OR 3.063, 95% CI (1.231, 7.624)], blood transfusion [OR 2.631, 95% CI (1.055, 6.559)], and preparation time [OR 1.476, 95% CI (1.170, 1.862)] were associated with postoperative delirium, achieving an area under receiver operating curve (AUC) of 0.779, 95% CI (0.703, 0.856).The accuracy of machine learning models for predicting the occurrence of postoperative delirium ranged from 83.67 to 87.75%. Machine learning methods detected 16 risk factors contributing to the development of delirium. Preparation time, frailty index uses of vasopressors during the surgery, dementia/history of stroke, duration of surgery, and anesthesia were the six most important risk factors of delirium.Conclusion: Electronic chart-derived machine learning models could generate hospital-specific delirium prediction models and calculate the contribution of risk factors to the occurrence of delirium. Further research is needed to evaluate the significance and applicability of electronic chart-derived machine learning models for the detection risk of delirium in elderly patients undergoing hip fracture repair surgeries.

scholarly journals Influence of social determinants of health and county vaccination rates on machine learning models to predict COVID-19 case growth in Tennessee

2021 ◽  
Author(s):  
Lukasz S Wylezinski ◽  
Coleman R Harris ◽  
Cody N Heiser ◽  
Jamieson D Gray ◽  
Charles F Spurlock
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Social Determinants Of Health ◽  
Social Determinants ◽  
The United States ◽  
Determinants Of Health ◽  
Learning Models ◽  
Vaccination Rates ◽  
The Impact ◽  
Machine Learning Models

The SARS-CoV-2 (COVID-19) pandemic has exposed health disparities throughout the United States, particularly among racial and ethnic minorities. As a result, there is a need for data-driven approaches to pinpoint the unique constellation of clinical and social determinants of health (SDOH) risk factors that give rise to poor patient outcomes following infection in US communities. We combined county-level COVID-19 testing data, COVID-19 vaccination rates, and SDOH information in Tennessee. Between February-May 2021, we trained machine learning models on a semi-monthly basis using these datasets to predict COVID-19 incidence in Tennessee counties. We then analyzed SDOH data features at each time point to rank the impact of each feature on model performance. Our results indicate that COVID-19 vaccination rates play a crucial role in determining future COVID-19 disease risk. Beginning in mid-March 2021, higher vaccination rates significantly correlated with lower COVID-19 case growth predictions. Further, as the relative importance of COVID-19 vaccination data features grew, demographic SDOH features such as age, race, and ethnicity decreased while the impact of socioeconomic and environmental factors, including access to healthcare and transportation, increased. Incorporating a data framework to track the evolving patterns of community-level SDOH risk factors could provide policymakers with additional data resources to improve health equity and resilience to future public health emergencies.

scholarly journals Machine Learning Models of Hemorrhage/Ischemia in Moyamoya Disease and Analysis of Its Risk Factors

2021 ◽  
Author(s):  
Zhongjun Chen ◽  
Haowen Luo ◽  
Lijun Xu
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Moyamoya Disease ◽  
Stroke Recurrence ◽  
Learning Models ◽  
Prediction Ability ◽  
Significant Difference ◽  
Medical Big Data ◽  
Mlp Model ◽  
Machine Learning Models

Abstract Object: Identify the risk factors for hemorrhage/ischemia in patients with moyamoya disease and establish models using Logistic regression (LR), XGboost and Multilayer Perceptron (MLP), evaluating and comparison the effects of those models; providing theoretical basis for moyamoya disease patients to prevent stroke recurrence. Methods: This retrospective study used data from the database of Jiang Xi Province Medical Big Data Engineering & Technology Research Center; the data of patients with moyamoya disease admitted to the second affiliated hospital of Nanchang university from January 1, 2012 to December 31, 2019 were collected. A total of 994 patients with moyamoya disease were screened, including 496 patients with cerebral infarction and 498 patients with cerebral hemorrhage. LR, XGboost and MLP were used to establish models for hemorrhage /ischemia in moyamoya disease, the effects of different models were verified and compared. Result: LR, XGboost and MLP models all had good discrimination (AUC>0.75), and their AUC value are 0.9227（95%CI:0.9215-0.9239）、0.9677(95%CI:0.9657-0.9696)、 0.9672（95%CI：0.9643-0.9701）. Compared with LR model, the prediction ability of XGboost and MLP model in training and test set is improved, which is increased by 18.11% and 14.34% respectively in training set, and there is a significant difference. Conclusion: Compared with the traditional LR model, the machine learning models are more effective in predicting hemorrhage/ischemia in moyamoya disease.

Development and Validation of a Radiomics Nomogram for Preoperative Prediction of Lymph Node Metastasis in Colorectal Cancer

2016 ◽  
Vol 34 (18) ◽  
pp. 2157-2164 ◽  
Author(s):  
Yan-qi Huang ◽  
Chang-hong Liang ◽  
Lan He ◽  
Jie Tian ◽  
Cui-shan Liang ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Risk Factors ◽  
Lymph Node ◽  
Validation Cohort ◽  
Good Discrimination ◽  
Internal Validation ◽  
Preoperative Prediction ◽  
Individualized Prediction ◽  
Radiomics Signature ◽  
Good Calibration

Purpose To develop and validate a radiomics nomogram for preoperative prediction of lymph node (LN) metastasis in patients with colorectal cancer (CRC). Patients and Methods The prediction model was developed in a primary cohort that consisted of 326 patients with clinicopathologically confirmed CRC, and data was gathered from January 2007 to April 2010. Radiomic features were extracted from portal venous–phase computed tomography (CT) of CRC. Lasso regression model was used for data dimension reduction, feature selection, and radiomics signature building. Multivariable logistic regression analysis was used to develop the predicting model, we incorporated the radiomics signature, CT-reported LN status, and independent clinicopathologic risk factors, and this was presented with a radiomics nomogram. The performance of the nomogram was assessed with respect to its calibration, discrimination, and clinical usefulness. Internal validation was assessed. An independent validation cohort contained 200 consecutive patients from May 2010 to December 2011. Results The radiomics signature, which consisted of 24 selected features, was significantly associated with LN status (P < .001 for both primary and validation cohorts). Predictors contained in the individualized prediction nomogram included the radiomics signature, CT-reported LN status, and carcinoembryonic antigen level. Addition of histologic grade to the nomogram failed to show incremental prognostic value. The model showed good discrimination, with a C-index of 0.736 (C-index, 0.759 and 0.766 through internal validation), and good calibration. Application of the nomogram in the validation cohort still gave good discrimination (C-index, 0.778 [95% CI, 0.769 to 0.787]) and good calibration. Decision curve analysis demonstrated that the radiomics nomogram was clinically useful. Conclusion This study presents a radiomics nomogram that incorporates the radiomics signature, CT-reported LN status, and clinical risk factors, which can be conveniently used to facilitate the preoperative individualized prediction of LN metastasis in patients with CRC.

scholarly journals Analysis of main risk factors causing stroke in Shanxi Province based on machine learning models

2021 ◽  
pp. 100712
Author(s):  
Junjie Liu ◽  
Yiyang Sun ◽  
Jing Ma ◽  
Jiachen Tu ◽  
Yuhui Deng ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Shanxi Province ◽  
Learning Models ◽  
Machine Learning Models

scholarly journals A Modified Drake Equation for Assessing Adversarial Risk to Machine Learning Models

2021 ◽  
Author(s):  
Josh Kalin ◽  
David Noever ◽  
Matthew Ciolino ◽  
Gerry Dozier
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Potential Risk ◽  
Learning Models ◽  
Contributing Factors ◽  
Empirical Measures ◽  
Research Fields ◽  
Potential Risk Factors ◽  
Adversarial Attack ◽  
Machine Learning Models

Machine learning models present a risk of adversarial attack when deployed in production. Quantifying the contributing factors and uncertainties using empirical measures could assist the industry with assessing the risk of downloading and deploying common model types. This work proposes modifying the traditional Drake Equation’s formalism to estimate the number of potentially successful adversarial attacks on a deployed model. The Drake Equation is famously used for parameterizing uncertainties and it has been used in many research fields outside of its original intentions to estimate the number of radio-capable extra-terrestrial civilizations. While previous work has outlined methods for discovering vulnerabilities in public model architectures, the proposed equation seeks to provide a semi-quantitative benchmark for evaluating and estimating the potential risk factors for adversarial attacks.

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