Prediction of Maternal Hemorrhage: Using Machine Learning to Identify Patients at Risk (Preprint)

2021 ◽  
Author(s):  
Jill M Westcott ◽  
Francine Hughes ◽  
Wenke Liu ◽  
Mark Grivainis ◽  
Iffath Hoskins ◽  
...  
Keyword(s):  
Machine Learning ◽  
At Risk ◽  
Postpartum Hemorrhage ◽  
Mode Of Delivery ◽  
Receiver Operating Curve ◽  
Stage Model ◽  
Learning Methods ◽  
Second Stage ◽  
Machine Learning Methods ◽  
Patients At Risk

BACKGROUND Postpartum hemorrhage remains one of the largest causes of maternal morbidity and mortality in the United States. OBJECTIVE To utilize machine learning techniques to identify patients at risk for postpartum hemorrhage at obstetric delivery. METHODS Women aged 18 to 55 delivering at a major academic center from July 2013 to October 2018 were included for analysis (n = 30,867). A total of 497 variables were collected from the electronic medical record including demographic information, obstetric, medical, surgical, and family history, vital signs, laboratory results, labor medication exposures, and delivery outcomes. Postpartum hemorrhage was defined as a blood loss of ≥ 1000 mL at the time of delivery, regardless of delivery method, with 2179 positive cases observed (7.06%). Supervised learning with regression-, tree-, and kernel-based machine learning methods was used to create classification models based upon training (n = 21,606) and validation (n = 4,630) cohorts. Models were tuned using feature selection algorithms and domain knowledge. An independent test cohort (n = 4,631) determined final performance by assessing for accuracy, area under the receiver operating curve (AUC), and sensitivity for proper classification of postpartum hemorrhage. Separate models were created using all collected data versus limited to data available prior to the second stage of labor/at the time of decision to proceed with cesarean delivery. Additional models examined patients by mode of delivery. RESULTS Gradient boosted decision trees achieved the best discrimination in the overall model. The model including all data mildly outperformed the second stage model (AUC 0.979, 95% CI 0.971-0.986 vs. AUC 0.955, 95% CI 0.939-0.970). Optimal model accuracy was 98.1% with a sensitivity of 0.763 for positive prediction of postpartum hemorrhage. The second stage model achieved an accuracy of 98.0% with a sensitivity of 0.737. Other selected algorithms returned models that performed with decreased discrimination. Models stratified by mode of delivery achieved good to excellent discrimination, but lacked sensitivity necessary for clinical applicability. CONCLUSIONS Machine learning methods can be used to identify women at risk for postpartum hemorrhage who may benefit from individualized preventative measures. Models limited to data available prior to delivery perform nearly as well as those with more complete datasets, supporting their potential utility in the clinical setting. Further work is necessary to create successful models based upon mode of delivery. An unbiased approach to hemorrhage risk prediction may be superior to human risk assessment and represents an area for future research.

scholarly journals Prediction of Maternal Hemorrhage: Using Machine Learning to Identify Patients at Risk

2020 ◽  
Author(s):  
Jill M. Westcott ◽  
Francine Hughes ◽  
Wenke Liu ◽  
Mark Grivainis ◽  
David Fenyö
Keyword(s):  
Machine Learning ◽  
At Risk ◽  
Postpartum Hemorrhage ◽  
Mode Of Delivery ◽  
Machine Learning Techniques ◽  
List Type ◽  
Second Stage ◽  
Machine Learning Methods ◽  
Learning Techniques ◽  
Patients At Risk

AbstractBackgroundPostpartum hemorrhage remains one of the largest causes of maternal morbidity and mortality in the United States.ObjectiveTo utilize machine learning techniques to identify patients at risk for postpartum hemorrhage at obstetric delivery.Study DesignWomen aged 18 to 55 delivering at a major academic center from July 2013 to October 2018 were included for analysis (n = 30,867). A total of 497 variables were collected from the electronic medical record including demographic information, obstetric, medical, surgical, and family history, vital signs, laboratory results, labor medication exposures, and delivery outcomes. Postpartum hemorrhage was defined as a blood loss of 1000 mL at the time of delivery, regardless of delivery method, with 2179 positive cases observed (7.06%).Supervised learning with regression-, tree-, and kernel-based machine learning methods was used to create classification models based upon training (n = 21,606) and validation (n = 4,630) cohorts. Models were tuned using feature selection algorithms and domain knowledge. An independent test cohort (n = 4,631) determined final performance by assessing for accuracy, area under the receiver operating curve (AUC), and sensitivity for proper classification of postpartum hemorrhage. Separate models were created using all collected data versus limited to data available prior to the second stage of labor/at the time of decision to proceed with cesarean delivery. Additional models examined patients by mode of delivery.ResultsGradient boosted decision trees achieved the best discrimination in the overall model. The model including all data mildly outperformed the second stage model (AUC 0.979, 95% CI 0.971–0.986 vs. AUC 0.955, 95% CI 0.939–0.970). Optimal model accuracy was 98.1% with a sensitivity of 0.763 for positive prediction of postpartum hemorrhage. The second stage model achieved an accuracy of 98.0% with a sensitivity of 0.737. Other selected algorithms returned ≥ models that performed with decreased discrimination. Models stratified by mode of delivery achieved good to excellent discrimination, but lacked sensitivity necessary for clinical applicability.ConclusionsMachine learning methods can be used to identify women at risk for postpartum hemorrhage who may benefit from individualized preventative measures. Models limited to data available prior to delivery perform nearly as well as those with more complete datasets, supporting their potential utility in the clinical setting. Further work is necessary to create successful models based upon mode of delivery. An unbiased approach to hemorrhage risk prediction may be superior to human risk assessment and represents an area for future research.CondensationMachine learning methods can be successfully utilized to predict nearly three-quarters of women at risk of postpartum hemorrhage when undergoing obstetric delivery.AJOG at a GlanceWhy was the study conducted? To determine patients at risk for postpartum hemorrhage using modern machine learning techniques on a robust data set directly derived from the electronic medical recordWhat are the key findings? Using 28 predictor features, the model successfully classified 73.7% of patients who ultimately had a postpartum hemorrhage using information available prior to deliveryMany previously identified risk factors for postpartum hemorrhage were not included in the final model, potentially discounting their contribution to hemorrhage riskModels stratified by delivery method achieved good to excellent discrimination but noted lower sensitivity and need further investigationWhat does this study add to what is already known? This study represents the largest cohort directly-derived from the electronic medical record to use machine learning techniques to identify patients at risk for postpartum hemorrhage

scholarly journals Machine Learning in Aging: An Example of Developing Prediction Models for Serious Fall Injury in Older Adults

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 268-269
Author(s):  
Jaime Speiser ◽  
Kathryn Callahan ◽  
Jason Fanning ◽  
Thomas Gill ◽  
Anne Newman ◽  
...  
Keyword(s):  
Machine Learning ◽  
Older Adults ◽  
Random Forest ◽  
Decision Tree ◽  
Prediction Models ◽  
Receiver Operating Curve ◽  
Learning Methods ◽  
Life Study ◽  
Fall Injury ◽  
Machine Learning Methods

Abstract Advances in computational algorithms and the availability of large datasets with clinically relevant characteristics provide an opportunity to develop machine learning prediction models to aid in diagnosis, prognosis, and treatment of older adults. Some studies have employed machine learning methods for prediction modeling, but skepticism of these methods remains due to lack of reproducibility and difficulty understanding the complex algorithms behind models. We aim to provide an overview of two common machine learning methods: decision tree and random forest. We focus on these methods because they provide a high degree of interpretability. We discuss the underlying algorithms of decision tree and random forest methods and present a tutorial for developing prediction models for serious fall injury using data from the Lifestyle Interventions and Independence for Elders (LIFE) study. Decision tree is a machine learning method that produces a model resembling a flow chart. Random forest consists of a collection of many decision trees whose results are aggregated. In the tutorial example, we discuss evaluation metrics and interpretation for these models. Illustrated in data from the LIFE study, prediction models for serious fall injury were moderate at best (area under the receiver operating curve of 0.54 for decision tree and 0.66 for random forest). Machine learning methods may offer improved performance compared to traditional models for modeling outcomes in aging, but their use should be justified and output should be carefully described. Models should be assessed by clinical experts to ensure compatibility with clinical practice.

Novel Machine Learning Methods for ERP Analysis: A Validation From Research on Infants at Risk for Autism

2012 ◽  
Vol 37 (3) ◽  
pp. 274-298 ◽  
Author(s):  
Daniel Stahl ◽  
Andrew Pickles ◽  
Mayada Elsabbagh ◽  
Mark H. Johnson ◽  
The BASIS Team
Keyword(s):  
Machine Learning ◽  
At Risk ◽  
Learning Methods ◽  
Machine Learning Methods

Using Machine Learning to Advance Early Warning Systems: Promise and Pitfalls

2020 ◽  
Vol 122 (14) ◽  
pp. 1-30
Author(s):  
James Soland ◽  
Benjamin Domingue ◽  
David Lang
Keyword(s):  
Machine Learning ◽  
High School ◽  
At Risk ◽  
Early Warning ◽  
Early Warning Systems ◽  
Machine Learning Techniques ◽  
Dropping Out ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Learning Techniques

Background/Context Early warning indicators (EWI) are often used by states and districts to identify students who are not on track to finish high school, and provide supports/interventions to increase the odds the student will graduate. While EWI are diverse in terms of the academic behaviors they capture, research suggests that indicators like course failures, chronic absenteeism, and suspensions can help identify students in need of additional supports. In parallel with the expansion of administrative data that have made early versions of EWI possible, new machine learning methods have been developed. These methods are data-driven and often designed to sift through thousands of variables with the purpose of identifying the best predictors of a given outcome. While applications of machine learning techniques to identify students at-risk of high school dropout have obvious appeal, few studies consider the benefits and limitations of applying those models in an EWI context, especially as they relate to questions of fairness and equity. Focus of Study In this study, we will provide applied examples of how machine learning can be used to support EWI selection. The purpose is to articulate the broad risks and benefits of using machine learning methods to identify students who may be at risk of dropping out. We focus on dropping out given its salience in the EWI literature, but also anticipate generating insights that will be germane to EWI used for a variety of outcomes. Research Design We explore these issues by using several hypothetical examples of how ML techniques might be used to identify EWI. For example, we show results from decision tree algorithms used to identify predictors of dropout that use simulated data. Conclusions/Recommendations Generally, we argue that machine learning techniques have several potential benefits in the EWI context. For example, some related methods can help create clear decision rules for which students are a dropout risk, and their predictive accuracy can be higher than for more traditional, regression-based models. At the same time, these methods often require additional statistical and data management expertise to be used appropriately. Further, the black-box nature of machine learning algorithms could invite their users to interpret results through the lens of preexisting biases about students and educational settings.

scholarly journals Clinical-learning versus machine-learning for transdiagnostic prediction of psychosis onset in individuals at-risk

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Paolo Fusar-Poli ◽  
Dominic Stringer ◽  
Alice M. S. Durieux ◽  
Grazia Rutigliano ◽  
Ilaria Bonoldi ◽  
...  
Keyword(s):  
Machine Learning ◽  
At Risk ◽  
Model Building ◽  
A Priori ◽  
Prognostic Models ◽  
Clinical Learning ◽  
Clinical Knowledge ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Icd 10

Abstract Predicting the onset of psychosis in individuals at-risk is based on robust prognostic model building methods including a priori clinical knowledge (also termed clinical-learning) to preselect predictors or machine-learning methods to select predictors automatically. To date, there is no empirical research comparing the prognostic accuracy of these two methods for the prediction of psychosis onset. In a first experiment, no improved performance was observed when machine-learning methods (LASSO and RIDGE) were applied—using the same predictors—to an individualised, transdiagnostic, clinically based, risk calculator previously developed on the basis of clinical-learning (predictors: age, gender, age by gender, ethnicity, ICD-10 diagnostic spectrum), and externally validated twice. In a second experiment, two refined versions of the published model which expanded the granularity of the ICD-10 diagnosis were introduced: ICD-10 diagnostic categories and ICD-10 diagnostic subdivisions. Although these refined versions showed an increase in apparent performance, their external performance was similar to the original model. In a third experiment, the three refined models were analysed under machine-learning and clinical-learning with a variable event per variable ratio (EPV). The best performing model under low EPVs was obtained through machine-learning approaches. The development of prognostic models on the basis of a priori clinical knowledge, large samples and adequate events per variable is a robust clinical prediction method to forecast psychosis onset in patients at-risk, and is comparable to machine-learning methods, which are more difficult to interpret and implement. Machine-learning methods should be preferred for high dimensional data when no a priori knowledge is available.

scholarly journals Exploiting Machine Learning Algorithms and Methods for the Prediction of Agitated Delirium After Cardiac Surgery: Models Development and Validation Study

10.2196/14993 ◽  
2019 ◽  
Vol 7 (4) ◽  
pp. e14993
Author(s):  
Hani Nabeel Mufti ◽  
Gregory Marshal Hirsch ◽  
Samina Raza Abidi ◽  
Syed Sibte Raza Abidi
Keyword(s):  
Machine Learning ◽  
Cardiac Surgery ◽  
Postoperative Delirium ◽  
Class Imbalance ◽  
Area Under The Curve ◽  
Training Dataset ◽  
Learning Methods ◽  
Test Dataset ◽  
Machine Learning Methods ◽  
Patients At Risk

Background Delirium is a temporary mental disorder that occasionally affects patients undergoing surgery, especially cardiac surgery. It is strongly associated with major adverse events, which in turn leads to increased cost and poor outcomes (eg, need for nursing home due to cognitive impairment, stroke, and death). The ability to foresee patients at risk of delirium will guide the timely initiation of multimodal preventive interventions, which will aid in reducing the burden and negative consequences associated with delirium. Several studies have focused on the prediction of delirium. However, the number of studies in cardiac surgical patients that have used machine learning methods is very limited. Objective This study aimed to explore the application of several machine learning predictive models that can pre-emptively predict delirium in patients undergoing cardiac surgery and compare their performance. Methods We investigated a number of machine learning methods to develop models that can predict delirium after cardiac surgery. A clinical dataset comprising over 5000 actual patients who underwent cardiac surgery in a single center was used to develop the models using logistic regression, artificial neural networks (ANN), support vector machines (SVM), Bayesian belief networks (BBN), naïve Bayesian, random forest, and decision trees. Results Only 507 out of 5584 patients (11.4%) developed delirium. We addressed the underlying class imbalance, using random undersampling, in the training dataset. The final prediction performance was validated on a separate test dataset. Owing to the target class imbalance, several measures were used to evaluate algorithm’s performance for the delirium class on the test dataset. Out of the selected algorithms, the SVM algorithm had the best F1 score for positive cases, kappa, and positive predictive value (40.2%, 29.3%, and 29.7%, respectively) with a P=.01, .03, .02, respectively. The ANN had the best receiver-operator area-under the curve (78.2%; P=.03). The BBN had the best precision-recall area-under the curve for detecting positive cases (30.4%; P=.03). Conclusions Although delirium is inherently complex, preventive measures to mitigate its negative effect can be applied proactively if patients at risk are prospectively identified. Our results highlight 2 important points: (1) addressing class imbalance on the training dataset will augment machine learning model’s performance in identifying patients likely to develop postoperative delirium, and (2) as the prediction of postoperative delirium is difficult because it is multifactorial and has complex pathophysiology, applying machine learning methods (complex or simple) may improve the prediction by revealing hidden patterns, which will lead to cost reduction by prevention of complications and will optimize patients’ outcomes.

scholarly journals Exploiting Machine Learning Algorithms and Methods for the Prediction of Agitated Delirium After Cardiac Surgery: Models Development and Validation Study (Preprint)

2019 ◽  
Author(s):  
Hani Nabeel Mufti ◽  
Gregory Marshal Hirsch ◽  
Samina Raza Abidi ◽  
Syed Sibte Raza Abidi
Keyword(s):  
Machine Learning ◽  
Cardiac Surgery ◽  
Postoperative Delirium ◽  
Class Imbalance ◽  
Area Under The Curve ◽  
Training Dataset ◽  
Learning Methods ◽  
Test Dataset ◽  
Machine Learning Methods ◽  
Patients At Risk

BACKGROUND Delirium is a temporary mental disorder that occasionally affects patients undergoing surgery, especially cardiac surgery. It is strongly associated with major adverse events, which in turn leads to increased cost and poor outcomes (eg, need for nursing home due to cognitive impairment, stroke, and death). The ability to foresee patients at risk of delirium will guide the timely initiation of multimodal preventive interventions, which will aid in reducing the burden and negative consequences associated with delirium. Several studies have focused on the prediction of delirium. However, the number of studies in cardiac surgical patients that have used machine learning methods is very limited. OBJECTIVE This study aimed to explore the application of several machine learning predictive models that can pre-emptively predict delirium in patients undergoing cardiac surgery and compare their performance. METHODS We investigated a number of machine learning methods to develop models that can predict delirium after cardiac surgery. A clinical dataset comprising over 5000 actual patients who underwent cardiac surgery in a single center was used to develop the models using logistic regression, artificial neural networks (ANN), support vector machines (SVM), Bayesian belief networks (BBN), naïve Bayesian, random forest, and decision trees. RESULTS Only 507 out of 5584 patients (11.4%) developed delirium. We addressed the underlying class imbalance, using random undersampling, in the training dataset. The final prediction performance was validated on a separate test dataset. Owing to the target class imbalance, several measures were used to evaluate algorithm’s performance for the delirium class on the test dataset. Out of the selected algorithms, the SVM algorithm had the best F1 score for positive cases, kappa, and positive predictive value (40.2%, 29.3%, and 29.7%, respectively) with a <italic>P</italic>=.01, .03, .02, respectively. The ANN had the best receiver-operator area-under the curve (78.2%; <italic>P</italic>=.03). The BBN had the best precision-recall area-under the curve for detecting positive cases (30.4%; <italic>P</italic>=.03). CONCLUSIONS Although delirium is inherently complex, preventive measures to mitigate its negative effect can be applied proactively if patients at risk are prospectively identified. Our results highlight 2 important points: (1) addressing class imbalance on the training dataset will augment machine learning model’s performance in identifying patients likely to develop postoperative delirium, and (2) as the prediction of postoperative delirium is difficult because it is multifactorial and has complex pathophysiology, applying machine learning methods (complex or simple) may improve the prediction by revealing hidden patterns, which will lead to cost reduction by prevention of complications and will optimize patients’ outcomes.

Use of machine learning methods for prediction of acute toxicity in organs at risk following prostate radiotherapy

2011 ◽  
Vol 38 (6Part1) ◽  
pp. 2859-2867 ◽  
Author(s):  
Andrea Pella ◽  
Raffaella Cambria ◽  
Marco Riboldi ◽  
Barbara Alicja Jereczek-Fossa ◽  
Cristiana Fodor ◽  
...  
Keyword(s):  
Machine Learning ◽  
At Risk ◽  
Acute Toxicity ◽  
Organs At Risk ◽  
Learning Methods ◽  
Prostate Radiotherapy ◽  
Machine Learning Methods

scholarly journals Association of TLR 9 gene polymorphisms with remission in patients with rheumatoid arthritis receiving TNF-α inhibitors and development of machine learning models

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Woorim Kim ◽  
Tae Hyeok Kim ◽  
Soo Jin Oh ◽  
Hyun Jeong Kim ◽  
Joo Hee Kim ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Machine Learning ◽  
Logistic Regression ◽  
Gene Polymorphisms ◽  
Elastic Net ◽  
Receiver Operating Curve ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Tnf Α ◽  
Tlr9 Gene

AbstractToll-like receptor (TLR)-4 and TLR9 are known to play important roles in the immune system, and several studies have shown their association with the development of rheumatoid arthritis (RA) and regulation of tumor necrosis factor alpha (TNF-α). However, studies that investigate the association between TLR4 or TLR9 gene polymorphisms and remission of the disease in RA patients taking TNF-α inhibitors have yet to be conducted. In this context, this study was designed to investigate the effects of polymorphisms in TLR4 and TLR9 on response to TNF-α inhibitors and to train various models using machine learning approaches to predict remission. A total of six single nucleotide polymorphisms (SNPs) were investigated. Logistic regression analysis was used to investigate the association between genetic polymorphisms and response to treatment. Various machine learning methods were utilized for prediction of remission. After adjusting for covariates, the rate of remission of T-allele carriers of TLR9 rs352139 was about 5 times that of the CC-genotype carriers (95% confidence interval (CI) 1.325–19.231, p = 0.018). Among machine learning algorithms, multivariate logistic regression and elastic net showed the best prediction with the area under the receiver-operating curve (AUROC) value of 0.71 (95% CI 0.597–0.823 for both models). This study showed an association between a TLR9 polymorphism (rs352139) and treatment response in RA patients receiving TNF-α inhibitors. Moreover, this study utilized various machine learning methods for prediction, among which the elastic net provided the best model for remission prediction.

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