scholarly journals Predicting women with depressive symptoms postpartum with machine learning methods

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sam Andersson ◽  
Deepti R. Bathula ◽  
Stavros I. Iliadis ◽  
Martin Walter ◽  
Alkistis Skalkidou
Keyword(s):  
Machine Learning ◽  
High Risk ◽  
Postpartum Depression ◽  
Predictive Value ◽  
Area Under The Curve ◽  
Population Based ◽  
Health Condition ◽  
Learning Methods ◽  
Basic Study ◽  
Machine Learning Methods

AbstractPostpartum depression (PPD) is a detrimental health condition that affects 12% of new mothers. Despite negative effects on mothers’ and children’s health, many women do not receive adequate care. Preventive interventions are cost-efficient among high-risk women, but our ability to identify these is poor. We leveraged the power of clinical, demographic, and psychometric data to assess if machine learning methods can make accurate predictions of postpartum depression. Data were obtained from a population-based prospective cohort study in Uppsala, Sweden, collected between 2009 and 2018 (BASIC study, n = 4313). Sub-analyses among women without previous depression were performed. The extremely randomized trees method provided robust performance with highest accuracy and well-balanced sensitivity and specificity (accuracy 73%, sensitivity 72%, specificity 75%, positive predictive value 33%, negative predictive value 94%, area under the curve 81%). Among women without earlier mental health issues, the accuracy was 64%. The variables setting women at most risk for PPD were depression and anxiety during pregnancy, as well as variables related to resilience and personality. Future clinical models that could be implemented directly after delivery might consider including these variables in order to identify women at high risk for postpartum depression to facilitate individualized follow-up and cost-effectiveness.

scholarly journals Machine Learning Methods to Identify Missed Cases of Bladder Cancer in Population-Based Registries

2021 ◽  
pp. 641-653
Author(s):  
Anne-Michelle Noone ◽  
Clara J. K. Lam ◽  
Angela B. Smith ◽  
Matthew E. Nielsen ◽  
Eric Boyd ◽  
...  
Keyword(s):  
United States ◽  
Machine Learning ◽  
Bladder Cancer ◽  
Cancer Incidence ◽  
Cancer Registries ◽  
The United States ◽  
Population Based ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Classification And Regression

PURPOSE Population-based cancer incidence rates of bladder cancer may be underestimated. Accurate estimates are needed for understanding the burden of bladder cancer in the United States. We developed and evaluated the feasibility of a machine learning–based classifier to identify bladder cancer cases missed by cancer registries, and estimated the rate of bladder cancer cases potentially missed. METHODS Data were from population-based cohort of 37,940 bladder cancer cases 65 years of age and older in the SEER cancer registries linked with Medicare claims (2007-2013). Cases with other urologic cancers, abdominal cancers, and unrelated cancers were included as control groups. A cohort of cancer-free controls was also selected using the Medicare 5% random sample. We used five supervised machine learning methods: classification and regression trees, random forest, logic regression, support vector machines, and logistic regression, for predicting bladder cancer. RESULTS Registry linkages yielded 37,940 bladder cancer cases and 766,303 cancer-free controls. Using health insurance claims, classification and regression trees distinguished bladder cancer cases from noncancer controls with very high accuracy (95%). Bacille Calmette-Guerin, cystectomy, and mitomycin were the most important predictors for identifying bladder cancer. From 2007 to 2013, we estimated that up to 3,300 bladder cancer cases in the United States may have been missed by the SEER registries. This would result in an average of 3.5% increase in the reported incidence rate. CONCLUSION SEER cancer registries may potentially miss bladder cancer cases during routine reporting. These missed cases can be identified leveraging Medicare claims and data analytics, leading to more accurate estimates of bladder cancer incidence.

scholarly journals The effects of co-morbidity in defining major depression subtypes associated with long-term course and severity

2014 ◽  
Vol 44 (15) ◽  
pp. 3289-3302 ◽  
Author(s):  
K. J. Wardenaar ◽  
H. M. van Loo ◽  
T. Cai ◽  
M. Fava ◽  
M. J. Gruber ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cluster Analysis ◽  
High Risk ◽  
Penalized Regression ◽  
World Health ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Co Morbidity ◽  
Predicted Values

Background.Although variation in the long-term course of major depressive disorder (MDD) is not strongly predicted by existing symptom subtype distinctions, recent research suggests that prediction can be improved by using machine learning methods. However, it is not known whether these distinctions can be refined by added information about co-morbid conditions. The current report presents results on this question.Method.Data came from 8261 respondents with lifetime DSM-IV MDD in the World Health Organization (WHO) World Mental Health (WMH) Surveys. Outcomes included four retrospectively reported measures of persistence/severity of course (years in episode; years in chronic episodes; hospitalization for MDD; disability due to MDD). Machine learning methods (regression tree analysis; lasso, ridge and elastic net penalized regression) followed by k-means cluster analysis were used to augment previously detected subtypes with information about prior co-morbidity to predict these outcomes.Results.Predicted values were strongly correlated across outcomes. Cluster analysis of predicted values found three clusters with consistently high, intermediate or low values. The high-risk cluster (32.4% of cases) accounted for 56.6–72.9% of high persistence, high chronicity, hospitalization and disability. This high-risk cluster had both higher sensitivity and likelihood ratio positive (LR+; relative proportions of cases in the high-risk cluster versus other clusters having the adverse outcomes) than in a parallel analysis that excluded measures of co-morbidity as predictors.Conclusions.Although the results using the retrospective data reported here suggest that useful MDD subtyping distinctions can be made with machine learning and clustering across multiple indicators of illness persistence/severity, replication with prospective data is needed to confirm this preliminary conclusion.

scholarly journals Prediction of post-stroke epilepsy using machine learning method

2020 ◽  
Author(s):  
Anjiao Peng ◽  
Xiaorong Yang ◽  
Zhining Wen ◽  
Wanling Li ◽  
Yusha Tang ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Risk ◽  
Cerebral Infarction ◽  
Cerebral Hemorrhage ◽  
Characteristic Curve ◽  
Support Vector ◽  
Learning Methods ◽  
Post Stroke ◽  
Machine Learning Methods ◽  
One Year

Abstract Background : Stroke is one of the most important causes of epilepsy and we aimed to find if it is possible to predict patients with high risk of developing post-stroke epilepsy (PSE) at the time of discharge using machine learning methods. Methods : Patients with stroke were enrolled and followed at least one year. Machine learning methods including support vector machine (SVM), random forest (RF) and logistic regression (LR) were used to learn the data. Results : A total of 2730 patients with cerebral infarction and 844 patients with cerebral hemorrhage were enrolled and the risk of PSE was 2.8% after cerebral infarction and 7.8% after cerebral hemorrhage in one year. Machine learning methods showed good performance in predicting PSE. The area under the receiver operating characteristic curve (AUC) for SVM and RF in predicting PSE after cerebral infarction was close to 1 and it was 0.92 for LR. When predicting PSE after cerebral hemorrhage, the performance of SVM was best with AUC being close to 1, followed by RF ( AUC = 0.99) and LR (AUC = 0.85) . Conclusion : Machine learning methods could be used to predict patients with high risk of developing PSE, which will help to stratify patients with high risk and start treatment earlier. Nevertheless, more work is needed before the application of thus intelligent predictive model in clinical practice.

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.

scholarly journals Development and Validation of Interpretable Machine Learning for Stroke Occurrence in Older, Community Chinese Dwellers

2021 ◽  
Author(s):  
Yafei Wu ◽  
Zhongquan Jiang ◽  
Shaowu Lin ◽  
Ya Fang
Keyword(s):  
Machine Learning ◽  
Older Adults ◽  
Logistic Regression ◽  
High Risk ◽  
Prediction Models ◽  
Curve Analysis ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Interpretable Machine Learning ◽  
Extreme Gradient Boosting

Abstract Background: Prediction of stroke based on individuals’ risk factors, especially for a first stroke event, is of great significance for primary prevention of high-risk populations. Our study aimed to investigate the applicability of interpretable machine learning for predicting a 2-year stroke occurrence in older adults compared with logistic regression.Methods: A total of 5960 participants consecutively surveyed from July 2011 to August 2013 in the China Health and Retirement Longitudinal Study were included for analysis. We constructed a traditional logistic regression (LR) and two machine learning methods, namely random forest (RF) and extreme gradient boosting (XGBoost), to distinguish stroke occurrence versus non-stroke occurrence using data on demographics, lifestyle, disease history, and clinical variables. Grid search and 10-fold cross validation were used to tune the hyperparameters. Model performance was assessed by discrimination, calibration, decision curve and predictiveness curve analysis.Results: Among the 5960 participants, 131 (2.20%) of them developed stroke after an average of 2-year follow-up. Our prediction models distinguished stroke occurrence versus non-stroke occurrence with excellent performance. The AUCs of machine learning methods (RF, 0.823[95% CI, 0.759-0.886]; XGBoost, 0.808[95% CI, 0.730-0.886]) were significantly higher than LR (0.718[95% CI, 0.649, 0.787], p<0.05). No significant difference was observed between RF and XGBoost (p>0.05). All prediction models had good calibration results, and the brier score were 0.022 (95% CI, 0.015-0.028) in LR, 0.019 (95% CI, 0.014-0.025) in RF, and 0.020 (95% CI, 0.015-0.026) in XGBoost. XGBoost had much higher net benefits within a wider threshold range in terms of decision curve analysis, and more capable of recognizing high risk individuals in terms of predictiveness curve analysis. A total of eight predictors including gender, waist-to-height ratio, dyslipidemia, glycated hemoglobin, white blood cell count, blood glucose, triglycerides, and low-density lipoprotein cholesterol ranked top 5 in three prediction models.Conclusions: Machine learning methods, especially for XGBoost, had the potential to predict stroke occurrence compared with traditional logistic regression in the older adults.

scholarly journals Comparison of Machine Learning Methods and Conventional Logistic Regressions for Predicting Gestational Diabetes Using Routine Clinical Data: A Retrospective Cohort Study

2020 ◽  
Vol 2020 ◽  
pp. 1-10 ◽  
Author(s):  
Yunzhen Ye ◽  
Yu Xiong ◽  
Qiongjie Zhou ◽  
Jiangnan Wu ◽  
Xiaotian Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Gestational Diabetes ◽  
Early Pregnancy ◽  
Predictive Value ◽  
Regression Models ◽  
Learning Methods ◽  
Logistic Regression Models ◽  
Machine Learning Methods ◽  
Logistic Regressions

Background. Gestational diabetes mellitus (GDM) contributes to adverse pregnancy and birth outcomes. In recent decades, extensive research has been devoted to the early prediction of GDM by various methods. Machine learning methods are flexible prediction algorithms with potential advantages over conventional regression. Objective. The purpose of this study was to use machine learning methods to predict GDM and compare their performance with that of logistic regressions. Methods. We performed a retrospective, observational study including women who attended their routine first hospital visits during early pregnancy and had Down’s syndrome screening at 16-20 gestational weeks in a tertiary maternity hospital in China from 2013.1.1 to 2017.12.31. A total of 22,242 singleton pregnancies were included, and 3182 (14.31%) women developed GDM. Candidate predictors included maternal demographic characteristics and medical history (maternal factors) and laboratory values at early pregnancy. The models were derived from the first 70% of the data and then validated with the next 30%. Variables were trained in different machine learning models and traditional logistic regression models. Eight common machine learning methods (GDBT, AdaBoost, LGB, Logistic, Vote, XGB, Decision Tree, and Random Forest) and two common regressions (stepwise logistic regression and logistic regression with RCS) were implemented to predict the occurrence of GDM. Models were compared on discrimination and calibration metrics. Results. In the validation dataset, the machine learning and logistic regression models performed moderately (AUC 0.59-0.74). Overall, the GBDT model performed best (AUC 0.74, 95% CI 0.71-0.76) among the machine learning methods, with negligible differences between them. Fasting blood glucose, HbA1c, triglycerides, and BMI strongly contributed to GDM. A cutoff point for the predictive value at 0.3 in the GBDT model had a negative predictive value of 74.1% (95% CI 69.5%-78.2%) and a sensitivity of 90% (95% CI 88.0%-91.7%), and the cutoff point at 0.7 had a positive predictive value of 93.2% (95% CI 88.2%-96.1%) and a specificity of 99% (95% CI 98.2%-99.4%). Conclusion. In this study, we found that several machine learning methods did not outperform logistic regression in predicting GDM. We developed a model with cutoff points for risk stratification of GDM.

scholarly journals Bayesian Network as a Decision Tool for Predicting ALS Disease

2021 ◽  
Vol 11 (2) ◽  
pp. 150
Author(s):  
Hasan Aykut Karaboga ◽  
Aslihan Gunel ◽  
Senay Vural Korkut ◽  
Ibrahim Demir ◽  
Resit Celik
Keyword(s):  
Machine Learning ◽  
Bayesian Networks ◽  
Neurological Diseases ◽  
Low Cost ◽  
Early Period ◽  
Area Under The Curve ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Comparison Results ◽  
Als Patients

Clinical diagnosis of amyotrophic lateral sclerosis (ALS) is difficult in the early period. But blood tests are less time consuming and low cost methods compared to other methods for the diagnosis. The ALS researchers have been used machine learning methods to predict the genetic architecture of disease. In this study we take advantages of Bayesian networks and machine learning methods to predict the ALS patients with blood plasma protein level and independent personal features. According to the comparison results, Bayesian Networks produced best results with accuracy (0.887), area under the curve (AUC) (0.970) and other comparison metrics. We confirmed that sex and age are effective variables on the ALS. In addition, we found that the probability of onset involvement in the ALS patients is very high. Also, a person’s other chronic or neurological diseases are associated with the ALS disease. Finally, we confirmed that the Parkin level may also have an effect on the ALS disease. While this protein is at very low levels in Parkinson’s patients, it is higher in the ALS patients than all control groups.

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