Electronic health records (EHR) are patient-level information, e.g., laboratory tests and questionnaires, stored in electronic format. Compared to physical records, the EHR alternative allows patients to access their data easily and helps staff with management procedural tasks such as information sharing across different organizations. Moreover, this type of data is commonly used by researchers for predictive and classification purposes, employing statistical and machine learning methods. However, missingness is a phenomenon that is observed very frequently for such measurements. Even though this missingness is often significant, it is usually treated poorly with either case deletion or simple methods, resulting in suboptimal and/or inaccurate predictive results. This happens because the simple methods, e.g., k-nearest neighbors (kNN) and mean/mode imputation, fail in most cases to incorporate the complex relationships that define these medical datasets. To address these limitations, in this paper we test and improve state-of-the-art missing data imputation models and practices. We propose a new missing value imputation method based on denoising autoencoders (DAE) with kNN for the pre-imputation task. We optimize the training methodology by re-applying kNN to the missing data every <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N epochs using a different value for the variable <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k each time to yield more accurate results. We also revise a state-of-the-art missing data imputation approach based on a generative adversarial network (GAN). Using this as a baseline, we introduce improvements regarding both the architecture and the training procedure. These models are compared with the ones usually employed within clinical research studies for both the task of imputation and post-imputation prediction. Results show that our proposed deep learning approaches outperform the standard baselines, yielding better imputation and predictive results.

/pdf/missing-value-imputation-methods-for-electronic-health-34qxdgix.pdf

Missing Value Imputation Methods for Electronic Health Records

Cardiovascular diseases have been identified as one of the top three causes of death worldwide, with onset and deaths mostly due to heart failure (HF). In ICU, where patients with HF are at increased risk of death and consume significant medical resources, early and accurate prediction of the time of death for patients at high risk of death would enable them to receive appropriate and timely medical care. The data for this study were obtained from the MIMIC-III database, where we collected vital signs and tests for 6699 HF patient during the first 24 h of their first ICU admission. In order to predict the mortality of HF patients in ICUs more precisely, an integrated stacking model is proposed and applied in this paper. In the first stage of dataset classification, the datasets were subjected to first-level classifiers using RF, SVC, KNN, LGBM, Bagging, and Adaboost. Then, the fusion of these six classifier decisions was used to construct and optimize the stacked set of second-level classifiers. The results indicate that our model obtained an accuracy of 95.25% and AUROC of 82.55% in predicting the mortality rate of HF patients, which demonstrates the outstanding capability and efficiency of our method. In addition, the results of this study also revealed that platelets, glucose, and blood urea nitrogen were the clinical features that had the greatest impact on model prediction. The results of this analysis not only improve the understanding of patients’ conditions by healthcare professionals but allow for a more optimal use of healthcare resources.

/pdf/applying-an-improved-stacking-ensemble-model-to-predict-the-2xnmqa67.pdf

Applying an Improved Stacking Ensemble Model to Predict the Mortality of ICU Patients with Heart Failure

Background and Aims: Limited evidence exists on the prognostic outcomes of the blood urea nitrogen to serum albumin ratio (B/A ratio) in congestive heart failure (CHF), particularly in developing countries with scarce heart failure epidemiological data.We aimed to investigate the association between B/A ratio and short-term outcomes in Chinese patients with CHF. Methods and Results:We included 1761 CHF patients with available B/A ratio data from a cohort of 2008 patients.Patients were categorized into three groups based on B/A ratio (low to high).The primary endpoint was death or readmission within 28 days, and the secondary endpoint was death or readmission within 90 days.We employed restricted cubic spline analysis, Cox proportional hazards regression, and Kaplan-Meier curves to evaluate the relationship between B/A ratio at admission and the endpoints.Even after adjusting for other variables, higher B/A ratios were associated with increased rates of 28 days and 90 days mortality or readmission (HR: 2.4, 95% CI:1.81-3.18and HR: 1.74, 95% CI: 1.48-2.05).Significant differences in the risks of both primary and secondary endpoints were observed among the three B/A ratio groups.The association between B/A ratio and CHF was stable in the different subgroups (all P for interaction＞0.05). Conclusion:Higher B/A ratios are associated with an increased risk of short-term mortality or readmission in Chinese patients with CHF.The B/A ratio shows promise as a prognostic indicator for short-term outcomes in CHF patients. 

The Association of Blood Urea Nitrogen to Serum Albumin Ratio with Short-Term Outcomes in Chinese Patients with Congestive Heart Failure: A Retrospective Cohort Study

Since most patients with heart failure are re-admitted to the hospital, accurately identifying the risk of re-admission of patients with heart failure is important for clinical decision making and management. This study plans to develop an interpretable predictive model based on a Chinese population for predicting six-month re-admission rates in heart failure patients. Research data were obtained from the PhysioNet portal. To ensure robustness, we used three approaches for variable selection. Six different machine learning models were estimated based on selected variables. The ROC curve, prediction accuracy, sensitivity, and specificity were used to evaluate the performance of the established models. In addition, we visualized the optimized model with a nomogram. In all, 2002 patients with heart failure were included in this study. Of these, 773 patients experienced re-admission and a six-month re-admission incidence of 38.61%. Based on evaluation metrics, the logistic regression model performed best in the validation cohort, with an AUC of 0.634 (95%CI: 0.599–0.646) and an accuracy of 0.652. A nomogram was also generated. The established prediction model has good discrimination ability in predicting. Our findings are helpful and could provide useful information for the allocation of healthcare resources and for improving the quality of survival of heart failure patients.

/pdf/predicting-six-month-re-admission-risk-in-heart-failure-u5d5bo1g.pdf

Predicting Six-Month Re-Admission Risk in Heart Failure Patients Using Multiple Machine Learning Methods: A Study Based on the Chinese Heart Failure Population Database

Risk factors and management of pulmonary infection in elderly patients with heart failure: A retrospective analysis.

Heart failure is one of the most important reasons for hospitalization among elderly individuals and is associated with significant mortality and morbidity. Epidemiological studies require the establishment of high-quality databases. Several datasets that primarily involve heart failure populations have been established in Western countries and have generated many high-quality studies. However, no such dataset is available from China. Due to differences in genetic background and healthcare systems between China and Western countries, the establishment of a heart failure database for the Chinese population is urgently needed. We performed a retrospective single-center observational study to collect data regarding the characteristics of heart failure patients in China by integrating electronic healthcare records and follow-up outcome data. The study collected information for a total of 2,008 patients with heart failure, containing 166 attributes.

/pdf/electronic-healthcare-records-and-external-outcome-data-for-1ook98530g.pdf

Electronic healthcare records and external outcome data for hospitalized patients with heart failure.

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Electronic healthcare records and external outcome data for hospitalized patients with heart failure.