Development of a machine learning model for early prediction of plasma leakage in suspected dengue patients

Abstract

Background At least a third of dengue patients develop plasma leakage with increased risk of life-threatening complications. Predicting plasma leakage using laboratory parameters obtained in early infection as means of triaging patients for hospital admission is important for resource-limited settings. Methods A Sri Lankan cohort including 4,768 instances of clinical data from N = 877 patients (60.3% patients with confirmed dengue infection) recorded in the first 96 hours of fever was considered. After excluding incomplete instances, the dataset was randomly split into a development and a test set with 374 (70%) and 172 (30%) patients, respectively. From the development set, five most informative features were selected using the minimum description length (MDL) algorithm. Random forest and light gradient boosting machine (LightGBM) were used to develop a classification model using the development set based on nested cross validation. An ensemble of the learners via average stacking was used as the final model to predict plasma leakage. Results Lymphocyte count, haemoglobin, haematocrit, age, and aspartate aminotransferase were the most informative features to predict plasma leakage. The final model achieved the area under the receiver operating characteristics curve, AUC = 0.80 with positive predictive value, PPV = 76.9%, negative predictive value, NPV = 72.5%, specificity = 87.9%, and sensitivity = 54.8% on the test set. Conclusion The early predictors of plasma leakage identified in this study are similar to those identified in several prior studies that used non-machine learning based methods. However, our observations strengthen the evidence base for these predictors by showing their relevance even when individual data points, missing data and non-linear associations were considered. Testing the model on different populations using these low-cost observations would identify further strengths and limitations of the presented model.

Genomic Surveillance of Recent Dengue Outbreaks in Colombo, Sri Lanka

“…The details of CDS (methods, data on clinical parameters and demographics) have been published previously [ 16 , 17 ]. In brief, the primary purpose of the cohort is to identify early risk associations for dengue associated plasma leakage [ 18 ]. Thus, patients presenting within the first 96 h of fever and who were clinically suspected of having dengue were recruited and followed up daily to record adverse outcomes including plasma leakage and shock.…”

ExplaineR: an R package to explain machine learning models

“…This is an important capability for data-driven profiling of data samples associated with individuals, e.g. patients ( Zargari Marandi et al 2023 ).…”

Using Computational Simulations Based on Fuzzy Cognitive Maps to Detect Dengue Complications

“…The model found 10 factors predictive of illness: tachycardia, respiratory failure, cold hands and feet, dyspnea, substantial blood plasma leakage, shock, altered consciousness, albumin, total protein, and leukocytes with a sensitivity of 78% and a specificity of 91%. Zaccary et al [ 27 ] proposed a classification model for the prediction of plasma leakage from laboratory tests, without an in-depth exploration of adverse patient outcomes.…”