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

Author summaryCurrent machine learning approaches are mostly designed for decision support systems that used for predicting severity of dengue and forecasting of dengue cases. The few studies to predict plasma leakage rely on traditional statistical approach with a priori predictors. To our knowledge, no study used machine learning to predict plasma leakage in suspected dengue patients. This is the first study to develop a machine learning model to identify predictors that detect plasma leakage in the early stages of dengue infection. In addition, this study placed focus on a limited resource setting enabling decision support systems to be utilised in the low- to middle-income country of Sri Lanka. The study identified five accessible predictors as inputs to the model to aid the decision making for hospital admission for suspected cases of dengue. Lastly, the multi-metric assessments, model fairness and Shapley additive explanations push the agenda for model transparency and interpretability. Our machine learning approach demonstrated reasonable performance and has the potential to identify patients who are likely to develop plasma leakage for close monitoring to prevent severe consequences. BackgroundAt 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. MethodsA 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. ResultsLymphocyte 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. ConclusionThe 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.