An explainable machine learning-based clinical decision support system for prediction of gestational diabetes mellitus

Abstract

Gestational Diabetes Mellitus (GDM), a common pregnancy complication associated with many maternal and neonatal consequences, is increased in mothers with overweight and obesity. Interventions initiated early in pregnancy can reduce the rate of GDM in these women, however, untargeted interventions can be costly and time-consuming. We have developed an explainable machine learning-based clinical decision support system (CDSS) to identify at-risk women in need of targeted pregnancy intervention. Maternal characteristics and blood biomarkers at baseline from the PEARS study were used. After appropriate data preparation, synthetic minority oversampling technique and feature selection, five machine learning algorithms were applied with five-fold cross-validated grid search optimising the balanced accuracy. Our models were explained with Shapley additive explanations to increase the trustworthiness and acceptability of the system. We developed multiple models for different use cases: theoretical (AUC-PR 0.485, AUC-ROC 0.792), GDM screening during a normal antenatal visit (AUC-PR 0.208, AUC-ROC 0.659), and remote GDM risk assessment (AUC-PR 0.199, AUC-ROC 0.656). Our models have been implemented as a web server that is publicly available for academic use. Our explainable CDSS demonstrates the potential to assist clinicians in screening at risk patients who may benefit from early pregnancy GDM prevention strategies.

A rapid review of machine learning approaches for telemedicine in the scope of COVID-19

“…On the one hand, the telehealth and telemedicine adoption showed promised outcomes in medical error reduction [35] , and assertiveness in the medical conduct [36] , as well as using CDSS to help predict prognosis and assist clinicians in screening [37] become positive impacts to promptness response and resolve complex cases by consulting remotely specialized professionals. This scenario became positive when compared with traditional attendance approaches.…”

“…Besides the use of AI to enhance experiences of both clinicians and patients [33] in healthcare. Furthermore, the growth of AI explainable learning has made the AI understandable, providing explanations catered to humans, promoting transparency about the decisions and consequently reliability, [37] , [45] . Once the healthcare professionals and patients do not accept decisions without understanding and trusting the explanations, or at least how the decisions are made [46] .…”

A Clinical Decision Support System for the Prediction of Quality of Life in ALS

“…Additionally, it is based on a ML model that was built using a small sample size (due to the rarity of the disease) on an Irish population. As a result, our intent is to evaluate the CDSS on both healthcare professionals and patients in the future and to collect data from an EU cohort to examine a potential ethnic/cultural difference in QoL prediction, similarly to the study by Du et al [ 51 ]. This will allow us to expand the scope of our CDSS beyond the Irish population.…”

Artificial Intelligence for Predicting and Diagnosing Complications of Diabetes

“…9 Previous studies have demonstrated tradeoffs associated with including different types and numbers of factors and different populations, as shown in Table 2. [11][12][13][14][15][16][17][18] For selecting factors in an AI model, one must consider incorporating numerous detailed laboratory and genetic data to improve AI accuracy or fewer data only available with routine prenatal care to improve clinical usability. For different populations, one must decide whether to use data only from high-risk populations to improve accuracy (but sacrifice generalizability) or nonselected populations to improve generalizability (but sacrifice accuracy).…”

“…Additional studies are also needed in large multinational, diverse populations to further improve predictive ability and ensure there are no biases by race or other factors. 18,20 Table 1. Suggestions for Clinicians Who Are Developing and Using AI for Delivering Care.…”