AI-Driven SaO2 prediction from pulse oximetry and electronic health records

To address limitations in pulse oximetry accuracy associated with low saturation and melanin content using machine learning (ML) and to compare our results to standard pulse oximetry and gold standard arterial blood gas readings. Oxygen saturation is traditionally measured through the gold standard arterial blood gas (SaO2) or pulse oximetry (SpO2), which approximates SaO2 using light absorption patterns. However, SpO2 has been shown to overestimate oxygen saturation, particularly in individuals with darker skin tone, leading to hidden hypoxemia and delayed medical interventions. We developed a machine learning (ML) model trained on the BOLD dataset, integrating patient data from eICU, MIMIC-III, and MIMIC-IV (n = 49,093). With 64 clinical features and 2 outcomes (SaO2 and hidden hypoxemia events), we trained regression ML models (linear regression, random forest, and XGBoost) to predict SaO2, minimizing the mean squared error between predicted SaO2 and ground truth SaO2. We used our test data to compare model performance to standard SaO2 measurement with accuracy root mean square error (Arms), R2, and change in hidden hypoxemia events. We used SHapley Additive exPlanations (SHAP) to rank important features for SaO2 prediction. The XGBoost-Vanilla model improved Arms to 3.3% from a baseline of 4.1%. In low SaO2 patients (SaO2 < 90%), the accuracy of pulse oximetry was heavily compromised with Arms of 10.3%; the linear regression model with weighted loss was able to reduce Arms to 8.6%. We found that SpO2, creatinine level, mean corpuscular hemoglobin level, respiratory rate, and age at admission were the leading features driving the SaO2 prediction. These findings suggest that ML-based models can enhance the accuracy of the standard SpO2. Further investigation is warranted to address SpO₂ inaccuracies and bias.