Comparative machine learning approaches to prognosticate clinical outcomes in oral and maxillofacial space...

The objective of this study was to evaluate the performance of multiple machine learning algorithms to provide evidence supporting early intervention for high-risk patients with oral and maxillofacial space infections (OMSI), thereby reducing complication rates and improving overall clinical outcomes.

A retrospective cohort study was performed to analyse clinical data from 432 medical records, with a focus on key variables related to disease severity and treatment outcomes. The data included age, gender, height, weight, BMI, blood test results, such as the CRP, white blood cell count, neutrophil count, lymphocyte count, monocyte count, albumin, potassium, sodium, chlorine, calcium, uric acid, blood urea nitrogen, PCT, fibrinogen, blood glucose, vital signs, such as body temperature, heart rate, systolic blood pressure, diastolic blood pressure, respiration, days before hospitalization, the extent of mouth opening, the space of infected spaces, and the source of infection.

We summarized the predictive performance of three models-Logistic regression, Random Forest, and XGBoost-across three key clinical outcomes. For predicting hospital stay duration, Logistic regression performed best. For predicting ICU admission, XGBoost exhibited the strongest performance. For predicting surgical intervention, Logistic regression achieved the optimal overall performance, while XGBoost and Random Forest demonstrated the highest specificity.

In summary, we have successfully developed, validated, and compared predictive models for key clinical outcomes in patients with OMSI. They further hold promise for ultimately optimizing the diagnostic and therapeutic workflow and improving outcomes for patients with this common yet potentially life-threatening condition.

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