Irish journal of medical science · 2024
Interpretation of acid-base metabolism on arterial blood gas samples via machine learning algorithms.
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Ozdemir Habib, Sasmaz Muhammed Ikbal, Guven Ramazan, Avci Akkan
Аннотация
BACKGROUND: Arterial blood gas evaluation is crucial for critically ill patients, as it provides essential information about acid-base metabolism and respiratory balance, but evaluation can be complex and time-consuming. Artificial intelligence can perform tasks that require human intelligence, and it is revolutionizing healthcare through technological advancements.
AIM: This study aims to assess arterial blood gas evaluation using artificial intelligence algorithms.
METHODS: The study included 21.541 retrospective arterial blood gas samples, categorized into 15 different classes by experts for evaluating acid-base metabolism status. Six machine learning algorithms were utilized; accuracy, balanced accuracy, sensitivity, specificity, precision, and F1 values of the models were determined; and ROC curves were drawn to assess areas under the curve for each class. Evaluation of which sample was estimated in which class was conducted using the confusion matrices of the models.
RESULTS: The bagging classifier (BC) model achieved the highest balanced accuracy with 99.24%, whereas the XGBoost model reached the highest accuracy with 99.66%. The BC model shows 100% sensitivity for nine classes and 100% specificity for 10 classes, and the model correctly predicted 6438 of 6463 test samples and achieved an accuracy of 99.61%, with an area under the curve > 0.9 in all classes on a class basis.
CONCLUSION: The machine learning models developed exhibited remarkable accuracy, sensitivity, and specificity in predicting the status of acid-base metabolism. However, implementing these models can aid clinicians, freeing up their time for more intricate tasks.
AIM: This study aims to assess arterial blood gas evaluation using artificial intelligence algorithms.
METHODS: The study included 21.541 retrospective arterial blood gas samples, categorized into 15 different classes by experts for evaluating acid-base metabolism status. Six machine learning algorithms were utilized; accuracy, balanced accuracy, sensitivity, specificity, precision, and F1 values of the models were determined; and ROC curves were drawn to assess areas under the curve for each class. Evaluation of which sample was estimated in which class was conducted using the confusion matrices of the models.
RESULTS: The bagging classifier (BC) model achieved the highest balanced accuracy with 99.24%, whereas the XGBoost model reached the highest accuracy with 99.66%. The BC model shows 100% sensitivity for nine classes and 100% specificity for 10 classes, and the model correctly predicted 6438 of 6463 test samples and achieved an accuracy of 99.61%, with an area under the curve > 0.9 in all classes on a class basis.
CONCLUSION: The machine learning models developed exhibited remarkable accuracy, sensitivity, and specificity in predicting the status of acid-base metabolism. However, implementing these models can aid clinicians, freeing up their time for more intricate tasks.