Application of AI in predicting postoperative infections using routine blood parameters
Angshuman De, Vasantavada Venkata Satya Sai Preeti, Mukul Singh, Mukesh Kumar Patwa, Niyati Pandya, Amrit Podder, Parth Jani, Chandan Gogoi
TL;DR
This paper shows how AI can predict postoperative infections using blood markers like CRP and NLR, with high accuracy.
Contribution
A novel AI approach using routine blood parameters to predict postoperative infections is introduced and validated.
Findings
A Random Forest model achieved an AUC of 0.93 in predicting postoperative infections.
CRP and NLR were identified as the most influential predictors in the model.
Abstract
The application of artificial intelligence in predicting postoperative infections using routine blood parameters is of interest. Hence, a cohort of 120 surgical patients was analyzed and machine learning models were developed using WBC, CRP, NLR and other markers. The Random Forest model achieved the highest predictive performance with an AUC of 0.93. CRP and NLR were identified as the most influential predictors. Thus, we show the integration of AI for early infection detection in surgical care.
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Taxonomy
TopicsSepsis Diagnosis and Treatment · Machine Learning in Healthcare · Artificial Intelligence in Healthcare and Education