• Mukesh Kumar Saini
• Brett Fritze

Sepsis remains a leading cause of mortality in hospital settings, with its early prediction and diagnosis being critical to improving patient outcomes. The challenge lies in the fact that sepsis symptoms often mimic those of less severe conditions, complicating early detection. To address this issue, we present the SERA algorithm, an advanced artificial intelligence (AI) tool designed to enhance the early prediction and diagnosis of sepsis. The SERA algorithm leverages both structured data, such as vital signs and laboratory results, and unstructured clinical notes, including physician narratives and patient histories. Our study evaluates the performance of the SERA algorithm using a dataset of independent clinical notes and demonstrates its capability to predict sepsis with high accuracy up to 12 hours before its clinical onset. The algorithm achieves an area under the curve (AUC) of 0.94, with sensitivity and specificity rates of 0.87 each, indicating strong predictive performance. In a comparative analysis with traditional physician predictions, the SERA algorithm shows a potential improvement in early sepsis detection by up to 32%, highlighting its enhanced ability to identify patients at risk earlier. Additionally, it reduces the incidence of false positives by up to 17%, thereby improving diagnostic precision. The incorporation of unstructured clinical notes into the algorithm significantly boosts its accuracy over models relying solely on structured clinical measures. This improvement is particularly notable in the 12 to 48-hour window preceding sepsis onset, underscoring the value of integrating diverse data sources for more effective early warning systems. Our findings suggest that the SERA algorithm represents a promising advancement in sepsis prediction and diagnosis, offering the potential to reduce mortality rates and improve patient outcomes through timely intervention

Sepsis, Artificial Intelligence (AI), SERA Algorithm, Structured Data, Unstructured Data, Clinical Measures, Mortality Reduction, Healthcare Analytics, Machine Learning, Risk Assessment, Patient Outcomes

"AI-Driven Early Detection of Sepsis and Utilization of Unstructured Healthcare Data for Improved Diagnosis", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 4, page no.128-141, April-2018, Available :http://www.jetir.org/papers/JETIR1804484.pdf

"AI-Driven Early Detection of Sepsis and Utilization of Unstructured Healthcare Data for Improved Diagnosis", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 4, page no. pp128-141, April-2018, Available at : http://www.jetir.org/papers/JETIR1804484.pdf