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International Journal of Advanced Artificial Intelligence Research

Open Access Peer Review International
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A Longitudinal Patient Reasoning Layer for Intelligent Sepsis Surveillance in Real-Time Laboratory Networks

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Rahul Reddy Hanumanthgari 1 ORCID iD ,
4 Independent Researcher, AI Engineering, NC, USA

Abstract

Automated sepsis alert systems are now standard components of hospital clinical decision support infrastructure, yet their clinical value is undermined by false positive rates exceeding 75%, with independently validated systems reporting positive predictive values as low as 22.4%. This paper introduces the Longitudinal Patient Reasoning Layer (LPRL), a middleware architecture deployed at the reference laboratory layer that addresses this limitation by replacing population-derived reference thresholds with patient-specific longitudinal baselines. The LPRL maintains a rolling 36-month analyte history for each enrolled patient and applies four sequential reasoning modules to enrich each incoming laboratory result before alert generation. Central to this process is the Personal Deviation Ratio (PDR), which quantifies deviation from an individual patient's own historical norm rather than a population mean. The architecture is evaluated against published performance benchmarks for population-threshold and machine learning alert systems, using primary metrics of sensitivity, specificity, positive predictive value, and the Alert-to-Sepsis Positive Ratio (ASPR). Beyond sepsis surveillance, the LPRL's longitudinal profile store and PDR reasoning engine are substantively extensible to chronic disease surveillance, multi-site epidemiological monitoring, and real-world evidence generation.

Keywords

Sepsis Detection, Longitudinal Patient Baseline, Alert Fatigue, Clinical Decision Support, HL7 FHIR, Laboratory Information Systems, Real-World Evidence, EHR Interoperability

References

C. W. Seymour, F. Gesten, H. C. Prescott, et al., "Time to Treatment and Mortality during Mandated Emergency Care for Sepsis," New England Journal of Medicine, vol. 376, pp. 2235–2244, 2017.
M. Singer, C. S. Deutschman, C. W. Seymour, et al., "The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3)," JAMA, vol. 315, no. 8, pp. 801–810, 2016.
C. Rhee, R. Dantes, L. Epstein, et al., "Incidence and Trends of Sepsis in US Hospitals Using Clinical vs Claims Data, 2009–2014," JAMA, vol. 318, no. 13, pp. 1241–1249, 2017.
K. E. Rudd, S. C. Johnson, K. M. Agesa, et al., "Global, Regional, and National Sepsis Incidence and Mortality, 1990–2017: Analysis for the Global Burden of Disease Study," The Lancet, vol. 395, no. 10219, pp. 200–211, 2020.
A. Wong, E. Otles, J. P. Donnelly, et al., "External Validation of a Widely Implemented Proprietary Sepsis Prediction Model in Hospitalized Patients," JAMA Internal Medicine, vol. 181, no. 8, pp. 1065–1070, 2021.
B. J. Drew, P. Harris, J. K. Zègre-Hemsey, et al., "Insights into the Problem of Alarm Fatigue with Physiologic Monitor Devices: A Comprehensive Observational Study of Consecutive Intensive Care Unit Patients," PLOS ONE, vol. 9, no. 10, p. e110274, 2014.
S. Sendelbach and M. Funk, "Alarm Fatigue: A Patient Safety Concern," AACN Advanced Critical Care, vol. 24, no. 4, pp. 378–386, 2013.
S. Manaktala and S. R. Claypool, "Evaluating the Impact of a Computerized Surveillance Algorithm and Decision Support System on Sepsis Mortality," Journal of the American Medical Informatics Association, vol. 24, no. 1, pp. 88–95, 2017.
L. M. Fleuren, T. L. T. Klausch, C. L. Zwager, et al., "Machine Learning for the Prediction of Sepsis: A Systematic Review and Meta-Analysis of Diagnostic Test Accuracy," Intensive Care Medicine, vol. 46, pp. 383–400, 2020.
S. Nemati, A. Holder, F. Razmi, M. D. Stanley, G. D. Clifford, and T. G. Buchman, "An Interpretable Machine Learning Model for Accurate Prediction of Sepsis in the ICU," Critical Care Medicine, vol. 46, no. 4, pp. 547–553, 2018.
A. E. W. Johnson, T. J. Pollard, L. Shen, et al., "MIMIC-III, a Freely Accessible Critical Care Database," Scientific Data, vol. 3, p. 160035, 2016.
Q. Mao, M. Jay, J. L. Hoffman, et al., "Multicentre Validation of a Sepsis Prediction Algorithm Using Only Vital Sign Data in the Emergency Department, General Ward and ICU," BMJ Open, vol. 8, no. 1, p. e017833, 2018.
HL7 International, "HL7 Fast Healthcare Interoperability Resources (FHIR) Release 4," HL7 FHIR Standard, 2019. [Online]. Available: https://hl7.org/fhir/R4/ (accessed: Apr. 2026).
J. Dijk, Y. Rieter-Barrell, J. V. Rest, and H. Bouma, "Intelligent Sensor Networks for Surveillance," Journal of Police Studies: Technology-Led Policing, vol. 3, no. 20, pp. 109–125, 2011.
L.-P. Chou, J.-D. Sun, and M.-L. Chen, "A New Application Framework for Intelligent Surveillance Sensor Networks," in Proc. Third International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP 2007), Kaohsiung, Taiwan, 2007, pp. 617–620. doi: 10.1109/iih-msp.2007.39.
A. Ziani, C. Motamed, and J.-C. Noyer, "Temporal Reasoning for Scenario Recognition in Video-Surveillance Using Bayesian Networks," IET Computer Vision, vol. 2, no. 2, pp. 57–68, Jun. 2008. doi: 10.1049/iet-cvi:20070074.
S. Yang and H. Byun, "Wide Area Ontology Integration Scheme for Reasoning Agents in Surveillance Networks," Journal of Computers, vol. 11, no. 6, pp. 497–503, 2016. doi: 10.17706/jcp.11.6.497-503.
P. J. Thoral, J. M. Peppink, R. H. Driessen, et al., "Sharing ICU Patient Data Responsibly under the Society of Critical Care Medicine and European Society of Intensive Care Medicine Joint Data Science Collaboration: The Amsterdam University Medical Centers Database (AmsterdamUMCdb) Example," Critical Care Medicine, vol. 49, no. 6, pp. e563–e577, 2021.
U.S. Department of Health and Human Services, "HIPAA Security Rule," 45 C.F.R. Part 164, Subparts A and C, 2003.
J. C. Mandel, D. A. Kreda, K. D. Mandl, I. S. Kohane, and R. B. Ramoni, "SMART on FHIR: A Standards-Based, Interoperable Apps Platform for Electronic Health Records," Journal of the American Medical Informatics Association, vol. 23, no. 5, pp. 899–908, 2016.
M. P. Sendak, M. Elish, M. Gao, et al., "The Human Body is a Black Box: Supporting Clinical Decision-Making with Deep Learning," in Proc. ACM FAT* Conference, Barcelona, Spain, 2020, pp. 99–109.
M. Topaz, L. Seger, R. Slight, et al., "Rising Drug-Drug Interaction Alerts and Overrides in the United States—5-Year Analysis," Journal of the American Medical Informatics Association, vol. 23, no. 3, pp. 601–608, 2016.

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