{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,15]],"date-time":"2025-08-15T01:04:06Z","timestamp":1755219846464,"version":"3.43.0"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686080","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T00:00:00Z","timestamp":1754524800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,8,7]]},"abstract":"<jats:p>This paper presents a scalable, serverless machine learning operations (ML Ops) architecture for near real-time sepsis detection in Emergency Department (ED) waiting rooms. Built on Amazon Web Services (AWS) cloud environment, the system processes HL7 messages via MuleSoft, using Lambda for data handling, and SageMaker for model deployment. Data is stored in Aurora PostgreSQL and visualized in on-premise Tableau\u2122. With 99.7% of HL7 messages successfully processed, the system shows strong performance, though occasional downtime, code set mismatches, and peak execution times reveal areas for optimization.<\/jats:p>","DOI":"10.3233\/shti251304","type":"book-chapter","created":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T11:47:52Z","timestamp":1754567272000},"source":"Crossref","is-referenced-by-count":0,"title":["Decoding Sepsis: A Technical Blueprint for an Algorithm-Driven System Architecture"],"prefix":"10.3233","author":[{"given":"Abdullah","family":"Safi","sequence":"first","affiliation":[{"name":"Ministry of Health, New South Wales, Australia"}]},{"given":"Mostafa","family":"Shaikh","sequence":"additional","affiliation":[{"name":"Ministry of Health, New South Wales, Australia"}]},{"given":"Minh Trang","family":"Hoang","sequence":"additional","affiliation":[{"name":"Biomedical Informatics and Digital Health, The University of Sydney, Australia"}]},{"given":"Amith","family":"Shetty","sequence":"additional","affiliation":[{"name":"Ministry of Health, New South Wales, Australia"},{"name":"Biomedical Informatics and Digital Health, The University of Sydney, Australia"}]},{"given":"Gladis","family":"Kabil","sequence":"additional","affiliation":[{"name":"DHI Lab, Research Education Network, Western Sydney LHD, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1230-4357","authenticated-orcid":false,"given":"Audrey P.","family":"Wang","sequence":"additional","affiliation":[{"name":"Biomedical Informatics and Digital Health, The University of Sydney, Australia"},{"name":"DHI Lab, Research Education Network, Western Sydney LHD, Australia"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","MEDINFO 2025 \u2014 Healthcare Smart \u00d7 Medicine Deep"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI251304","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T11:47:52Z","timestamp":1754567272000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI251304"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,7]]},"ISBN":["9781643686080"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti251304","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"value":"0926-9630","type":"print"},{"value":"1879-8365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,7]]}}}