[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-449085-105":59,"doc-detail-449085-en":130},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":123,"head_meta":125,"extra_data":127,"updated_unix":129},105,"en","from-narrative-to-navigation-a-translational-roadmap-for-ai-enabled-early-sepsis-prediction-comment","From Narrative to Navigation - A Translational Roadmap for AI-enabled Early Sepsis Prediction - Comment","","Letter to the editor commenting on Shanmugam et al.’s scoping review of machine learning and deep learning for early sepsis prediction in the Indian Journal of Critical Care Medicine. It proposes extending the review by outlining a translational roadmap tailored to resource-constrained ICUs, emphasizing minimal standardized datasets, explainability and quantified uncertainty, prospective workflow-aware evaluations using sepsis bundles and early warning scores, and equity-focused governance. The letter highlights system performance over time, calibration drift, external validation, and trustable bedside action.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/from-narrative-to-navigation-a-translational-roadmap-for-ai-enabled-early-sepsis-prediction-comment/449085/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/from-narrative-to-navigation-a-translational-roadmap-for-ai-enabled-early-sepsis-prediction-comment/449085.png","ImageObject",300,407,{"name":92,"@type":93},"eBook King","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-01","2026-09-30",true,{"@type":102,"interactionType":103,"userInteractionCount":39},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What is the main purpose of this letter?","Question",{"text":112,"@type":113},"To comment on Shanmugam et al.’s scoping review and propose a concrete translational roadmap for deploying AI tools for early sepsis prediction in real-world ICUs.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"Which design choices are highlighted for making AI usable in resource-constrained ICUs?",{"text":117,"@type":113},"Using minimal standardized datasets, prioritizing interpretability and uncertainty reporting by design, and ensuring models are implementable with limited real-time data.",{"name":119,"@type":110,"acceptedAnswer":120},"How should future evaluations be structured according to the letter?",{"text":121,"@type":113},"With prospective, workflow-aware pilot studies integrating AI risk scores with existing sepsis bundles and early warning scores, using predefined impact metrics like time-to-antibiotics and ICU resource use.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},449085,1791346395,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":39,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":14,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":143,"read_time":24,"preview_page_count":4},962088006270,"https://ap-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45","LETTER TO THE EDITOR  \nFrom Narrative to Navigation—A Translational Roadmap for AI-enabled Early Sepsis Prediction: Comment on Shanmugamet al.  \nMulavagili Vijayasimha1, D Jayarajan2, Zeenat R Mir3  \nKeywords: Artificial intelligence, Critical care, Explainable machine learning, India, Sepsis prediction.  \nIndian Journal of Critical Care Medicine (2025): 10.5005/jp-journals-10071-25105  \nDear Editor,  \nShanmugam et al. provide a timely scoping review on “Machine Learning and Deep Learning Models for Early Sepsis Prediction”in the Indian Journal of Critical Care Medicine, mapping an increasingly complex AI literature for early sepsis warning.1 Their synthesis is highly relevant for intensivists in India and other lowand middle-income countries (LMICs), where sepsis burden is high and digital infrastructure remains heterogeneous. We believe their work can be extended by outlining a concrete translational roadmap for deploying AI tools in resource-constrained intensive care units (ICUs).  \nRecent explainable AI ( X AI) models for bloodstream infection (BSI) prediction explicitly prioritize data parsimony and interpretability. Bopche et al. used historical electronic health records and an interpretable XAI framework to predict BSI, deliberately avoiding dependence on continuous high-fidelity monitoring so that the model remains usable in hospitals with limited real-time data streams.2 Murri et al. similarly developed a machine-learning model for BSI in hospitalized patients, demonstrating that transparent models can still achieve clinically useful discrimination.3 These studies go beyond architectural novelty; they exemplify design choices that are realistic for many Indian ICUs, where fragmented records, variable laboratory access, and staffing constraints are the norm.  \nIn parallel, Diwan et al. have reported explainable machinelearning models for mortality prediction in sepsis patients in a tertiary-care ICU in an LMIC setting, using SHAP values to link model outputs to familiar clinical features.4 Thiele et al. showed that real-time machine-learning models for ICU deterioration can be prospectively implemented across multiple centers when they are built as continuously updateable systems, with explicit attention to calibration drift and external validation.5 Together, these papers shift the focus from “best AUROC” to system performance overtime and to whether predictions can be trusted and acted upon at the bedside.  \nBuilding on Shanmugam et al.,1 we suggest that future IJCCM contributions on AI-enabled sepsis prediction make four elements explicit:  \n• Minimal, standardized dataset: Define a low-burden, consensus variable set (demographics, basic hemodynamics, routine laboratories) that most Indian ICUs can capture reliably, enabling  \n1,3Department of Allied Health Science, Chandigarh University, Mohali, Punjab, India  \n2Department of Medical Laboratory Technology, Faculty of Paramedical Sciences, Assam DownTown University, Guwahati, Assam, India Corresponding Author: Mulavagili Vijayasimha, Department of Allied Health Science, Chandigarh University, Mohali, Punjab, India, Phone:  \n+91 9717576987, [e-mail: mvsimha@gmail.com](e-mail: mvsimha@gmail.com)  \nHow to cite this article: Vijayasimha M, Jayarajan D, Mir ZR. From Narrative to Navigation—A Translational Roadmap for AI-enabled Early Sepsis Prediction: Comment on Shanmugam et al. Indian J Crit Care Med 2025;29(12):1050–1051 .  \nSource of support: Nil  \nConflict of interest: None  \nmulticenter external validation and head-to-head comparison of candidate models.  \n• Explainability and uncertainty by design: Require that models report not only discrimination and calibration but also how predictions are generated (e.g., SHAP-based feature importance) and how uncertainty is quantified, following emerging XAI sepsis and BSI work.2,4  \n• Prospective, workflow-aware evaluation: Encourage pilots where AI-generated sepsis risk scores are integrated with existing sepsis bundlesand","cbCaimMAwKzhsOdW","https://ap.wps.com/l/cbCaimMAwKzhsOdW","pdf",179942,"English","# Letter to the Editor\n## Overview and relevance\n## Explainable AI and system performance\n## Proposed elements for future contributions\n## Conclusion and next steps","[{\"question\":\"What is the main purpose of this letter?\",\"answer\":\"To comment on Shanmugam et al.’s scoping review and propose a concrete translational roadmap for deploying AI tools for early sepsis prediction in real-world ICUs.\"},{\"question\":\"Which design choices are highlighted for making AI usable in resource-constrained ICUs?\",\"answer\":\"Using minimal standardized datasets, prioritizing interpretability and uncertainty reporting by design, and ensuring models are implementable with limited real-time data.\"},{\"question\":\"How should future evaluations be structured according to the letter?\",\"answer\":\"With prospective, workflow-aware pilot studies integrating AI risk scores with existing sepsis bundles and early warning scores, using predefined impact metrics like time-to-antibiotics and ICU resource use.\"}]","From Narrative to Navigation - A Translational Roadmap for AI-enabled Early Sepsis Prediction - Comment | PDF",1790729330]