{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T06:58:08Z","timestamp":1785308288306,"version":"3.55.0"},"reference-count":46,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T00:00:00Z","timestamp":1785283200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Oncol."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>This study proposes the Counterfactual Risk Assessor, a deep learning framework integrating attention mechanisms for operating room nursing quality assessment and postoperative risk prediction in tumor surgery patients. The framework is motivated by the need to model heterogeneous perioperative information and dynamic clinical events more effectively than static risk assessment approaches.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>It contains three main modules: Low Dimensional Manifold Projection, Event driven Segmentation Router, and Probabilistic Outcome Modeler. The projection module learns compact representations from high dimensional perioperative data while preserving clinically relevant structure. The segmentation router identifies event associated time windows and emphasizes informative signals such as abnormal vital signs, nursing interventions, medication administration, transfusion, and postoperative observations. The outcome modeler then estimates future risk probabilities using attention based representations and uncertainty aware prediction. Counterfactual Pacing is used to examine plausible alternative perioperative scenarios, and uncertainty propagation is applied to quantify prediction confidence.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results and Discussion<\/jats:title>\n                    <jats:p>Experimental results show that the proposed framework achieves better accuracy, F1 score, AUROC, and AUPRC than clinical, statistical, machine learning, and deep learning baselines across the evaluated datasets. These findings suggest that event aware representation learning may provide useful support for postoperative risk stratification and nursing quality evaluation. The proposed framework should be regarded as a decision support method rather than a replacement for clinical judgment, and further prospective validation, external multicenter testing, and workflow integration are required before routine clinical application.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fonc.2026.1834175","type":"journal-article","created":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T05:58:00Z","timestamp":1785304680000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Application of deep learning models integrating attention mechanisms in operating room nursing quality and postoperative risk assessment for tumors"],"prefix":"10.3389","volume":"16","author":[{"given":"Zhiping","family":"Liu","sequence":"first","affiliation":[{"name":"School of Mathematics and Statistics, Shanxi Datong University, Datong","place":["Shanxi, 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