在醫院環境中，能夠及早發現病患病情的惡化，避免錯過黃金的治療時機是非常重要的一件事。尤其成本低廉、容易蒐集的生命體徵，是評估病人健康狀態的關鍵指標。然而，過往的生命體徵分析研究中，通常獨立分析每個生命體徵的波形，忽略了生命體徵內部波動之間的關聯性，以及不同生命體徵之間的相關性。因此，這些方法無法有效的在早期就識別出細微的病情惡化。
為了解決這些限制，我們提出了以知識圖鄰近資訊整合為導向（COKI）的生命體徵的表徵學習框架。此框架將生命體徵內部的動態，以及跨生命體徵之間的關聯性，分別建模成「領域內」，以及「跨領域」的知識。COKI 首先透過多視角知識擴增的技術，整合各個領域內，以及跨領域之間的資訊，來捕捉生命體徵各層面的深層知識關聯，增強捕捉細微病情變化的能力。為了推判跨領域中，知識間不確定的相關性強度，COKI 將擴增的知識導入基於貝式排名的策略，使 COKI 能夠有效地估計跨領域知識間的相關性強度，並透過這些相關性，引導聯合多重知識圖的建模，加強生命體徵的表達，從而更全面解讀病人的健康狀況。
我們在真實世界的 ICU 數據集上，進行的多種實驗表明，COKI 在分類性能以及及早性之間，達成有效的平衡，顯著超越了現有方法在檢測細微病情惡化的能力。此外，我們多個案例研究進一步展示了其在不同病患族群上的有效性，為持續監測患者病情變化的技術，提供了廣泛應用的視角。

Early detection of patient deterioration is essential for timely clinical intervention in hospital settings. Economical vital signs are critical indicators in assessing a patient’s health status. Existing methods typically analyze vital sign waveforms in isolation, disregarding the complex intra- and interdependencies among different vital signs, leading to missing subtle, early-stage deterioration in health. To overcome these limitations, we propose Context-Oriented Knowledge Integration (COKI), a vital sign representation learning model that distinctly organizes the intra-correlation within vital signs as ”intra-domain” knowledge and their cross-correlation as ”cross-domain” knowledge. Specifically, COKI effectively integrates these domain knowledge through multi-view context augmentation, which enhances profound insights into nuanced health changes. To infer the uncertain correlation strength between cross-domain knowledge, COKI employs a Bayesian-based ranking strategy based on augmented knowledge from both intra-domain and cross-domain sources. This integration enables COKI to effectively estimate correlation strengths within cross-domain contexts, instructing joint modeling of multiple knowledge graphs and refining vital sign representations for a more precise interpretation of patient health. Our extensive experiments on a real-world ICU dataset demonstrate that COKI noticeably surpasses existing methods in detecting subtle health deterioration and strikes a balance between classification effectiveness and earliness. Additionally, our several case study further shows its potential on various patient populations in hospitals, providing a broader applicability view into patient monitoring.

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