Research On Key Technologies Of Artificial Intelligence Dynamic Early Warning For Multiple Organ Dysfunction Syndrome

Multiple Organ Dysfunction Syndrome(MODS)has been the most common underlying cause of death from combat trauma since the Korean War and Vietnam War.It has a high mortality rate,a complex etiology,and the possibility of dysfunction in all tissues and organs of the body,and is also unpredictable.The current method of identifying MODS is based on biomarker method and diagnostic score method,but it still faces objective problems such as long identification time,need of test equipment support,and labor consumption.To address these problems,this thesis proposes an artificial intelligence dynamic early warning method using "multi-source data fusion + artificial intelligence + dynamic early warning",aiming to achieve MODS early warning identification in out-of-hospital environments such as emergency warfare and natural disaster rescue,and to improve the efficiency of warfare rescue and the quality of emergency medical rescue..It mainly involves the research,exploration and implementation of key technologies such as clinical big data mining and standardization,dynamic early warning model construction and comprehensive evaluation,and interpretable analysis of artificial intelligence-based auxiliary diagnosis model,which provides accurate auxiliary tools for clinical decisionmaking of MODS.This study innovatively established a set of data processing and standardization methods for MODS to address current issues such as missing data and uneven data quality in medical databases.The method uses a combination of rule interpolation and fixed value interpolation to achieve reasonable interpolation of 45 common clinical data types,including blood gas,biochemical,blood routine,and bedside monitoring.The method also complies with current clinical standards and indicator requirements,providing a data foundation for MODS early warning and recognition.This study innovatively proposed and verified an algorithm that can achieve MODS early warning and recognition using only non-invasive parameters,without the need for clinical laboratory indicators which is currently necessary for MODS recognition.The 8-hour MODS early warning and recognition algorithm achieved an AUC value of 0.830.The study also compared the performance changes of the model when using all parameters versus no parameters,verifying the optimal combination of recognition accuracy and minimal data support to achieve a balance between accuracy and data volume.Finally,the study compared the algorithm with four common MODS scoring methods including SOFA,LODS,Marshall MODS,and QSOFA,further demonstrating the ability of the algorithm for MODS early warning recognition.The use of non-invasive parameters in the MODS early warning recognition algorithm can be widely applied in remote areas without laboratory equipment support,such as battlefields and disaster relief sites,broadening the application scenarios of clinical decision-making methods and providing new ideas for disease recognition and warning.Building on this,the study introduced the concept of dynamic time and applied it to the early warning recognition algorithm.Through extensive experiments,the study established the theoretical relationship between MODS early warning recognition algorithm results and the length of patient stay in the hospital,validating the real-time and dynamic nature of the algorithm.The study achieved hourly-level MODS dynamic early warning recognition in the MIMIC IV database and theoretically,using high-frequency databases,could achieve second-level MODS early warning recognition.To address the issues of low transferability and poor interpretability,the study innovatively introduced incremental learning technology to update the original algorithm through incremental training with small batch data,enabling the model to be compatible with data from different databases.The SHAP method was used to explain the implementation process of the MODS early warning recognition algorithm,and the analysis of existing errors was conducted.The results showed that the non-invasive parameter-based MODS early warning recognition algorithm proposed in this study is consistent with current clinical experience,demonstrating the accuracy and reliability of the algorithm.Therefore,it can serve as a clinical decision-making tool for MODS recognition warning.Finally,the MODS intelligent early warning recognition system was established,providing a visual display platform for MODS early warning recognition.It achieved intercommunication with the monitor at the data level and expanded the functions of existing monitors.Through preliminary evaluations and trials by three medical institutions,it has been demonstrated that the system has strong research significance and practical value,laying the foundation for the future application of MODS early warning recognition algorithms.The research on the key technology of artificial intelligence dynamic early warning for MODS,through the study of data interpolation,data fusion,model construction,interpretive analysis and other techniques,proposed and validated a non-invasive parameter-based disease early warning recognition method and a dynamic real-time early warning machine learning model,validated by extensive experimental and theoretical analysis,proved the method in different scenarios,different databases The algorithm is validated by extensive experimental and theoretical analysis.The proposed algorithm provides a new idea for the research related to disease recognition and early warning,which is beneficial to the development and popularization of disease warning and recognition technology.