Construction of an Artificial Intelligence Prognostic Prediction Model for Sepsis Based on Time Series Analysis
Construction of an Artificial Intelligence Prognostic Prediction Model for Sepsis Based on Time Series Analysis
Sepsis is a life-threatening organ dysfunction caused by a dysregulated host response to infection. It is one of the leading causes of death and disability worldwide, with an inpatient mortality rate of 10-20%. Sepsis is a severe complication in critically ill patients and can lead to septic shock and multiple organ dysfunction syndrome (MODS), usually triggered by severe trauma, surgery, and infections. Despite the availability of advanced diagnostic, therapeutic, and monitoring technologies, the incidence and mortality of sepsis remain high, posing a significant global challenge to the medical community. Over 49 million people worldwide develop sepsis annually, with approximately 11 million deaths, resulting in a mortality rate of about 15%-25%.
This study aims to develop a prognosis prediction model for sepsis patients using a neural network architecture (Transformer algorithm), based on time-series data. The primary outcome observed is the mortality outcome of sepsis patients. The goal of the research is to enhance the early identification of high-risk sepsis patients, thereby optimizing the timing of sepsis treatment and intervention and improving the accuracy of prognosis prediction for sepsis patients.
1. Research Background Sepsis is a life-threatening organ dysfunction caused by a dysregulated host response to infection. It is one of the leading causes of death and disability worldwide, with an inpatient mortality rate of 10-20%. Sepsis is a severe complication in critically ill patients and can lead to septic shock and multiple organ dysfunction syndrome (MODS), usually triggered by severe trauma, surgery, and infections. Despite the availability of advanced diagnostic, therapeutic, and monitoring technologies, the incidence and mortality of sepsis remain high, posing a significant global challenge to the medical community. Over 49 million people worldwide develop sepsis annually, with approximately 11 million deaths, resulting in a mortality rate of about 15%-25%.
2. Research Objectives and Content
Research Objective This study aims to develop a prognosis prediction model for sepsis patients using a neural network architecture (Transformer algorithm), based on time-series data. The primary outcome observed is the mortality outcome of sepsis patients. The goal of the research is to enhance the early identification of high-risk sepsis patients, thereby optimizing the timing of sepsis treatment and intervention and improving the accuracy of prognosis prediction for sepsis patients.
Research Content 2.1 Inclusion Criteria Patients diagnosed with sepsis at West China Hospital of Sichuan University from January 2020 to December 2023.
2.2 Exclusion Criteria 1) Age under 18 years; 2) Gender unknown; 3) Incorrect or invalid discharge diagnosis; 4) Hospitalization period less than 24 hours; 5) Missing data exceeds 30%. 2.3 Sample Size 3,000 cases. 2.4 Data to be Collected A retrospective analysis of the clinical data of patients diagnosed with sepsis at West China Hospital of Sichuan University from January 2020 to December 2023 will be conducted. The baseline data of patients (including age, gender, comorbidities, history of malignant tumors, lesion sites, pathological types, etc.), occurrence of severe complications, total hospital stay, survival time, and other relevant information will be summarized to build a time-series-based prognosis prediction model for sepsis mortality risk.
Clinical Research Ethical Principles and Requirements This clinical research will comply with the Declaration of Helsinki issued by the World Medical Association and the relevant regulations set forth by the National Health and Family Planning Commission of the People's Republic of China concerning the Ethical Review of Biomedical Research Involving Human Subjects. The research will only utilize retrospective medical records and/or specimens, with all personal identifiers removed. There will be no risk to the subjects, nor will it negatively impact their rights or health. Therefore, informed consent is waived. The research data will be stored at West China Hospital of Sichuan University, accessible to the researchers, supervising departments, and the ethics review committee. Any public reports related to the research findings will not disclose the personal identity of the subjects. We will make every effort within the legal framework to protect the privacy and personal medical information of the subjects.
Inclusion Criteria:
Exclusion Criteria: