Analysis of core indicators of fatal gastrointestinal rebleeding in emergency from the perspective of big data

Abstract

To explore a method of screening the core indicators in the emergency database that can be used to evaluate the in-hospital fatal gastrointestinal rebleeding by using the big data algorithm. Based on the emergency database of the Chinese PLA General Hospital, through the big data retrieval technology, all the 647 patients diagnosed as gastrointestinal bleeding in the emergency database were enrolled, except those who were admitted to the hospital for the first time and whose hemoglobin (Hb) was less than 90 g/L or did not undergo Hb test. Among them, there were 313 in the rebleeding group (fatal rebleeding in the hospital) and 334 in the non-rebleeding group (no fatal rebleeding in the hospital). General data of patients were collected, including gender, age, physical signs, blood gas, test index collection data, and the identification of gastrointestinal rebleeding. The fusion algorithm of rough set algorithm, genetic algorithm, and cellular automaton algorithm were used to calculate the key indicators that affect gastrointestinal rebleeding. A total of 499 indicators were calculated by machine fusion algorithm, after screening 5 times repeatedly, 24 key indicators were screened out, 3 of which were vital signs, including systolic blood pressure (SBP), diastolic blood pressure (DBP), temperature (T); 7 key indicators of blood routine, including white blood cell count (WBC), eosinophil (EOS), monocyte (MONO), Hb, hematocrit (HCT), red cell distribution width (RDW), mean corpuscular hemoglobin (MCH); 3 key indicators of coagulation, including prothrombin time (PT), plasma fibrinogen (FIB), activated partial thromboplastin time (APTT); 5 key indicators of biochemical, including myoglobin (MYO), chloride, glucose (GLU), serum albumin (ALB), total bilirubin (TBil); and 6 key indicators of blood gas, including pH, lactate (Lac), oxygen saturation (SO2), base excess (BE), bicarbonate (HCO3-), partial pressure of carbon dioxide (PaCO2). Using big data technology, 24 core indicators for evaluating the fatal gastrointestinal rebleeding in hospitals can be screened out from the emergency database, providing new ideas and methods for clinical diagnosis of the disease.