ABSTRACT
On?the?premise?of?fully?studying?the?disaster?medical?rescue?monitoring?mechanism?in?emergencies?at?home?and?abroad,?the?functional?requirements?of?the?domestic?disaster?medical?rescue?monitoring?system?was?analyzed?in?this?paper,?the?logical?framework?and?data?structure?of?disaster?medical?rescue?monitoring?system?with?privacy?protection?mechanism?was?designed?by?department?of?emergency?in?Chinese?PLA?General?Hospital,?department?of?information?management?in?School?of?Economics?and?Management?of?Beijing?Jiaotong?University,?the?School?of?Information?Management?of?Nanjing?University.?Three?major?functional?modules?were?realized?in?the?system:?reporter?information?management,?disaster?medical?rescue?data?upload,?and?disaster?medical?rescue?data?search.?Android?client?and?Web?client?were?developed?for?easy?access?to?the?system.?The?system?also?had?the?function?of?privacy?protection.?Based?on?symmetric?searchable?encryption?algorithm,?the?system?realized?the?encryption?storage?of?untrusted?servers?and?ensured?the?security?of?medical?and?health?data.?It?is?beneficial?for?the?further?development?and?improvement?of?disaster?medical?rescue?data?collection?in?China.
ABSTRACT
Medical big data is a hot research topic in China,and it is also the main research direction in the field of emergency medicine.The current situation of the construction of the first-aid big data platform and the construction of the first-aid clinical decision support system were analyzed,the problems existing in the development of the first-aid big data research field were enumerated,to explore the theoretical methods for promoting the development of domestic first-aid big data,so as to provide references for the research in related fields.
ABSTRACT
The analysis of big data in medical field cannot be isolated from the high quality clinical database, and the construction of first aid database in our country is still in the early stage of exploration. This paper introduces the idea and key technology of the construction of multi-parameter first aid database. By combining emergency business flow with information flow, an emergency data integration model was designed with reference to the architecture of the Medical Information Mart for Intensive Care III (MIMIC-III), created by Computational Physiology Laboratory of Massachusetts Institute of Technology (MIT), and a high-quality first-aid database was built. The database currently covers 22 941 medical records for 19 814 different patients from May 2015 to October 2017, including relatively complete information on physiology, biochemistry, treatment, examination, nursing, etc. And based on the database, the first First-Aid Big Data Datathon event, which 13 teams from all over the country participated in, was launched. The First-Aid database provides a reference for the construction and application of clinical database in China. And it could provide powerful data support for scientific research, clinical decision making and the improvement of medical quality, which will further promote secondary analysis of clinical data in our country.
Subject(s)
Humans , Big Data , Critical Care , Databases, Factual , Medical InformaticsABSTRACT
Objective To propose a method of prediction for fatal gastrointestinal bleeding recurrence in hospital and a method of feature selection via machine learning models. Methods 728 digestive tract hemorrhage samples were extracted from the first aid database of PLA General Hospital, and 343 patients among them were diagnosed as fatal gastrointestinal bleeding recurrence in hospital. A total of 64 physiological or laboratory indicators were extracted and screened. Based on the ten-fold cross-validation, Logistic regression, AdaBoost and XGBoost were used for classification prediction and comparison. XGBoost was used to search sequence features, and the key indicators for predicting fatal gastrointestinal bleeding recurrence in hospital were screened out according to the importance of the indicators during training. Results Logistic regression, AdaBoost and XGBoost all get better F1.5 score under each feature input dimension, among which XGBoost had the best effect and the highest score, which was able to identify as many patients as possible who might have fatal gastrointestinal bleeding recurrence in hospital. Through XGBoost iteration results, the Top 30 indicators with high importance for predicting fatal gastrointestinal bleeding recurrence in hospital were ranked. The F1.5 scores of the first 12 key indicators peaked at iteration (0.893), including hemoglobin (Hb), calcium (CA), red blood cell count (RBC), mean platelet volume (MPV), mean erythrocyte hemoglobin concentration (MCH), systolic blood pressure (SBP), platelet count (PLT), magnesium (MG), lymphocyte (LYM), glucose (GLU, blood gas analysis), glucose (GLU, blood biochemistry) and diastolic blood pressure (DBP). Conclusions Logistic regression, AdaBoost and XGBoost could achieve the purpose of early warning for predicting fatal gastrointestinal bleeding recurrence in hospital, and XGBoost is the most suitable. The 12 most important indicators were screened out by sequential forward selection.
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Objective To construct a database containing multiple kinds of diseases that can provide "real world"data for first-aid clinical research. Methods Structured or non-structured information from hospital information system, laboratory information system, emergency medical system, emergency nursing system and bedside monitoring instruments of patients who visited department of emergency in PLA General Hospital from January 2014 to January 2018 were extracted. Database was created by forms, code writing, and data process. Results Emergency Rescue Database is a single center database established by PLA General Hospital. The information was collected from the patients who had visited the emergency department in PLA General Hospital since January 2014 to January 2018. The database included 530 585 patients' information of triage and 22 941 patients' information of treatment in critical rescue room, including information related to human demography, triage, medical records, vital signs, lab tests, image and biological examinations and so on. There were 12 tables (PATIENTS, TRIAGE_PATIENTS, EMG_PATIENTS_VISIT, VITAL_SIGNS, CHARTEVENTS, MEDICAL_ORDER, MEDICAL_RECORD, NURSING_RECORD, LAB_TEST_MASTER, LAB_RESULT, MEDICAL_EXAMINATION, EMG_INOUT_RECORD) that containing different kinds of patients' information. Conclusions The setup of high quality emergency databases lay solid ground for scientific researches based on data. The model of constructing Emergency Rescue Database could be the reference for other medical institutions to build multiple-diseases databases.
ABSTRACT
Medical practice generates and stores immense amounts of clinical process data, while integrating and utilization of these data requires interdisciplinary cooperation together with novel models and methods to further promote applications of medical big data and research of artificial intelligence. A "Datathon" model is a novel event of data analysis and is typically organized as intense, short-duration, competitions in which participants with various knowledge and skills cooperate to address clinical questions based on "real world" data. This article introduces the origin of Datathon, organization of the events and relevant practice. The Datathon approach provides innovative solutions to promote cross-disciplinary collaboration and new methods for conducting research of big data in healthcare. It also offers insight into teaming up multi-expertise experts to investigate relevant clinical questions and further accelerate the application of medical big data.
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Objective To study the distribution of diseases in Medical Information Mart for Intensive Care Ⅲ(MIMIC-Ⅲ) database in order to provide reference for clinicians and engineers who use MIMIC-Ⅲ database to solve clinical research problems. Methods The exploratory data analysis technologies were used to explore the distribution characteristics of diseases and emergencies of patients (excluding newborns) in MIMIC-Ⅲ database were explored; then, neonatal gestational age, weight, length of hospital stay in intensive care unit (ICU) were analyzed with the same method. Results In the MIMIC-Ⅲ database, 46 428 patients were admitted for the first time, and 49 214 ICU records were recorded. There were 26 076 males and 20 352 females; the median age was 60.5 (38.6, 75.6) years, and most patients were between 60 and 80 years old. The first diagnosis in the disease spectrum analysis was firstly ranked by circulatory diseases (32%), followed by injury and poisoning (14%), digestive system disease (8%), tumor (7%), respiratory disease (6%) and so on. Patients with ischemic heart disease accounted for the largest proportion of circulatory disease (42%), the proportion of these patients gradually increased with age of 60-70 years old, then decreased. However, the proportion of patients with cerebrovascular disease declined first and then increased with age, which was the main cause of death of circulatory system disease (ICU mortality was 22.5%). Injury and poisoning patients showed a significant decrease with age. Digestive system diseases were younger than the general population (most people aged between 50 to 60 years), and non-infectious enteritis and colitis were the main causes of death (ICU mortality was 18.3%). Respiratory infections were predominant in infected patients (34%), but circulatory system infections were the main cause of death (ICU mortality was 25.6%). Secondly, in the neonatal care unit, premature infants accounted for the vast majority (82%). As the gestational age increased, the duration of ICU was decreased, and the mortality was decreased. Conclusions The diseases distribution of patients can be provided by MIMIC-Ⅲ database, which helps to grasp the overview of the volume and age distribution of the target patients in advance, and carry out the next step of research. Meanwhile, it points out the important role of exploratory data analysis in electronic health records analysis.
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Objective The detailed analysis of the surveillance in post extreme emergencies and disasters (SPEED) provides practical reference for China to establish a disaster medical rescue information monitoring system with Chinese characteristics. Methods The SPEED system under the scene of disaster medical rescue information monitoring is analyzed in detail. The SPEED system design, work flows, system implementation and other aspects are analyzed and summarized in this paper, and suggests the enlightenment of SPEED system for Chinese disaster medical rescue information monitoring work. Results The SPEED system is an information monitoring system for the early stages of disasters. It provides monitoring for diseases caused by disasters, and life and health trends. It has a complete data collection mechanism, a comprehensive personnel training system, a complete system function, and an implementation strategy involving multi-layer, multi-region, and multi -sector. It is a powerful tool for disaster medical rescue and management personnel to obtain information in time. In the field of disaster medical rescue, a similar public-facing information monitoring system in China is still not perfect. Conclusion Learning the design flows and establishment mode of the SPEED system can provide reference for China to establish a disaster medical rescue information monitoring system with Chinese characteristics.
ABSTRACT
Objective 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. Methods 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. Results 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). Conclusions 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.