RICD: Russian Intensive Care Dataset

In the era of healthcare digital transformation, the scientific community faces the need for structured and available datasets for research and technological projects in the field of artificial intelligence, related to the development of new diagnostic and treatment methods. Objective: to develop a dataset containing anonymized medical data of all patients treated at the Federal Research and Clinical Center of Intensive Care Medicine and Rehabilitology (FRCCR), and provide access for doctors and scientists of FRCCR and other centers to structured patient data for subsequent analysis and research. Materials and Methods. The FRCCR medical information system and the tools «Asclepius», PL/SQL, Microsoft Office Excel, Power Query M, Microsoft PowerBI, Open data editor, and Python were used for data collection and representation. To provide open access to the dataset and protect the personal data of patients, the information was anonymized. Results. We introduce the RICD (Russian Intensive Care Dataset, https://fnkcrr-database.ru/) — the first dataset of intensive care patients in the Russian Federation, developed at FRCCR based on advanced principles and methods used in international open database projects — «eICU Program» from Philips Healthcare, «MIMIC-IV», and «MIMIC-III». The developed dataset contains information on 7,730 hospitalizations of 5,115 patients (including readmissions), covering data from 3,291 hospitalizations in the intensive care units (ICUs). The total number of records in the RICD exceeds 14 million. The RICD presents medical-anthropometric data, patient movement within the institution, diagnoses, information on therapy provided, results of laboratory tests, scale assessments, and outcomes of hospitalization. RICD also contains data on several vital parameters collected from bedside monitors and other equipment of ICUs, with up to 10 evaluations per hour. Conclusion. The RICD allows for in-depth analysis and research of clinical practices in intensive care, enabling the development of clinical decision support tools and the application of machine learning methods to enhance diagnostic tools and improve patient outcomes. With its accessibility and detailed data structure, the dataset serves as a valuable tool for both scientific research and practical applications in intensive care.