ArticleFrontiers in public health2022
Critical Care Database Comprising Patients With Infection.
Article in Frontiers in public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Towards Accurate and Reliable ICU Outcome Prediction: A Multimodal Learning Framework Based on Belief Function Theory using Structured EHRs and Free-Text Notes.Journal of healthcare informatics research · 2026Article
- Predictive performance of blood urea nitrogen, cystatin C, and their ratio for in-hospital mortality in sepsis: a Chinese two-center analysis.Open medicine (Warsaw, Poland) · 2026Article
- Association between the Endothelial Activation and Stress Index and all-cause mortality in patients with chronic obstructive pulmonary disease.Frontiers in medicine · 2026Article
- Deriving consensus sepsis clusters via goal-directed subgroup identification in multi-omics study.Nature communications · 2025Article
- BOBM: an adaptive deep learning framework for extended-window sepsis prediction with cross-institutional generalizability.Frontiers in medicine · 2025Article
- Investigating computational models for diagnosis and prognosis of sepsis based on clinical parameters: Opportunities, challenges, and future research directions.Journal of intensive medicine · 2024Review
- Article
- Interpretability-based machine learning for predicting the risk of death from pulmonary inflammation in Chinese intensive care unit patients.Frontiers in medicine · 2024Article
- The relationship between dexmedetomidine administration and prognosis in patients with sepsis-induced coagulopathy: a retrospective cohort study.Frontiers in pharmacology · 2024Article
- Exploring disease axes as an alternative to distinct clusters for characterizing sepsis heterogeneity.Intensive care medicine · 2023Article
- A generalizable and interpretable model for mortality risk stratification of sepsis patients in intensive care unit.BMC medical informatics and decision making · 2023Article
- Development of a nomogram model for the early prediction of sepsis-associated acute kidney injury in critically ill patients.Scientific reports · 2023Observational
- Establishment of a Chinese critical care database from electronic healthcare records in a tertiary care medical center.Scientific data · 2023Article
- Chinese experts' consensus on the application of intensive care big data.Frontiers in medicine · 2023Article
- Developing an ensemble machine learning model for early prediction of sepsis-associated acute kidney injury.iScience · 2022Article
- A prediction and interpretation machine learning framework of mortality risk among severe infection patients with pseudomonas aeruginosa.Frontiers in medicine · 2022Article
- A Nomogram to Better Predict the In-Hospital Mortality of Trauma Patients with Sepsis in the Intensive Care Unit.International journal of clinical practice · 2022Article
- Machine learning models to predict in-hospital mortality in septic patients with diabetes.Frontiers in endocrinology · 2022Article
- Relationship of Admission Serum Anion Gap and Prognosis of Critically Ill Patients: A Large Multicenter Cohort Study.Disease markers · 2022Article
- Artificial intelligence-assisted phenotyping of sepsis: Research progress, clinical challenges, and translational prospects.Digital healthReview
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Patients treated in the intensive care unit (ICU) are closely monitored and receive intensive treatment. Such aggressive monitoring and treatment will generate high-granularity data from both electronic healthcare records and nursing charts. These data not only provide infrastructure for daily clinical practice but also can help to inform clinical studies. It is technically challenging to integrate and cleanse medical data from a variety of sources. Although there are several open-access critical care databases from western countries, there is a lack of this kind of database for Chinese adult patients. We established a critical care database involving patients with infection. A large proportion of these patients have sepsis and/or septic shock. High-granularity data comprising laboratory findings, baseline characteristics, medications, international statistical classification of diseases (ICD) code, nursing charts, and follow-up results were integrated to generate a comprehensive database. The database can be utilized for a variety of clinical studies. The dataset is fully accessible at PhysioNet(https://physionet.org/content/icu-infection-zigong-fourth/1.0/).
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.