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References from Time-Series modeling for predicting mortality risk in intensive care unit patients with pulmonary inflammation. Local targets link to admitted publications; unresolved targets remain external evidence.
Incidence and trends of nosocomial infection in a tertiary general hospital in China from 2018 to 2021: a retrospective observational study
10.26355/eurrev_202307_33146 · 2023 · External reference
Healthcare associated infection and its risk factors among patients admitted to a tertiary hospital in Ethiopia: longitudinal study
10.1186/s13756-017-0298-5 · 2018 · External reference
Estimation of additional costs in patients with ventilator-associated pneumonia
10.3390/antibiotics13010002 · 2024 · External reference
Early pneumonia diagnosis decreases ventilator-associated pneumonia rates in trauma population
10.1097/ta.0000000000003808 · 2023 · External reference
Inspiratory effort and lung mechanics in spontaneously breathing patients with acute respiratory failure due to COVID-19: a matched control study
10.1164/rccm.202104-1029le · 2021 · External reference
Lung function during and after acute respiratory infection in COVID-19 positive and negative outpatients
10.1183/13993003.02837-2021 · 2022 · External reference
Risk factors for COVID-19-related death, hospitalization and intensive care: a population-wide study of all inhabitants in Stockholm
10.1007/s10654-021-00840-7 · 2022 · External reference
Significance of body temperature in elderly patients with sepsis
10.1186/s13054-020-02976-6 · 2020 · External reference
Serum albumin as a risk factor for death in patients with prolonged sepsis: an observational study
10.1016/j.jcrc.2019.02.004 · 2019 · External reference
Mixed shock complicating cardiogenic shock: frequency, predictors, and clinical outcomes
10.1161/circheartfailure.123.011404 · 2024 · External reference
Derivation and validation of a novel severity scoring system for pneumonia at intensive care unit admission
10.1093/cid/ciaa183 · 2021 · External reference
Machine learning-based prediction of in-ICU mortality in pneumonia patients
10.1038/s41598-023-38765-8 · 2023 · External reference
The prognostic performance of qSOFA for community-acquired pneumonia
10.1186/s40560-018-0307-7 · 2018 · External reference
“Why should I trust you?”: explaining the predictions of any classifier [Z]
2016 · External reference
From patterns to patients: advances in clinical machine learning for cancer diagnosis, prognosis, and treatment
10.1016/j.cell.2023.01.035 · 2023 · External reference
The value of machine and deep learning in management of critically ill patients: an umbrella review
10.1016/j.ijmedinf.2025.106081 · 2025 · External reference
Prediction of mortality events of patients with acute heart failure in intensive care unit based on deep neural network
10.1016/j.cmpb.2024.108403 · 2024 · External reference
Predicting ICU mortality in acute respiratory distress syndrome patients using machine learning: the predicting outcome and STratifiCation of severity in ARDS (POSTCARDS). Study
10.1097/ccm.0000000000006030 · 2023 · External reference
A novel higher performance nomogram based on explainable machine learning for predicting mortality risk in stroke patients within 30 days based on clinical features on the first day ICU admission
10.1186/s12911-024-02547-7 · 2024 · External reference
Prognostic assessment of COVID-19 in the intensive care unit by machine learning methods Model Development and Validation
10.2196/23128 · 2020 · External reference
Machine learning models for predicting in-hospital mortality in acute aortic dissection patients
10.3389/fcvm.2021.727773 · 2021 · External reference
Performance of various pneumonia severity models for predicting adverse outcomes in elderly inpatients with community-acquired pneumonia
10.1016/j.cmi.2024.07.008 · 2024 · External reference
Machine learning models to evaluate mortality in pediatric patients with pneumonia in the intensive care unit
10.1002/ppul.26897 · 2024 · External reference
Risk factors analysis and prediction model construction for severe pneumonia in older adult patients
10.3389/fpubh.2024.1399470 · 2024 · External reference
Explainable machine learning models for early Alzheimer's disease detection using multimodal clinical data
10.1016/j.ijmedinf.2025.106093 · 2025 · External reference
MIMIC-IV, a freely accessible electronic health record dataset
10.1038/s41597-022-01899-x · 2023 · External reference
The eICU collaborative research database, a freely available multi-center database for critical care research
10.1038/sdata.2018.178 · 2018 · External reference
Guidelines for developing and reporting machine learning predictive models in biomedical research: a multidisciplinary view
10.2196/jmir.5870 · 2016 · External reference
TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods
10.1136/bmj-2023-078378 · 2024 · External reference
PROBAST+AI: an updated quality, risk of bias, and applicability assessment tool for prediction models using regression or artificial intelligence methods
10.1136/bmj-2024-082505 · 2025 · External reference
SMOTE: synthetic minority over-sampling technique
10.1613/jair.953 · 2002 · External reference
A hybrid sampling algorithm combining M-SMOTE and ENN based on random forest for medical imbalanced data
10.1016/j.jbi.2020.103465 · 2020 · External reference
Explainable machine-learning predictions for the prevention of hypoxaemia during surgery
10.1038/s41551-018-0304-0 · 2018 · External reference
A web-based calculator to predict early death among patients with bone metastasis using machine learning techniques: development and validation study
10.2196/47590 · 2023 · External reference
Childhood mortality due to pneumonia; evidence from a tertiary paediatric referral center in Sri Lanka
10.1186/s12889-024-20883-1 · 2024 · External reference
Severe pneumonia in PICU admissions: the pediatric acute and critical care medicine Asian network (PACCMAN) observational cohort study, 2020–2022
10.1097/pcc.0000000000003598 · 2024 · External reference
Machine learning-enabled risk prediction of chronic obstructive pulmonary disease with unbalanced data
10.1016/j.cmpb.2023.107340 · 2023 · External reference
What determines outcomes in multivalve reoperations? Effect of patient and surgical complexity
10.1016/j.jtcvs.2023.12.005 · 2024 · External reference
Machine learning-based delta check method for detecting misidentification errors in tumor marker tests
10.1515/cclm-2023-1185 · 2024 · External reference
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
10.1038/s42256-019-0048-x · 2019 · External reference
Gas exchange and ventilation-perfusion relationships in the lung
10.1183/09031936.00037014 · 2014 · External reference
Pathophysiology and clinical meaning of ventilation-perfusion mismatch in the acute respiratory distress syndrome
10.3390/biology12010067 · 2023 · External reference
Sepsis and septic shock: endothelial molecular pathogenesis associated with vascular microthrombotic disease
10.1186/s12959-019-0198-4 · 2019 · External reference
Endothelial dysfunction and immunothrombosis in sepsis
10.3389/fimmu.2023.1144229 · 2023 · External reference
Prognostic value of Serum albumin level in critically ill patients: observational data from large intensive care unit databases
10.3389/fnut.2022.770674 · 2022 · External reference
Lactate dehydrogenase-to-albumin ratio as a predictor of 28-day mortality in critically Ill patients with gastrointestinal cancers: insights from machine learning and the medical information mart for intensive care IV database
10.1177/20552076251395708 · 2025 · External reference
Pathophysiology of acute respiratory distress syndrome and COVID-19 lung injury
10.1016/j.ccc.2021.05.003 · 2021 · External reference
Sepsis-Induced coagulopathy: a comprehensive narrative review of pathophysiology, clinical presentation, diagnosis, and management strategies
10.1213/ane.0000000000006888 · 2024 · External reference
Crosstalk between the coagulation and complement systems in sepsis
10.1016/j.thromres.2014.03.014 · 2014 · External reference
Sepsis-Induced coagulopathy: an update on pathophysiology, biomarkers, and current guidelines
10.3390/life13020350 · 2023 · External reference
Predicting 30-day mortality of patients with pneumonia in an emergency department setting using machine-learning models
10.15441/ceem.19.052 · 2020 · External reference
Identification of time-series pattern marker in its application to mortality analysis of pneumonia patients in intensive care unit
10.3390/jpm14080812 · 2024 · External reference
Explainable time-series deep learning models for the prediction of mortality, prolonged length of stay and 30-day readmission in intensive care patients
10.3389/fmed.2022.933037 · 2022 · External reference
Dynamic survival prediction in intensive care units from heterogeneous time series without the need for variable selection or curation
10.1038/s41598-020-79142-z · 2020 · External reference
Learning to predict in-hospital mortality risk in the intensive care unit with attention-based temporal convolution network
10.1186/s12871-022-01625-5 · 2022 · External reference
