AIMC Topic: Hospital Mortality
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Sentiment Analysis Based on the Nursing Notes on In-Hospital 28-Day Mortality of Sepsis Patients Utilizing the MIMIC-III Database.
Computational and mathematical methods in medicine
Oct 13, 2021
In medical visualization, nursing notes contain rich information about a patient's pathological condition. However, they are not widely used in the prediction of clinical outcomes. With advances in the processing of natural language, information begi...
Artificial intelligence forecasting mortality at an intensive care unit and comparison to a logistic regression system.
Einstein (Sao Paulo, Brazil)
Oct 11, 2021
OBJECTIVE: To explore an artificial intelligence approach based on gradient-boosted decision trees for prediction of all-cause mortality at an intensive care unit, comparing its performance to a recent logistic regression system in the literature, an...
Improved Prediction of Older Adult Discharge After Trauma Using a Novel Machine Learning Paradigm.
The Journal of surgical research
Oct 7, 2021
BACKGROUND: The ability to reliably predict outcomes after trauma in older adults (age ≥ 65 y) is critical for clinical decision making. Using novel machine-learning techniques, we sought to design a nonlinear, competing risks paradigm for prediction...
Clinically Distinct Subtypes of Acute Kidney Injury on Hospital Admission Identified by Machine Learning Consensus Clustering.
Medical sciences (Basel, Switzerland)
Sep 24, 2021
BACKGROUND: We aimed to cluster patients with acute kidney injury at hospital admission into clinically distinct subtypes using an unsupervised machine learning approach and assess the mortality risk among the distinct clusters.
Risk prediction of clinical adverse outcomes with machine learning in a cohort of critically ill patients with atrial fibrillation.
Scientific reports
Sep 23, 2021
Critically ill patients affected by atrial fibrillation are at high risk of adverse events: however, the actual risk stratification models for haemorrhagic and thrombotic events are not validated in a critical care setting. With this paper we aimed t...
Machine-learning-based COVID-19 mortality prediction model and identification of patients at low and high risk of dying.
Critical care (London, England)
Sep 8, 2021
BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic caused by the SARS-Cov2 virus has become the greatest health and controversial issue for worldwide nations. It is associated with different clinical manifestations and a high mortality rate...
Uncertainty-Gated Stochastic Sequential Model for EHR Mortality Prediction.
IEEE transactions on neural networks and learning systems
Aug 31, 2021
Electronic health records (EHRs) are characterized as nonstationary, heterogeneous, noisy, and sparse data; therefore, it is challenging to learn the regularities or patterns inherent within them. In particular, sparseness caused mostly by many missi...
Comparing COVID-19 risk factors in Brazil using machine learning: the importance of socioeconomic, demographic and structural factors.
Scientific reports
Aug 2, 2021
The COVID-19 pandemic continues to have a devastating impact on Brazil. Brazil's social, health and economic crises are aggravated by strong societal inequities and persisting political disarray. This complex scenario motivates careful study of the c...
Short- and long-term mortality prediction after an acute ST-elevation myocardial infarction (STEMI) in Asians: A machine learning approach.
PloS one
Aug 2, 2021
BACKGROUND: Conventional risk score for predicting short and long-term mortality following an ST-segment elevation myocardial infarction (STEMI) is often not population specific.
Deep learning to predict long-term mortality in patients requiring 7 days of mechanical ventilation.
PloS one
Jun 29, 2021
BACKGROUND: Among patients with acute respiratory failure requiring prolonged mechanical ventilation, tracheostomies are typically placed after approximately 7 to 10 days. Yet half of patients admitted to the intensive care unit receiving tracheostom...
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