AIMC Topic: Hospitalization

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Identifying real time surveillance indicators to estimate COVID-19 hospital admissions in Colorado during and after the public health emergency.

Scientific reports
Questions remain about how best to focus surveillance efforts for COVID-19 and other emerging respiratory diseases. We used an archive of COVID-19 data in Colorado from October 2020 to March 2024 to reconstruct seven real-time surveillance indicators...

Usability of machine learning algorithms based on electronic health records for the prediction of acute kidney injury and transition to acute kidney disease: A proof of concept study.

PloS one
BACKGROUND: Acute kidney injury (AKI) and acute kidney disease (AKD) are frequent complications of hospitalization, resulting in reduced outcomes and increased cost burden. However, these conditions are only sometimes recognized and promptly treated....

Machine learning methods, applications and economic analysis to predict heart failure hospitalisation risk: a scoping review.

BMJ open
BACKGROUND:  Machine Learning (ML) has been transformative in healthcare, enabling more precise diagnostics, personalised treatment regimens and enhanced patient care. In cardiology, ML plays a crucial role in risk prediction and patient stratificati...

Incorporating the STOP-BANG questionnaire improves prediction of cardiovascular events during hospitalization after myocardial infarction.

Scientific reports
Obstructive sleep apnea (OSA) may impact outcomes in acute coronary syndrome (ACS) patients. The Global Registry of Acute Coronary Events (GRACE) score assesses cardiovascular risk post-ACS. This study evaluated whether incorporating the STOP-BANG sc...

Machine Learning for Predicting Critical Events Among Hospitalized Children.

JAMA network open
IMPORTANCE: Unrecognized deterioration among hospitalized children is associated with a high risk of mortality and morbidity. The current approach to pediatric risk stratification is fragmented, as each hospital unit (emergency, ward, or intensive ca...

Using machine learning involving diagnoses and medications as a risk prediction tool for post-acute sequelae of COVID-19 (PASC) in primary care.

BMC medicine
BACKGROUND: The aim of our study was to determine whether the application of machine learning could predict PASC by using diagnoses from primary care and prescribed medication 1 year prior to PASC diagnosis.

Clinical assessment of the criticality index - dynamic, a machine learning prediction model of future care needs in pediatric inpatients.

PloS one
OBJECTIVE: To assess patient characteristics and care factors that are associated with correct and incorrect predictions of future care locations (ICU vs. non-ICU) by the Criticality Index-Dynamic (CI-D), with the goal of enhancing the CI-D.

Analyzing factors influencing hospitalization costs for five common cancers in China using neural network models.

Journal of medical economics
BACKGROUND: Malignant tumors are a major global health crisis, causing 25% of deaths in China, with lung, liver, thyroid, breast, and colon cancers being the most common. Understanding the factors influencing hospitalization costs for these cancers i...

Initial seizure episodes risk factors identification during hospitalization of ICU patients: A retrospective analysis of the eICU collaborative research database.

Journal of clinical neuroscience : official journal of the Neurosurgical Society of Australasia
BACKGROUND: We aimed to identify risk factors for initial seizure episodes in ICU patients using various machine learning algorithms.

Daily Automated Prediction of Delirium Risk in Hospitalized Patients: Model Development and Validation.

JMIR medical informatics
BACKGROUND: Delirium is common in hospitalized patients and is correlated with increased morbidity and mortality. Despite this, delirium is underdiagnosed, and many institutions do not have sufficient resources to consistently apply effective screeni...