AIMC Topic: Hospitalization

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Multimodal deep learning for COVID-19 prognosis prediction in the emergency department: a bi-centric study.

Scientific reports
Predicting clinical deterioration in COVID-19 patients remains a challenging task in the Emergency Department (ED). To address this aim, we developed an artificial neural network using textual (e.g. patient history) and tabular (e.g. laboratory value...

Doctors Identify Hemorrhage Better during Chart Review when Assisted by Artificial Intelligence.

Applied clinical informatics
OBJECTIVES: This study evaluated if medical doctors could identify more hemorrhage events during chart review in a clinical setting when assisted by an artificial intelligence (AI) model and medical doctors' perception of using the AI model.

Machine learning to predict poor school performance in paediatric survivors of intensive care: a population-based cohort study.

Intensive care medicine
PURPOSE: Whilst survival in paediatric critical care has improved, clinicians lack tools capable of predicting long-term outcomes. We developed a machine learning model to predict poor school outcomes in children surviving intensive care unit (ICU).

Artificial intelligence-driven prediction of COVID-19-related hospitalization and death: a systematic review.

Frontiers in public health
AIM: To perform a systematic review on the use of Artificial Intelligence (AI) techniques for predicting COVID-19 hospitalization and mortality using primary and secondary data sources.

Supporting SURgery with GEriatric Co-Management and AI (SURGE-Ahead): A study protocol for the development of a digital geriatrician.

PloS one
INTRODUCTION: Geriatric co-management is known to improve treatment of older adults in various clinical settings, however, widespread application of the concept is limited due to restricted resources. Digitalization may offer options to overcome thes...

Machine learning functional impairment classification with electronic health record data.

Journal of the American Geriatrics Society
BACKGROUND: Poor functional status is a key marker of morbidity, yet is not routinely captured in clinical encounters. We developed and evaluated the accuracy of a machine learning algorithm that leveraged electronic health record (EHR) data to provi...

Confidence-based laboratory test reduction recommendation algorithm.

BMC medical informatics and decision making
BACKGROUND: We propose a new deep learning model to identify unnecessary hemoglobin (Hgb) tests for patients admitted to the hospital, which can help reduce health risks and healthcare costs.

The implementation of a real time early warning system using machine learning in an Australian hospital to improve patient outcomes.

Resuscitation
BACKGROUND: Early Warning Scores (EWS) monitor inpatient deterioration predominantly using vital signs. We evaluated inpatient outcomes after implementing an Artificial Intelligence (AI) based intervention in our local EWS.

Explainable prediction of daily hospitalizations for cerebrovascular disease using stacked ensemble learning.

BMC medical informatics and decision making
BACKGROUND: With the prevalence of cerebrovascular disease (CD) and the increasing strain on healthcare resources, forecasting the healthcare demands of cerebrovascular patients has significant implications for optimizing medical resources.