AIMC Topic: Inpatients

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Evaluation and comparison of machine learning algorithms for predicting discharge against medical advice in injured inpatients.

Surgery
BACKGROUND: Whether the application of machine learning algorithms offers an advantage over logistic regression in forecasting discharge against medical advice occurrences needs to be evaluated.

Utilizing machine learning algorithms for predicting Anxiety-Depression Comorbidity Syndrome in Gastroenterology Inpatients (ADCS-GI).

BMC psychiatry
BACKGROUND: Accurately diagnosing Anxiety-Depression Comorbidity Syndrome in Gastroenterology Inpatients (ADCS-GI) shows significant challenges as traditional diagnostic methods fail to meet expectations due to patient hesitance and non-psychiatric h...

Fast screening of COVID-19 inpatient samples by integrating machine learning and label-free SERS methods.

Analytica chimica acta
BACKGROUND: Advances in bio-analyte detection demonstrate the need for innovation to overcome the limitations of traditional methods. Emerging viruses evolve into variants, driving the need for fast screening to minimize the time required for positiv...

Machine learning based on nutritional assessment to predict adverse events in older inpatients with possible sarcopenia.

Aging clinical and experimental research
BACKGROUND: The accuracy of current tools for predicting adverse events in older inpatients with possible sarcopenia is still insufficient to develop individualized nutrition-related management strategies. The objectives were to develop a machine lea...

Constructing a fall risk prediction model for hospitalized patients using machine learning.

BMC public health
STUDY OBJECTIVES: This study aimed to identify the risk factors associated with falls in hospitalized patients, develop a predictive risk model using machine learning algorithms, and evaluate the validity of the model's predictions.

Machine-learning-based cost prediction models for inpatients with mental disorders in China.

BMC psychiatry
BACKGROUND: Mental disorders are increasingly prevalent, leading to increased medical expenditures. To refine the reimbursement of medical costs for inpatients with mental disorders by health insurance, an accurate prediction model is essential. Per-...

Predicting Discharge Destination From Inpatient Rehabilitation Using Machine Learning.

American journal of physical medicine & rehabilitation
Predicting discharge destination for patients at inpatient rehabilitation facilities is important as it facilitates transitions of care and can improve healthcare resource utilization. This study aims to build on previous studies investigating discha...

Mortality prediction of inpatients with NSTEMI in a premier hospital in China based on stacking model.

PloS one
BACKGROUND: Acute myocardial infarction (AMI) remains a leading cause of hospitalization and death in China. Accurate mortality prediction of inpatient is crucial for clinical decision-making of non-ST-segment elevation myocardial infarction (NSTEMI)...

Exploring correlates of high psychiatric inpatient utilization in Switzerland: a descriptive and machine learning analysis.

BMC psychiatry
BACKGROUND: This study investigated socio-demographic, psychiatric, and psychological characteristics of patients with high versus low utilization of psychiatric inpatient services. Our objective was to better understand the utilization pattern and t...

Extracting social determinants of health from inpatient electronic medical records using natural language processing.

Journal of epidemiology and population health
BACKGROUND: Social determinants of health (SDOH) have been shown to be important predictors of health outcomes. Here we developed methods to extract them from inpatient electronic medical record (EMR) data using techniques compatible with current EMR...