AI Medical Compendium Topic:
Electronic Health Records

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Application of a time-series deep learning model to predict cardiac dysrhythmias in electronic health records.

PloS one
BACKGROUND: Cardiac dysrhythmias (CD) affect millions of Americans in the United States (US), and are associated with considerable morbidity and mortality. New strategies to combat this growing problem are urgently needed.

Machine learning based early mortality prediction in the emergency department.

International journal of medical informatics
BACKGROUND: It is a great challenge for emergency physicians to early detect the patient's deterioration and prevent unexpected death through a large amount of clinical data, which requires sufficient experience and keen insight.

Influential Usage of Big Data and Artificial Intelligence in Healthcare.

Computational and mathematical methods in medicine
Artificial intelligence (AI) is making computer systems capable of executing human brain tasks in many fields in all aspects of daily life. The enhancement in information and communications technology (ICT) has indisputably improved the quality of pe...

Applying interpretable deep learning models to identify chronic cough patients using EHR data.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Chronic cough (CC) affects approximately 10% of adults. Many disease states are associated with chronic cough, such as asthma, upper airway cough syndrome, bronchitis, and gastroesophageal reflux disease. The lack of an ICD ...

A Review of Recent Work in Transfer Learning and Domain Adaptation for Natural Language Processing of Electronic Health Records.

Yearbook of medical informatics
OBJECTIVES: We survey recent work in biomedical NLP on building more adaptable or generalizable models, with a focus on work dealing with electronic health record (EHR) texts, to better understand recent trends in this area and identify opportunities...

Uncertainty-Gated Stochastic Sequential Model for EHR Mortality Prediction.

IEEE transactions on neural networks and learning systems
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...

Predicting mortality among patients with liver cirrhosis in electronic health records with machine learning.

PloS one
OBJECTIVE: Liver cirrhosis is a leading cause of death and effects millions of people in the United States. Early mortality prediction among patients with cirrhosis might give healthcare providers more opportunity to effectively treat the condition. ...

Modeling and prediction of pressure injury in hospitalized patients using artificial intelligence.

BMC medical informatics and decision making
BACKGROUND: Hospital-acquired pressure injuries (PIs) induce significant patient suffering, inflate healthcare costs, and increase clinical co-morbidities. PIs are mostly due to bed-immobility, sensory impairment, bed positioning, and length of hospi...

Artificial Intelligence Algorithm with ICD Coding Technology Guided by the Embedded Electronic Medical Record System in Medical Record Information Management.

Journal of healthcare engineering
The study aims to explore the application of international classification of diseases (ICD) coding technology and embedded electronic medical record (EMR) system. The study established an EMR information knowledge system and collected the data of pat...

Machine learning identifies ICU outcome predictors in a multicenter COVID-19 cohort.

Critical care (London, England)
BACKGROUND: Intensive Care Resources are heavily utilized during the COVID-19 pandemic. However, risk stratification and prediction of SARS-CoV-2 patient clinical outcomes upon ICU admission remain inadequate. This study aimed to develop a machine le...