AI Medical Compendium Topic:
Electronic Health Records

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Identification of elders at higher risk for fall with statewide electronic health records and a machine learning algorithm.

International journal of medical informatics
OBJECTIVE: Predicting the risk of falls in advance can benefit the quality of care and potentially reduce mortality and morbidity in the older population. The aim of this study was to construct and validate an electronic health record-based fall risk...

Medical Named Entity Extraction from Chinese Resident Admit Notes Using Character and Word Attention-Enhanced Neural Network.

International journal of environmental research and public health
The resident admit notes (RANs) in electronic medical records (EMRs) is first-hand information to study the patient's condition. Medical entity extraction of RANs is an important task to get disease information for medical decision-making. For Chines...

EMR-Based Phenotyping of Ischemic Stroke Using Supervised Machine Learning and Text Mining Techniques.

IEEE journal of biomedical and health informatics
Ischemic stroke is a major cause of death and disability in adulthood worldwide. Because it has highly heterogeneous phenotypes, phenotyping of ischemic stroke is an essential task for medical research and clinical prognostication. However, this task...

Evolving Role and Future Directions of Natural Language Processing in Gastroenterology.

Digestive diseases and sciences
In line with the current trajectory of healthcare reform, significant emphasis has been placed on improving the utilization of data collected during a clinical encounter. Although the structured fields of electronic health records have provided a con...

Applications of Artificial Intelligence to Electronic Health Record Data in Ophthalmology.

Translational vision science & technology
Widespread adoption of electronic health records (EHRs) has resulted in the collection of massive amounts of clinical data. In ophthalmology in particular, the volume range of data captured in EHR systems has been growing rapidly. Yet making effectiv...

The application of unsupervised deep learning in predictive models using electronic health records.

BMC medical research methodology
BACKGROUND: The main goal of this study is to explore the use of features representing patient-level electronic health record (EHR) data, generated by the unsupervised deep learning algorithm autoencoder, in predictive modeling. Since autoencoder fea...

Research on Chinese medical named entity recognition based on collaborative cooperation of multiple neural network models.

Journal of biomedical informatics
Medical named entity recognition (NER) in Chinese electronic medical records (CEMRs) has drawn much research attention, and plays a vital prerequisite role for extracting high-value medical information. In 2018, China Health Information Processing Co...

Healthcare pathway discovery and probabilistic machine learning.

International journal of medical informatics
BACKGROUND AND PURPOSE: Healthcare pathways define the execution sequence of clinical activities as patients move through a treatment process, and they are critical for maintaining quality of care. The aim of this study is to combine healthcare pathw...

Development of an early-warning system for high-risk patients for suicide attempt using deep learning and electronic health records.

Translational psychiatry
Suicide is the tenth leading cause of death in the United States (US). An early-warning system (EWS) for suicide attempt could prove valuable for identifying those at risk of suicide attempts, and analyzing the contribution of repeated attempts to th...

Early detection of sepsis utilizing deep learning on electronic health record event sequences.

Artificial intelligence in medicine
BACKGROUND: The timeliness of detection of a sepsis incidence in progress is a crucial factor in the outcome for the patient. Machine learning models built from data in electronic health records can be used as an effective tool for improving this tim...