AI Medical Compendium Topic:
Electronic Health Records

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Clinical Named Entity Recognition from Chinese Electronic Medical Records Based on Deep Learning Pretraining.

Journal of healthcare engineering
BACKGROUND: Clinical named entity recognition is the basic task of mining electronic medical records text, which are with some challenges containing the language features of Chinese electronic medical records text with many compound entities, serious...

From electronic health records to terminology base: A novel knowledge base enrichment approach.

Journal of biomedical informatics
Enriching terminology base (TB) is an important and continuous process, since formal term can be renamed and new term alias emerges all the time. As a potential supplementary for TB enrichment, electronic health record (EHR) is a fundamental source f...

Validation of deep learning natural language processing algorithm for keyword extraction from pathology reports in electronic health records.

Scientific reports
Pathology reports contain the essential data for both clinical and research purposes. However, the extraction of meaningful, qualitative data from the original document is difficult due to the narrative and complex nature of such reports. Keyword ext...

Using digital technologies in clinical trials: Current and future applications.

Contemporary clinical trials
In 2015, we provided an overview of the use of digital technologies in clinical trials, both as a methodological tool and as a mechanism to deliver interventions. At that time, there was limited guidance and limited use of digital technologies in cli...

Analytics with artificial intelligence to advance the treatment of acute respiratory distress syndrome.

Journal of evidence-based medicine
Artificial intelligence (AI) has found its way into clinical studies in the era of big data. Acute respiratory distress syndrome (ARDS) or acute lung injury (ALI) is a clinical syndrome that encompasses a heterogeneous population. Management of such ...

Facilitating the Development of Deep Learning Models with Visual Analytics for Electronic Health Records.

International journal of environmental research and public health
Electronic health record (EHR) data are widely used to perform early diagnoses and create treatment plans, which are key areas of research. We aimed to increase the efficiency of iteratively applying data-intensive technology and verifying the result...

Machine Learning to Predict Mortality and Critical Events in a Cohort of Patients With COVID-19 in New York City: Model Development and Validation.

Journal of medical Internet research
BACKGROUND: COVID-19 has infected millions of people worldwide and is responsible for several hundred thousand fatalities. The COVID-19 pandemic has necessitated thoughtful resource allocation and early identification of high-risk patients. However, ...

Artificial Intelligence in the Intensive Care Unit.

Seminars in respiratory and critical care medicine
The diffusion of electronic health records collecting large amount of clinical, monitoring, and laboratory data produced by intensive care units (ICUs) is the natural terrain for the application of artificial intelligence (AI). AI has a broad definit...

TAPER: Time-Aware Patient EHR Representation.

IEEE journal of biomedical and health informatics
Effective representation learning of electronic health records is a challenging task and is becoming more important as the availability of such data is becoming pervasive. The data contained in these records are irregular and contain multiple modalit...

Predicting preventable hospital readmissions with causal machine learning.

Health services research
OBJECTIVE: To assess both the feasibility and potential impact of predicting preventable hospital readmissions using causal machine learning applied to data from the implementation of a readmissions prevention intervention (the Transitions Program).