AI Medical Compendium Topic:
Electronic Health Records

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Korean clinical entity recognition from diagnosis text using BERT.

BMC medical informatics and decision making
BACKGROUND: While clinical entity recognition mostly aims at electronic health records (EHRs), there are also the demands of dealing with the other type of text data. Automatic medical diagnosis is an example of new applications using a different dat...

Marrying Medical Domain Knowledge With Deep Learning on Electronic Health Records: A Deep Visual Analytics Approach.

Journal of medical Internet research
BACKGROUND: Deep learning models have attracted significant interest from health care researchers during the last few decades. There have been many studies that apply deep learning to medical applications and achieve promising results. However, there...

Patient generated health data and electronic health record integration in oncologic surgery: A call for artificial intelligence and machine learning.

Journal of surgical oncology
In this review, we aim to assess the current state of science in relation to the integration of patient-generated health data (PGHD) and patient-reported outcomes (PROs) into routine clinical care with a focus on surgical oncology populations. We wil...

REDBot: Natural language process methods for clinical copy number variation reporting in prenatal and products of conception diagnosis.

Molecular genetics & genomic medicine
BACKGROUND: Current copy number variation (CNV) identification methods have rapidly become mature. However, the postdetection processes such as variant interpretation or reporting are inefficient. To overcome this situation, we developed REDBot as an...

De-identifying free text of Japanese electronic health records.

Journal of biomedical semantics
BACKGROUND: Recently, more electronic data sources are becoming available in the healthcare domain. Electronic health records (EHRs), with their vast amounts of potentially available data, can greatly improve healthcare. Although EHR de-identificatio...

Utilizing imbalanced electronic health records to predict acute kidney injury by ensemble learning and time series model.

BMC medical informatics and decision making
BACKGROUND: Acute Kidney Injury (AKI) is a shared complication among Intensive Care Unit (ICU), marked by high cost, high morbidity and high mortality. As the early prediction of AKI is critical for patients' outcomes and data mining is such a powerf...

Artificial intelligence in celiac disease.

Computers in biology and medicine
Celiac disease (CD) has been on the rise in the world and a large part of it remains undiagnosed. Novel methods are required to address the gaps in prompt detection and management. Artificial intelligence (AI) has seen an exponential surge in the las...

Artificial intelligence in medicine creates real risk management and litigation issues.

Journal of healthcare risk management : the journal of the American Society for Healthcare Risk Management
The next step in the evolution of electronic medical record (EMR) use is the integration of artificial intelligence (AI) into health care. With the benefit of roughly 15 years of electronic medical records (EMR) data from millions of patients, health...

Extracting and classifying diagnosis dates from clinical notes: A case study.

Journal of biomedical informatics
Myeloproliferative neoplasms (MPNs) are chronic hematologic malignancies that may progress over long disease courses. The original date of diagnosis is an important piece of information for patient care and research, but is not consistently documente...

Exploiting complex medical data with interpretable deep learning for adverse drug event prediction.

Artificial intelligence in medicine
A variety of deep learning architectures have been developed for the goal of predictive modelling and knowledge extraction from medical records. Several models have placed strong emphasis on temporal attention mechanisms and decay factors as a means ...