AI Medical Compendium Topic:
Electronic Health Records

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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 ...

Development, implementation, and prospective validation of a model to predict 60-day end-of-life in hospitalized adults upon admission at three sites.

BMC medical informatics and decision making
BACKGROUND: Automated systems that use machine learning to estimate a patient's risk of death are being developed to influence care. There remains sparse transparent reporting of model generalizability in different subpopulations especially for imple...

Applications of Artificial Intelligence and Big Data Analytics in m-Health: A Healthcare System Perspective.

Journal of healthcare engineering
Mobile health (m-health) is the term of monitoring the health using mobile phones and patient monitoring devices etc. It has been often deemed as the substantial breakthrough in technology in this modern era. Recently, artificial intelligence (AI) an...

PASCAL: a pseudo cascade learning framework for breast cancer treatment entity normalization in Chinese clinical text.

BMC medical informatics and decision making
BACKGROUNDS: Knowledge discovery from breast cancer treatment records has promoted downstream clinical studies such as careflow mining and therapy analysis. However, the clinical treatment text from electronic health data might be recorded by differe...

Identifying Goals of Care Conversations in the Electronic Health Record Using Natural Language Processing and Machine Learning.

Journal of pain and symptom management
CONTEXT: Goals-of-care discussions are an important quality metric in palliative care. However, goals-of-care discussions are often documented as free text in diverse locations. It is difficult to identify these discussions in the electronic health r...