AIMC Topic: Electronic Health Records

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Feature-weighted survival learning machine for COPD failure prediction.

Artificial intelligence in medicine
Chronic obstructive pulmonary disease (COPD) yields a high rate of failures such as hospital readmission and death in the United States, Canada and worldwide. COPD failure imposes a significant social and economic burden on society, and predicting su...

The day when computers read between lines.

Japanese journal of radiology
There is a growing notion that artificial general intelligence (AGI) will replace some of the work done by trained professionals, including physicians. This idea, however, seems to have logical leap; herein, we discuss three problems that are signifi...

Minimalistic Approach to Coreference Resolution in Lithuanian Medical Records.

Computational and mathematical methods in medicine
Coreference resolution is a challenging part of natural language processing (NLP) with applications in machine translation, semantic search and other information retrieval, and decision support systems. Coreference resolution requires linguistic prep...

LSTM Model for Prediction of Heart Failure in Big Data.

Journal of medical systems
The combination of big data and deep learning is a world-shattering technology that can make a great impact on any industry if used in a proper way. With the availability of large volume of health care datasets and progressions in deep learning techn...

Leveraging Electronic Dental Record Data to Classify Patients Based on Their Smoking Intensity.

Methods of information in medicine
BACKGROUND: Smoking is an established risk factor for oral diseases and, therefore, dental clinicians routinely assess and record their patients' detailed smoking status. Researchers have successfully extracted smoking history from electronic health ...

Automatically identifying social isolation from clinical narratives for patients with prostate Cancer.

BMC medical informatics and decision making
BACKGROUND: Social isolation is an important social determinant that impacts health outcomes and mortality among patients. The National Academy of Medicine recently recommended that social isolation be documented in electronic health records (EHR). H...

Development of an intelligent decision support system for ischemic stroke risk assessment in a population-based electronic health record database.

PloS one
BACKGROUND: Intelligent decision support systems (IDSS) have been applied to tasks of disease management. Deep neural networks (DNNs) are artificial intelligent techniques to achieve high modeling power. The application of DNNs to large-scale data fo...

Detecting Hypoglycemia Incidents Reported in Patients' Secure Messages: Using Cost-Sensitive Learning and Oversampling to Reduce Data Imbalance.

Journal of medical Internet research
BACKGROUND: Improper dosing of medications such as insulin can cause hypoglycemic episodes, which may lead to severe morbidity or even death. Although secure messaging was designed for exchanging nonurgent messages, patients sometimes report hypoglyc...

Assessment of a Deep Learning Model Based on Electronic Health Record Data to Forecast Clinical Outcomes in Patients With Rheumatoid Arthritis.

JAMA network open
IMPORTANCE: Knowing the future condition of a patient would enable a physician to customize current therapeutic options to prevent disease worsening, but predicting that future condition requires sophisticated modeling and information. If artificial ...