AIMC Topic: Electronic Health Records

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Long-term epilepsy outcome dynamics revealed by natural language processing of clinic notes.

Epilepsia
OBJECTIVE: Electronic medical records allow for retrospective clinical research with large patient cohorts. However, epilepsy outcomes are often contained in free text notes that are difficult to mine. We recently developed and validated novel natura...

Confidence-based laboratory test reduction recommendation algorithm.

BMC medical informatics and decision making
BACKGROUND: We propose a new deep learning model to identify unnecessary hemoglobin (Hgb) tests for patients admitted to the hospital, which can help reduce health risks and healthcare costs.

In-Sensor Artificial Intelligence and Fusion With Electronic Medical Records for At-Home Monitoring.

IEEE transactions on biomedical circuits and systems
This work presents an artificial intelligence (AI) framework for real-time, personalized sepsis prediction four hours before onset through fusion of electrocardiogram (ECG) and patient electronic medical record. An on-chip classifier combines analog ...

Deep learning approach for early prediction of COVID-19 mortality using chest X-ray and electronic health records.

BMC bioinformatics
BACKGROUND: An artificial-intelligence (AI) model for predicting the prognosis or mortality of coronavirus disease 2019 (COVID-19) patients will allow efficient allocation of limited medical resources. We developed an early mortality prediction ensem...

Heart disease risk factors detection from electronic health records using advanced NLP and deep learning techniques.

Scientific reports
Heart disease remains the major cause of death, despite recent improvements in prediction and prevention. Risk factor identification is the main step in diagnosing and preventing heart disease. Automatically detecting risk factors for heart disease i...

Identifying Barriers to Post-Acute Care Referral and Characterizing Negative Patient Preferences Among Hospitalized Older Adults Using Natural Language Processing.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Our objective was to detect common barriers to post-acute care (B2PAC) among hospitalized older adults using natural language processing (NLP) of clinical notes from patients discharged home when a clinical decision support system recommended post-ac...

Artificial intelligence innovation in healthcare: Relevance of reporting guidelines for clinical translation from bench to bedside.

Annals of the Academy of Medicine, Singapore
Artificial intelligence (AI) and digital innovation are transforming healthcare. Technologies such as machine learning in image analysis, natural language processing in medical chatbots and electronic medical record extraction have the potential to i...

Predictive Modeling Using Artificial Intelligence and Machine Learning Algorithms on Electronic Health Record Data: Advantages and Challenges.

Critical care clinics
The rapid adoption of electronic health record (EHR) systems in US hospitals from 2008 to 2014 produced novel data elements for analysis. Concurrent innovations in computing architecture and machine learning (ML) algorithms have made rapid consumptio...

Artificial Intelligence-Based Ethical Hacking for Health Information Systems: Simulation Study.

Journal of medical Internet research
BACKGROUND: Health information systems (HISs) are continuously targeted by hackers, who aim to bring down critical health infrastructure. This study was motivated by recent attacks on health care organizations that have resulted in the compromise of ...