AIMC Topic: Electronic Health Records

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Benchmarking the most popular XAI used for explaining clinical predictive models: Untrustworthy but could be useful.

Health informatics journal
OBJECTIVE: This study aimed to assess the practicality and trustworthiness of explainable artificial intelligence (XAI) methods used for explaining clinical predictive models.

The Growing Impact of Natural Language Processing in Healthcare and Public Health.

Inquiry : a journal of medical care organization, provision and financing
Natural Language Processing (NLP) is a subset of Artificial Intelligence, specifically focused on understanding and generating human language. NLP technologies are becoming more prevalent in healthcare and hold potential solutions to current problems...

A Multi-Institutional Natural Language Processing Pipeline to Extract Performance Status From Electronic Health Records.

Cancer control : journal of the Moffitt Cancer Center
PURPOSE: Performance status (PS), an essential indicator of patients' functional abilities, is often documented in clinical notes of patients with cancer. The use of natural language processing (NLP) in extracting PS from electronic medical records (...

Developing an AI Tool to Derive Social Determinants of Health for Primary Care Patients: Qualitative Findings From a Codesign Workshop.

Annals of family medicine
PURPOSE: Information about social determinants of health (SDOH) is essential for primary care clinicians in the delivery of equitable, comprehensive care, as well as for program planning and resource allocation. SDOH are rarely captured consistently ...

Dilemmas and prospects of artificial intelligence technology in the data management of medical informatization in China: A new perspective on SPRAY-type AI applications.

Health informatics journal
This study aims to address the critical challenges of data integrity, accuracy, consistency, and precision in the application of electronic medical record (EMR) data within the healthcare sector, particularly within the context of Chinese medical in...

What Complexity Science Predicts About the Potential of Artificial Intelligence/Machine Learning to Improve Primary Care.

Journal of the American Board of Family Medicine : JABFM
Primary care physicians are likely both excited and apprehensive at the prospects for artificial intelligence (AI) and machine learning (ML). Complexity science may provide insight into which AI/ML applications will most likely affect primary care in...

IoT-based external attacks aware secure healthcare framework using blockchain and SB-RNN-NVS-FU techniques.

Technology and health care : official journal of the European Society for Engineering and Medicine
BACKGROUND: In recent times, there has been widespread deployment of Internet of Things (IoT) applications, particularly in the healthcare sector, where computations involving user-specific data are carried out on cloud servers. However, the network ...

Probabilistic neural network based visual data mining for the healthcare sector.

Technology and health care : official journal of the European Society for Engineering and Medicine
BACKGROUND: The need for personalised care in the long-term management of patient health is paramount due to the variability in individual features and responses to specific medication. With the availability of large quantities of electronic patient ...

Prediction models using artificial intelligence and longitudinal data from electronic health records: a systematic methodological review.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: To describe and appraise the use of artificial intelligence (AI) techniques that can cope with longitudinal data from electronic health records (EHRs) to predict health-related outcomes.

Self-supervised machine learning using adult inpatient data produces effective models for pediatric clinical prediction tasks.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Development of electronic health records (EHR)-based machine learning models for pediatric inpatients is challenged by limited training data. Self-supervised learning using adult data may be a promising approach to creating robust pediatri...