scholarly journals An Explainable Artificial Intelligence Approach for Predicting Cardiovascular Outcomes using Electronic Health Records

2021 ◽  
Author(s):  
Sergiusz Wesolowski ◽  
Gordon Howard Lemmon ◽  
Edgar J Hernandez ◽  
Alex Ryan Henrie ◽  
Thomas A Miller ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Electronic Health Records ◽  
Large Scale ◽  
Cardiovascular Health ◽  
Cardiovascular Outcomes ◽  
Health Records ◽  
University Of Utah ◽  
Explainable Artificial Intelligence ◽  
Electronic Health ◽  
Discovery Method

Understanding the conditionally-dependent clinical variables that drive cardiovascular health outcomes is a major challenge for precision medicine. Here, we deploy a recently developed massively scalable comorbidity discovery method called Poisson Binomial based Comorbidity discovery (PBC), to analyze Electronic Health Records (EHRs) from the University of Utah and Primary Children's Hospital (over 1.6 million patients and 77 million visits) for comorbid diagnoses, procedures, and medications. Using explainable Artificial Intelligence (AI) methodologies, we then tease apart the intertwined, conditionally-dependent impacts of comorbid conditions and demography upon cardiovascular health, focusing on the key areas of heart transplant, sinoatrial node dysfunction and various forms of congenital heart disease. The resulting multimorbidity networks make possible wide-ranging explorations of the comorbid and demographic landscapes surrounding these cardiovascular outcomes, and can be distributed as web-based tools for further community-based outcomes research. The ability to transform enormous collections of EHRs into compact, portable tools devoid of Protected Health Information solves many of the legal, technological, and data-scientific challenges associated with large-scale EHR analyzes.

scholarly journals Explainable artificial intelligence model to predict acute critical illness from electronic health records

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Simon Meyer Lauritsen ◽  
Mads Kristensen ◽  
Mathias Vassard Olsen ◽  
Morten Skaarup Larsen ◽  
Katrine Meyer Lauritsen ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Critical Illness ◽  
Electronic Health Records ◽  
Health Records ◽  
Explainable Artificial Intelligence ◽  
Electronic Health

Research and application of artificial intelligence based on electronic health record data from patients with cancer: a systematic review (Preprint)

2021 ◽  
Author(s):  
Xinyu Yang ◽  
Dongmei Mu ◽  
Hao Peng ◽  
Hua Li ◽  
Ying Wang ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Electronic Health Records ◽  
Ensemble Methods ◽  
Future Research ◽  
Electronic Health Record Data ◽  
Health Records ◽  
Patients With Cancer ◽  
Cancer Management ◽  
Treatment And Prevention ◽  
Electronic Health

BACKGROUND With the accumulation of electronic health records data and the development of artificial intelligence, patients with cancer urgently need new evidence of more personalized clinical and demographic characteristics and more sophisticated treatment and prevention strategies. However, no research has systematically analyzed the application and significance of electronic health records and artificial intelligence in cancer care. OBJECTIVE In this study, we reviewed the literature on the application of AI based on EHR data from patients with cancer, hoping to provide reference for subsequent researchers, and help accelerate the application of EHR data and AI technology in the field of cancer, so as to help patients get more scientific and accurate treatment. METHODS Three databases were systematically searched to retrieve potentially relevant articles published from January 2009 to October 2020. A combination of terms related to "electronic health records", "artificial intelligence" and "cancer" was used to search for these publications. RESULTS Of the 1034 articles considered, 148 met the inclusion criteria. The review has shown that ensemble methods and deep learning were on the rise. It presented the representative literatures on the subfield of cancer diagnosis, treatment and care. In addition, the vast majority of studies in this area were based on private institutional databases, resulting in poor portability of the proposed methodology process. CONCLUSIONS The use of new methods and electronic health records data sharing and fusion were recommended for future research. With the help of specialists, artificial intelligence and the mining of massive electronic medical records could provide great opportunities for improving cancer management.

Privacy-Friendly Management of Electronic Health Records in the eHealth Context

2015 ◽  
pp. 251-264
Author(s):  
Milica Milutinovic ◽  
Bart De Decker
Keyword(s):  
Electronic Health Records ◽  
Large Scale ◽  
Patient Data ◽  
Health Records ◽  
Privacy Concerns ◽  
Ubiquitous Technology ◽  
Emergency Situations ◽  
User Centric ◽  
Electronic Health

Electronic Health Records (EHRs) are becoming the ubiquitous technology for managing patients' records in many countries. They allow for easier transfer and analysis of patient data on a large scale. However, privacy concerns linked to this technology are emerging. Namely, patients rarely fully understand how EHRs are managed. Additionally, the records are not necessarily stored within the organization where the patient is receiving her healthcare. This service may be delegated to a remote provider, and it is not always clear which health-provisioning entities have access to this data. Therefore, in this chapter the authors propose an alternative where users can keep and manage their records in their existing eHealth systems. The approach is user-centric and enables the patients to have better control over their data while still allowing for special measures to be taken in case of emergency situations with the goal of providing the required care to the patient.

Five analytic challenges in working with electronic health records data to support clinical trials with some solutions

2020 ◽  
Vol 17 (4) ◽  
pp. 370-376
Author(s):  
Benjamin A Goldstein
Keyword(s):  
Clinical Trials ◽  
Electronic Health Records ◽  
Large Scale ◽  
Point Of Care ◽  
Cross Sectional ◽  
Health Records ◽  
Data Resource ◽  
Electronic Health ◽  
Data Elements ◽  
Data Efficiency

Electronic health records data are becoming a key data resource in clinical research. Owing to issues of data efficiency, electronic health records data are being used for clinical trials. This includes both large-scale pragmatic trails and smaller—more focused—point-of-care trials. While electronic health records data open up a number of scientific opportunities, they also present a number of analytic challenges. This article discusses five particular challenges related to organizing electronic health records data for analytic purposes. These are as follows: (1) data are not organized for research purposes, (2) data are both densely and irregularly observed, (3) we don’t have all data elements we may want or need, (4) data are both cross-sectional and longitudinal, and (5) data may be informatively observed. While laying out these challenges, the article notes how many of these challenges can be addressed by careful and thoughtful study design as well as by integration of clinicians and informaticians into the analytic team.

scholarly journals Savana: Re-using Electronic Health Records with Artificial Intelligence

2018 ◽  
Vol 4 (7) ◽  
pp. 8 ◽  
Author(s):  
Ignacio Hernandez Medrano ◽  
Jorge Tello Guijarro ◽  
Cristobal Belda ◽  
Alberto Urena ◽  
Ignacio Salcedo ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Electronic Health Records ◽  
Health Records ◽  
Electronic Health
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