Integrating Exosomal MicroRNA and Electronic Health Data Improves Tuberculosis Diagnosis

2018 ◽  
Author(s):  
Xuejiao Hu ◽  
Shun Liao ◽  
Hao Bai ◽  
Lijuan Wu ◽  
Minjin Wang ◽  
...  
Keyword(s):  
Health Data ◽  
Tuberculosis Diagnosis ◽  
Electronic Health ◽  
Exosomal Microrna

scholarly journals Integrating exosomal microRNAs and electronic health data improved tuberculosis diagnosis

EBioMedicine ◽  
2019 ◽  
Vol 40 ◽  
pp. 564-573 ◽  
Author(s):  
Xuejiao Hu ◽  
Shun Liao ◽  
Hao Bai ◽  
Lijuan Wu ◽  
Minjin Wang ◽  
...  
Keyword(s):  
Health Data ◽  
Tuberculosis Diagnosis ◽  
Electronic Health

Identification and Validation of Anaphylaxis Using Electronic Health Data in a Population-based Setting

Epidemiology ◽  
2021 ◽  
Vol 32 (3) ◽  
pp. 439-443
Author(s):  
Maralyssa A. Bann ◽  
David S. Carrell ◽  
Susan Gruber ◽  
Mayura Shinde ◽  
Robert Ball ◽  
...  
Keyword(s):  
Population Based ◽  
Health Data ◽  
Electronic Health

The process of sourcing and preparing electronic health records data to implement a machine-learning algorithm for early identification of maternal cardiovascular risk (Preprint)

2021 ◽  
Author(s):  
Nawar Shara ◽  
Kelley M. Anderson ◽  
Noor Falah ◽  
Maryam F. Ahmad ◽  
Darya Tavazoei ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cardiovascular Risk ◽  
Electronic Health Records ◽  
Patient Care ◽  
Early Identification ◽  
Health Data ◽  
Patient Specific ◽  
Patient Records ◽  
Health Records ◽  
Electronic Health

BACKGROUND Healthcare data are fragmenting as patients seek care from diverse sources. Consequently, patient care is negatively impacted by disparate health records. Machine learning (ML) offers a disruptive force in its ability to inform and improve patient care and outcomes [6]. However, the differences that exist in each individual’s health records, combined with the lack of health-data standards, in addition to systemic issues that render the data unreliable and that fail to create a single view of each patient, create challenges for ML. While these problems exist throughout healthcare, they are especially prevalent within maternal health, and exacerbate the maternal morbidity and mortality (MMM) crisis in the United States. OBJECTIVE Maternal patient records were extracted from the electronic health records (EHRs) of a large tertiary healthcare system and made into patient-specific, complete datasets through a systematic method so that a machine-learning-based (ML-based) risk-assessment algorithm could effectively identify maternal cardiovascular risk prior to evidence of diagnosis or intervention within the patient’s record. METHODS We outline the effort that was required to define the specifications of the computational systems, the dataset, and access to relevant systems, while ensuring data security, privacy laws, and policies were met. Data acquisition included the concatenation, anonymization, and normalization of health data across multiple EHRs in preparation for its use by a proprietary risk-stratification algorithm designed to establish patient-specific baselines to identify and establish cardiovascular risk based on deviations from the patient’s baselines to inform early interventions. RESULTS Patient records can be made actionable for the goal of effectively employing machine learning (ML), specifically to identify cardiovascular risk in pregnant patients. CONCLUSIONS Upon acquiring data, including the concatenation, anonymization, and normalization of said data across multiple EHRs, the use of a machine-learning-based (ML-based) tool can provide early identification of cardiovascular risk in pregnant patients. CLINICALTRIAL N/A

scholarly journals Cancer Screening Results and Follow-up Using Routinely Collected Electronic Health Data: Estimates for Breast, Colon, and Cervical Cancer Screenings

2018 ◽  
Vol 34 (3) ◽  
pp. 341-343 ◽  
Author(s):  
Sudha R. Raman ◽  
Jeffrey S. Brown ◽  
Lesley H. Curtis ◽  
Kevin Haynes ◽  
James Marshall ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Cancer Screening ◽  
Health Data ◽  
Cancer Screenings ◽  
Electronic Health

scholarly journals Privacy, security, and the public health researcher in the era of electronic health record research

2016 ◽  
Vol 8 (3) ◽  
Author(s):  
Neal D Goldstein ◽  
Anand D Sarwate
Keyword(s):  
Public Health ◽  
Data Privacy ◽  
Large Scale ◽  
Health Data ◽  
Shared Responsibility ◽  
Privacy And Security ◽  
The Public ◽  
Health Researcher ◽  
Public Health Researcher ◽  
Electronic Health

Health data derived from electronic health records are increasingly utilized in large-scale population health analyses. Going hand in hand with this increase in data is an increasing number of data breaches. Ensuring privacy and security of these data is a shared responsibility between the public health researcher, collaborators, and their institutions. In this article, we review the requirements of data privacy and security and discuss epidemiologic implications of emerging technologies from the computer science community that can be used for health data. In order to ensure that our needs as researchers are captured in these technologies, we must engage in the dialogue surrounding the development of these tools.

A Systematic Review and Meta-Analysis of Preoperative Frailty Instruments Derived From Electronic Health Data

2021 ◽  
Author(s):  
Jamal Alkadri ◽  
Dima Hage ◽  
Leigh H. Nickerson ◽  
Lia R. Scott ◽  
Julia F. Shaw ◽  
...  
Keyword(s):  
Systematic Review ◽  
Meta Analysis ◽  
Health Data ◽  
Electronic Health

scholarly journals Real-Time Automated Surveillance for Ventilator Associated Events Using Streaming Electronic Health Data

2017 ◽  
Vol 4 (suppl_1) ◽  
pp. S633-S633
Author(s):  
Erica S Shenoy ◽  
Eric S Rosenthal ◽  
Siddharth Biswal ◽  
Manohar Ghanta ◽  
Erin E Ryan ◽  
...  
Keyword(s):  
Real Time ◽  
Health Data ◽  
Automated Surveillance ◽  
Electronic Health
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