[HTML][HTML] Opportunities and challenges in developing risk prediction models with electronic health records data: a systematic review

BA Goldstein, AM Navar, MJ Pencina… - Journal of the …, 2017 - ncbi.nlm.nih.gov
Objective: Electronic health records (EHRs) are an increasingly common data source for
clinical risk prediction, presenting both unique analytic opportunities and challenges. We …

Big data analytics to improve cardiovascular care: promise and challenges

JS Rumsfeld, KE Joynt, TM Maddox - Nature Reviews Cardiology, 2016 - nature.com
The potential for big data analytics to improve cardiovascular quality of care and patient
outcomes is tremendous. However, the application of big data in health care is at a nascent …

Blockchain's coming to hospital to digitalize healthcare services: Designing a distributed electronic health record ecosystem

R Cerchione, P Centobelli, E Riccio, S Abbate… - Technovation, 2023 - Elsevier
The technological revolution in blockchain achieved the healthcare sector and offered a
significant opportunity to lead this digital transformation. A significant problem is that various …

[HTML][HTML] 2019 ACC expert consensus decision pathway on risk assessment, management, and clinical trajectory of patients hospitalized with heart failure: a report of …

SM Hollenberg, L Warner Stevenson, T Ahmad… - Journal of the American …, 2019 - jacc.org
The American College of Cardiology (ACC) has a long history of developing documents (eg,
decision pathways, health policy statements, appropriate use criteria) to provide members …

Improving palliative care with deep learning

A Avati, K Jung, S Harman, L Downing, A Ng… - BMC medical informatics …, 2018 - Springer
Background Access to palliative care is a key quality metric which most healthcare
organizations strive to improve. The primary challenges to increasing palliative care access …

Machine learning prediction of mortality and hospitalization in heart failure with preserved ejection fraction

S Angraal, BJ Mortazavi, A Gupta, R Khera, T Ahmad… - JACC: Heart Failure, 2020 - jacc.org
Objectives: This study sought to develop models for predicting mortality and heart failure
(HF) hospitalization for outpatients with HF with preserved ejection fraction (HFpEF) in the …

2013 ACCF/AHA guideline for the management of heart failure: a report of the American College of Cardiology Foundation/American Heart Association Task Force on …

CW Yancy, M Jessup, B Bozkurt, J Butler… - Journal of the American …, 2013 - jacc.org
2013 ACCF/AHA Guideline for the Management of Heart Failure: A Report of the American
College of Cardiology Foundation/American Heart Association Task Force on Practice …

Analysis of machine learning techniques for heart failure readmissions

BJ Mortazavi, NS Downing, EM Bucholz… - … Quality and Outcomes, 2016 - Am Heart Assoc
Background—The current ability to predict readmissions in patients with heart failure is
modest at best. It is unclear whether machine learning techniques that address higher …

Prediction of 30-day all-cause readmissions in patients hospitalized for heart failure: comparison of machine learning and other statistical approaches

JD Frizzell, L Liang, PJ Schulte, CW Yancy… - JAMA …, 2017 - jamanetwork.com
Importance Several attempts have been made at developing models to predict 30-day
readmissions in patients with heart failure, but none have sufficient discriminatory capacity …

Risk prediction models for hospital readmission: a systematic review

D Kansagara, H Englander, A Salanitro, D Kagen… - Jama, 2011 - jamanetwork.com
Context Predicting hospital readmission risk is of great interest to identify which patients
would benefit most from care transition interventions, as well as to risk-adjust readmission …