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Machine Learning and Big Data Research in Cardiac Arrest: Where...
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Machine Learning and Big Data Research in Cardiac Arrest: Where Are We and What Do We Know?

A special issue of Journal of Clinical Medicine (ISSN 2077-0383). This special issue belongs to the section "Cardiology".

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 4412

Special Issue Editor

Prof. Dr. Won Young Kim

E-Mail Website
Guest Editor
Department of Emergency Medicine, University of Ulsan College of Medicine, Asan Medical Center, 88, Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Republic of Korea
Interests: cardiac arrest; post-cardiac arrest care; sepsis; septic shock
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The amount of data being generated in the healthcare system is growing rapidly, and the availability of these data can improve outcomes and reduce costs. Big data may be defined as large sets of data that are given to analytic approaches that may reveal underlying patterns, associations, or trends. However, large, complicated datasets may not be readily approachable with classic statistical methods such as logistic regression and Cox model analysis. To address this issue, machine‐learning approaches have been developed that permit the analysis of large datasets. The nature of cardiac-arrest data presents unique challenges in processing and analysis. Utilizing big data, research with machine-learning approaches for advances in resuscitation science have led to the development of new successful therapies and novel tools for characterizing these clinical conditions and providing better care to patients.

This Special Issue of the Journal of Clinical Medicine on “Machine Learning and Big Data Research in Cardiac Arrest: Where Are We and What Do We Know?” aims to collect brilliant contributions from worldwide experts in the field of resuscitation for cardiac arrest and post-cardiac-arrest care using big data or data approached with machine learning. We invite investigators to contribute with original research articles as well as review articles to extend our knowledge of the management of resuscitation and post-cardiac-arrest care.

Prof. Won Young Kim
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Clinical Medicine is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • big data
  • cardiac arrest
  • post-cardiac-arrest care
  • machine learning

Published Papers (2 papers)

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Research

12 pages, 2926 KiB  
Article
The ED-PLANN Score: A Simple Risk Stratification Tool for Out-of-Hospital Cardiac Arrests Derived from Emergency Departments in Korea
by Hyouk Jae Lim, Young Sun Ro, Ki Hong Kim, Jeong Ho Park, Ki Jeong Hong, Kyoung Jun Song and Sang Do Shin
J. Clin. Med. 2022, 11(1), 174; https://doi.org/10.3390/jcm11010174 - 29 Dec 2021
Cited by 3 | Viewed by 2172
Abstract
Early risk stratification of out-of-hospital cardiac arrest (OHCA) patients with insufficient information in emergency departments (ED) is difficult but critical in improving intensive care resource allocation. This study aimed to develop a simple risk stratification score using initial information in the ED. Adult [...] Read more.
Early risk stratification of out-of-hospital cardiac arrest (OHCA) patients with insufficient information in emergency departments (ED) is difficult but critical in improving intensive care resource allocation. This study aimed to develop a simple risk stratification score using initial information in the ED. Adult patients who had OHCA with medical etiology from 2016 to 2020 were enrolled from the Korean Cardiac Arrest Research Consortium (KoCARC) database. To develop a scoring system, a backward logistic regression analysis was conducted. The developed scoring system was validated in both external dataset and internal bootstrap resampling. A total of 8240 patients were analyzed, including 4712 in the development cohort and 3528 in the external validation cohort. An ED-PLANN score (range 0–5) was developed incorporating 1 point for each: P for serum pH ≤ 7.1, L for serum lactate ≥ 10 mmol/L, A for age ≥ 70 years old, N for non-shockable rhythm, and N for no-prehospital return of spontaneous circulation. The area under the receiver operating characteristics curve (AUROC) for favorable neurological outcome was 0.93 (95% CI, 0.92–0.94) in the development cohort, 0.94 (95% CI, 0.92–0.95) in the validation cohort. Hosmer–Lemeshow goodness-of-fit tests also indicated good agreement. The ED-PLANN score is a practical and easily applicable clinical scoring system for predicting favorable neurological outcomes of OHCA patients. Full article
(This article belongs to the Special Issue Machine Learning and Big Data Research in Cardiac Arrest: Where Are We and What Do We Know?)
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14 pages, 1620 KiB  
Article
Early Identification of Resuscitated Patients with a Significant Coronary Disease in Out-of-Hospital Cardiac Arrest Survivors without ST-Segment Elevation
by Chun-Song Youn, Hahn Yi, Youn-Jung Kim, Hwan Song, Namkug Kim and Won-Young Kim
J. Clin. Med. 2021, 10(23), 5688; https://doi.org/10.3390/jcm10235688 - 2 Dec 2021
Cited by 1 | Viewed by 1594
Abstract
This study aimed to develop a machine learning (ML)-based model for identifying patients who had a significant coronary artery disease among out-of-hospital cardiac arrest (OHCA) survivors without ST-segment elevation (STE). This multicenter observational study used data from the Korean Hypothermia Network prospective registry [...] Read more.
This study aimed to develop a machine learning (ML)-based model for identifying patients who had a significant coronary artery disease among out-of-hospital cardiac arrest (OHCA) survivors without ST-segment elevation (STE). This multicenter observational study used data from the Korean Hypothermia Network prospective registry (KORHN-PRO) gathered between October 2015 and December 2018. We used information available before targeted temperature management (TTM) as predictor variables, and the primary outcome was a significant coronary artery lesion in coronary angiography (CAG). Among 1373 OHCA patients treated with TTM, 331 patients without STE who underwent CAG were enrolled. Among them, 127 patients (38.4%) had a significant coronary artery lesion. Four ML algorithms, namely regularized logistic regression (RLR), random forest classifier (RF), CatBoost classifier (CBC), and voting classifier (VC), were used with data collected before CAG. The VC model showed the highest accuracy for predicting significant lesions (area under the curve of 0.751). Eight variables (older age, male, initial shockable rhythm, shorter total collapse duration, higher glucose and creatinine, and lower pH and lactate) were significant to ML models. These results showed that ML models may be useful in developing early predictive tools for identifying high-risk patients with a significant stenosis in CAG. Full article
(This article belongs to the Special Issue Machine Learning and Big Data Research in Cardiac Arrest: Where Are We and What Do We Know?)
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