Details
| Originalsprache | Englisch |
|---|---|
| Aufsatznummer | 179 |
| Seitenumfang | 13 |
| Fachzeitschrift | Reviews in cardiovascular medicine |
| Jahrgang | 25 |
| Ausgabenummer | 5 |
| Publikationsstatus | Veröffentlicht - 20 Mai 2024 |
Abstract
Background: In the post-coronavirus disease 2019 (COVID-19) era, remote diagnosis and precision preventive medicine have emerged as pivotal clinical medicine applications. This study aims to develop a digital health-monitoring tool that utilizes electronic medical records (EMRs) as the foundation for performing a non-random correlation analysis among different comorbidity patterns for heart failure (HF). Methods: Novel similarity indices, including proportional Jaccard index (PJI), multiplication of the odds ratio proportional Jaccard index (OPJI), and alpha proportional Jaccard index (APJI), provide a fundamental framework for constructing machine learning models to predict the risk conditions associated with HF. Results: Our models were constructed for different age groups and sexes and yielded accurate predictions of high-risk HF across demographics. The results indicated that the optimal prediction model achieved a notable accuracy of 82.1% and an area under the curve (AUC) of 0.878. Conclusions: Our noninvasive HF risk prediction system is based on historical EMRs and provides a practical approach. The proposed indices provided simple and straightforward comparative indicators of comorbidity pattern matching within individual EMRs. All source codes developed for our noninvasive prediction models can be retrieved from GitHub.
ASJC Scopus Sachgebiete
- Medizin (insg.)
- Kardiologie und kardiovaskuläre Medizin
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in: Reviews in cardiovascular medicine, Jahrgang 25, Nr. 5, 179, 20.05.2024.
Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review
}
TY - JOUR
T1 - Noninvasive Risk Prediction Models for Heart Failure Using Proportional Jaccard Indices and Comorbidity Patterns
AU - Tang, Yueh
AU - Wang, Chao Hung
AU - Mitra, Prasenjit
AU - Pai, Tun Wen
N1 - Publisher Copyright: © 2024 The Author(s). Published by IMR Press.
PY - 2024/5/20
Y1 - 2024/5/20
N2 - Background: In the post-coronavirus disease 2019 (COVID-19) era, remote diagnosis and precision preventive medicine have emerged as pivotal clinical medicine applications. This study aims to develop a digital health-monitoring tool that utilizes electronic medical records (EMRs) as the foundation for performing a non-random correlation analysis among different comorbidity patterns for heart failure (HF). Methods: Novel similarity indices, including proportional Jaccard index (PJI), multiplication of the odds ratio proportional Jaccard index (OPJI), and alpha proportional Jaccard index (APJI), provide a fundamental framework for constructing machine learning models to predict the risk conditions associated with HF. Results: Our models were constructed for different age groups and sexes and yielded accurate predictions of high-risk HF across demographics. The results indicated that the optimal prediction model achieved a notable accuracy of 82.1% and an area under the curve (AUC) of 0.878. Conclusions: Our noninvasive HF risk prediction system is based on historical EMRs and provides a practical approach. The proposed indices provided simple and straightforward comparative indicators of comorbidity pattern matching within individual EMRs. All source codes developed for our noninvasive prediction models can be retrieved from GitHub.
AB - Background: In the post-coronavirus disease 2019 (COVID-19) era, remote diagnosis and precision preventive medicine have emerged as pivotal clinical medicine applications. This study aims to develop a digital health-monitoring tool that utilizes electronic medical records (EMRs) as the foundation for performing a non-random correlation analysis among different comorbidity patterns for heart failure (HF). Methods: Novel similarity indices, including proportional Jaccard index (PJI), multiplication of the odds ratio proportional Jaccard index (OPJI), and alpha proportional Jaccard index (APJI), provide a fundamental framework for constructing machine learning models to predict the risk conditions associated with HF. Results: Our models were constructed for different age groups and sexes and yielded accurate predictions of high-risk HF across demographics. The results indicated that the optimal prediction model achieved a notable accuracy of 82.1% and an area under the curve (AUC) of 0.878. Conclusions: Our noninvasive HF risk prediction system is based on historical EMRs and provides a practical approach. The proposed indices provided simple and straightforward comparative indicators of comorbidity pattern matching within individual EMRs. All source codes developed for our noninvasive prediction models can be retrieved from GitHub.
KW - alpha index
KW - alpha proportional jaccard index
KW - electronic medical records
KW - heart failure
KW - odds ratio
KW - odds ratio proportional jaccard index
KW - precision prevention
KW - proportional jaccard index
KW - telehealth
UR - http://www.scopus.com/inward/record.url?scp=85194758618&partnerID=8YFLogxK
U2 - 10.31083/j.rcm2505179
DO - 10.31083/j.rcm2505179
M3 - Article
AN - SCOPUS:85194758618
VL - 25
JO - Reviews in cardiovascular medicine
JF - Reviews in cardiovascular medicine
SN - 1530-6550
IS - 5
M1 - 179
ER -