Clinical Predictive Modeling of Heart Failure: Domain Description, Models’ Characteristics and Literature Review
This study attempts to identify and briefly describe the current directions in applied and theoretical clinical prediction research. Context-rich chronic heart failure syndrome (CHFS) telemedicine provides the medical foundation for this effort. In the chronic stage of heart failure, there are sudde...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-02-01
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| Series: | Diagnostics |
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| Online Access: | https://www.mdpi.com/2075-4418/14/4/443 |
| _version_ | 1797298476216745984 |
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| author | Igor Odrobina |
| author_facet | Igor Odrobina |
| author_sort | Igor Odrobina |
| collection | DOAJ |
| description | This study attempts to identify and briefly describe the current directions in applied and theoretical clinical prediction research. Context-rich chronic heart failure syndrome (CHFS) telemedicine provides the medical foundation for this effort. In the chronic stage of heart failure, there are sudden exacerbations of syndromes with subsequent hospitalizations, which are called acute decompensation of heart failure (ADHF). These decompensations are the subject of diagnostic and prognostic predictions. The primary purpose of ADHF predictions is to clarify the current and future health status of patients and subsequently optimize therapeutic responses. We proposed a simplified discrete-state disease model as an attempt at a typical summarization of a medical subject before starting predictive modeling. The study tries also to structure the essential common characteristics of quantitative models in order to understand the issue in an application context. The last part provides an overview of prediction works in the field of CHFS. These three parts provide the reader with a comprehensive view of quantitative clinical predictive modeling in heart failure telemedicine with an emphasis on several key general aspects. The target community is medical researchers seeking to align their clinical studies with prognostic or diagnostic predictive modeling, as well as other predictive researchers. The study was written by a non-medical expert. |
| first_indexed | 2024-03-07T22:35:23Z |
| format | Article |
| id | doaj.art-4af2c001515f416980dac53de23ecf70 |
| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-03-07T22:35:23Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-4af2c001515f416980dac53de23ecf702024-02-23T15:13:55ZengMDPI AGDiagnostics2075-44182024-02-0114444310.3390/diagnostics14040443Clinical Predictive Modeling of Heart Failure: Domain Description, Models’ Characteristics and Literature ReviewIgor Odrobina0Mathematical Institute, Slovak Academy of Science, Štefánikova 49, SK-841 73 Bratislava, SlovakiaThis study attempts to identify and briefly describe the current directions in applied and theoretical clinical prediction research. Context-rich chronic heart failure syndrome (CHFS) telemedicine provides the medical foundation for this effort. In the chronic stage of heart failure, there are sudden exacerbations of syndromes with subsequent hospitalizations, which are called acute decompensation of heart failure (ADHF). These decompensations are the subject of diagnostic and prognostic predictions. The primary purpose of ADHF predictions is to clarify the current and future health status of patients and subsequently optimize therapeutic responses. We proposed a simplified discrete-state disease model as an attempt at a typical summarization of a medical subject before starting predictive modeling. The study tries also to structure the essential common characteristics of quantitative models in order to understand the issue in an application context. The last part provides an overview of prediction works in the field of CHFS. These three parts provide the reader with a comprehensive view of quantitative clinical predictive modeling in heart failure telemedicine with an emphasis on several key general aspects. The target community is medical researchers seeking to align their clinical studies with prognostic or diagnostic predictive modeling, as well as other predictive researchers. The study was written by a non-medical expert.https://www.mdpi.com/2075-4418/14/4/443predictionmodelheart failuretelemedicineprognosisdiagnosis |
| spellingShingle | Igor Odrobina Clinical Predictive Modeling of Heart Failure: Domain Description, Models’ Characteristics and Literature Review Diagnostics prediction model heart failure telemedicine prognosis diagnosis |
| title | Clinical Predictive Modeling of Heart Failure: Domain Description, Models’ Characteristics and Literature Review |
| title_full | Clinical Predictive Modeling of Heart Failure: Domain Description, Models’ Characteristics and Literature Review |
| title_fullStr | Clinical Predictive Modeling of Heart Failure: Domain Description, Models’ Characteristics and Literature Review |
| title_full_unstemmed | Clinical Predictive Modeling of Heart Failure: Domain Description, Models’ Characteristics and Literature Review |
| title_short | Clinical Predictive Modeling of Heart Failure: Domain Description, Models’ Characteristics and Literature Review |
| title_sort | clinical predictive modeling of heart failure domain description models characteristics and literature review |
| topic | prediction model heart failure telemedicine prognosis diagnosis |
| url | https://www.mdpi.com/2075-4418/14/4/443 |
| work_keys_str_mv | AT igorodrobina clinicalpredictivemodelingofheartfailuredomaindescriptionmodelscharacteristicsandliteraturereview |