ISSN:
1382-4147
eISSN:
1573-7322
Disciplines
(Field of Science):
- biomedical engineering (Engineering and Technology)
- medical biology (Medical and Health Sciences )
- medical sciences (Medical and Health Sciences )
- health sciences (Medical and Health Sciences )
- biotechnology (Natural sciences)
(Field of Science)
Ministry points: Help
Year | Points | List |
---|---|---|
Year 2024 | 100 | Ministry scored journals list 2024 |
Year | Points | List |
---|---|---|
2024 | 100 | Ministry scored journals list 2024 |
2023 | 100 | Ministry Scored Journals List |
2022 | 100 | Ministry Scored Journals List 2019-2022 |
2021 | 100 | Ministry Scored Journals List 2019-2022 |
2020 | 100 | Ministry Scored Journals List 2019-2022 |
2019 | 100 | Ministry Scored Journals List 2019-2022 |
2018 | 35 | A |
2017 | 35 | A |
2016 | 30 | A |
2015 | 30 | A |
2014 | 30 | A |
2013 | 35 | A |
2012 | 35 | A |
2011 | 35 | A |
2010 | 32 | A |
Model:
Hybrid
Points CiteScore:
Year | Points |
---|---|
Year 2023 | 10.4 |
Year | Points |
---|---|
2023 | 10.4 |
2022 | 8.9 |
2021 | 7.3 |
2020 | 6.9 |
2019 | 7.1 |
2018 | 7 |
2017 | 7.3 |
2016 | 7.9 |
2015 | 8.1 |
2014 | 7.3 |
2013 | 7.2 |
2012 | 6.1 |
2011 | 6.9 |
Impact Factor:
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Sherpa Romeo:
Papers published in journal
Filters
total: 1
Catalog Journals
Year 2023
-
Artificial intelligence models in prediction of response to cardiac resynchronization therapy: a systematic review
PublicationThe aim of the presented review is to summarize the literature data on the accuracy and clinical applicability of artificial intelligence (AI) models as a valuable alternative to the current guidelines in predicting cardiac resynchronization therapy (CRT) response and phenotyping of patients eligible for CRT implantation. This systematic review was performed...
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