Zobrazeno 1 - 10
of 45
pro vyhledávání: '"Sorayya Malek"'
Autor:
Sazzli Kasim, Putri Nur Fatin Amir Rudin, Sorayya Malek, Khairul Shafiq Ibrahim, Wan Azman Wan Ahmad, Alan Yean Yip Fong, Wan Yin Lin, Firdaus Aziz, Nurulain Ibrahim
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-16 (2024)
Abstract The accurate prediction of in-hospital mortality in Asian women after ST-Elevation Myocardial Infarction (STEMI) remains a crucial issue in medical research. Existing models frequently neglect this demographic's particular attributes, result
Externí odkaz:
https://doaj.org/article/815762e35d6c47e3a32c3fa8b1f12926
Autor:
Sazzli Kasim, Putri Nur Fatin Amir Rudin, Sorayya Malek, Firdaus Aziz, Wan Azman Wan Ahmad, Khairul Shafiq Ibrahim, Muhammad Hanis Muhmad Hamidi, Raja Ezman Raja Shariff, Alan Yean Yip Fong, Cheen Song
Publikováno v:
PLoS ONE, Vol 19, Iss 2, p e0298036 (2024)
BackgroundTraditional risk assessment tools often lack accuracy when predicting the short- and long-term mortality following a non-ST-segment elevation myocardial infarction (NSTEMI) or Unstable Angina (UA) in specific population.ObjectiveTo employ m
Externí odkaz:
https://doaj.org/article/80a78fb962ff4f83a03ae2024785de86
Publikováno v:
All Life, Vol 15, Iss 1, Pp 490-499 (2022)
To analyse and predict the healing time of upper limb fractures in children, machine learning algorithms were used. Paediatric orthopaedic data was obtained from the University Malaya Medical Centre. The data set includes radiographs of upper limb fr
Externí odkaz:
https://doaj.org/article/cb794e229a864d01b475feadb83b3f5c
Publikováno v:
All Life, Vol 15, Iss 1, Pp 500-511 (2022)
Data visualisation is essential for communicating and interpreting biodiversity data effectively. When compared to numerical values, visualising information with images is easier. Citizen Science has facilitated the collection of biodiversity data th
Externí odkaz:
https://doaj.org/article/0322a90d5c274702ba99f8f174391572
Autor:
Sazzli Kasim, Sorayya Malek, Song Cheen, Muhammad Shahreeza Safiruz, Wan Azman Wan Ahmad, Khairul Shafiq Ibrahim, Firdaus Aziz, Kazuaki Negishi, Nurulain Ibrahim
Publikováno v:
Scientific Reports, Vol 12, Iss 1, Pp 1-17 (2022)
Abstract Limited research has been conducted in Asian elderly patients (aged 65 years and above) for in-hospital mortality prediction after an ST-segment elevation myocardial infarction (STEMI) using Deep Learning (DL) and Machine Learning (ML). We u
Externí odkaz:
https://doaj.org/article/f9de0cef6ccb425e8163268246101589
Autor:
Sazzli Kasim, Sorayya Malek, Cheen Song, Wan Azman Wan Ahmad, Alan Fong, Khairul Shafiq Ibrahim, Muhammad Shahreeza Safiruz, Firdaus Aziz, Jia Hui Hiew, Nurulain Ibrahim
Publikováno v:
PLoS ONE, Vol 17, Iss 12, p e0278944 (2022)
BackgroundConventional risk score for predicting in-hospital mortality following Acute Coronary Syndrome (ACS) is not catered for Asian patients and requires different types of scoring algorithms for STEMI and NSTEMI patients.ObjectiveTo derive a sin
Externí odkaz:
https://doaj.org/article/5e69aaa9ef8345aca54ca19efce371f1
Autor:
Firdaus Aziz, Sorayya Malek, Khairul Shafiq Ibrahim, Raja Ezman Raja Shariff, Wan Azman Wan Ahmad, Rosli Mohd Ali, Kien Ting Liu, Gunavathy Selvaraj, Sazzli Kasim
Publikováno v:
PLoS ONE, Vol 16, Iss 8 (2021)
Background Conventional risk score for predicting short and long-term mortality following an ST-segment elevation myocardial infarction (STEMI) is often not population specific. Objective Apply machine learning for the prediction and identification o
Externí odkaz:
https://doaj.org/article/ad456ee4c6ee4f169d65f35ea7a33d22
Publikováno v:
PeerJ, Vol 8, p e8286 (2020)
Background This study assesses the feasibility of using machine learning methods such as Random Forests (RF), Artificial Neural Networks (ANN), Support Vector Regression (SVR) and Self-Organizing Feature Maps (SOM) to identify and determine factors a
Externí odkaz:
https://doaj.org/article/21b6d7be7e8e4e258a17536d02efa98c
Autor:
Sazzli Kasim, Putri Nur Fatin Amir Rudin, Sorayya Malek, Khairul Shafiq Ibrahim, Wan Azman Wan Ahmad, Alan Yean Yip Fong, Wan Yin Ling, Firdaus Aziz, Nurulain Ibrahim
Predictions of mortality in Asian women following STEMI have been the subject of limited studies. This study aims to develop and validate prediction models for in-hospital mortality following STEMI in Asian women using machine learning (ML) and stack
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::30ecc78742f5834a0a8d35f43503083d
https://doi.org/10.21203/rs.3.rs-2611510/v1
https://doi.org/10.21203/rs.3.rs-2611510/v1
Autor:
Sazzli Shahlan Kasim, Nurulain Ibrahim, Sorayya Malek, Khairul Shafiq Ibrahim, Muhammad Firdaus Aziz, Cheen Song, Yook Chin Chia, Anis Safura Ramli, Kazuaki Negishi, Nafiza Mat Nasir
Publikováno v:
The Lancet Regional Health - Western Pacific. :100742