Zobrazeno 1 - 10
of 27
pro vyhledávání: '"Amin Naemi"'
Autor:
Samin Babaei Rikan, Amir Sorayaie Azar, Amin Naemi, Jamshid Bagherzadeh Mohasefi, Habibollah Pirnejad, Uffe Kock Wiil
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-12 (2024)
Abstract In this study, we utilized data from the Surveillance, Epidemiology, and End Results (SEER) database to predict the glioblastoma patients’ survival outcomes. To assess dataset skewness and detect feature importance, we applied Pearson's se
Externí odkaz:
https://doaj.org/article/1ca083ad63994163a750984fcf1ca7ba
Autor:
Amir Sorayaie Azar, Amin Naemi, Samin Babaei Rikan, Jamshid Bagherzadeh Mohasefi, Habibollah Pirnejad, Uffe Kock Wiil
Publikováno v:
BMC Infectious Diseases, Vol 23, Iss 1, Pp 1-13 (2023)
Abstract Background In May 2022, the World Health Organization (WHO) European Region announced an atypical Monkeypox epidemic in response to reports of numerous cases in some member countries unrelated to those where the illness is endemic. This issu
Externí odkaz:
https://doaj.org/article/8edc92d86c934d80911a10ee573d9de6
Autor:
Amir Sorayaie Azar, Samin Babaei Rikan, Amin Naemi, Jamshid Bagherzadeh Mohasefi, Habibollah Pirnejad, Matin Bagherzadeh Mohasefi, Uffe Kock Wiil
Publikováno v:
BMC Medical Informatics and Decision Making, Vol 22, Iss 1, Pp 1-24 (2022)
Abstract Background Ovarian cancer is the fifth leading cause of mortality among women in the United States. Ovarian cancer is also known as forgotten cancer or silent disease. The survival of ovarian cancer patients depends on several factors, inclu
Externí odkaz:
https://doaj.org/article/9f54ca0130d544e4b72e42f90e9ad7c3
Autor:
Ali Ebrahimi, Uffe Kock Wiil, Amin Naemi, Marjan Mansourvar, Kjeld Andersen, Anette Søgaard Nielsen
Publikováno v:
BMC Medical Informatics and Decision Making, Vol 22, Iss 1, Pp 1-25 (2022)
Abstract Background High dimensionality in electronic health records (EHR) causes a significant computational problem for any systematic search for predictive, diagnostic, or prognostic patterns. Feature selection (FS) methods have been indicated to
Externí odkaz:
https://doaj.org/article/7c57513cdc254c9a96531e2a3eba456c
Publikováno v:
BMC Medical Informatics and Decision Making, Vol 21, Iss 1, Pp 1-13 (2021)
Abstract Background Prediction of length of stay (LOS) at admission time can provide physicians and nurses insight into the illness severity of patients and aid them in avoiding adverse events and clinical deterioration. It also assists hospitals wit
Externí odkaz:
https://doaj.org/article/28a80b8dfdd84e5d8ab89f300bf51680
Autor:
Ali Ebrahimi, Uffe Kock Wiil, Thomas Schmidt, Amin Naemi, Anette Sogaard Nielsen, Ghulam Mujtaba Shaikh, Marjan Mansourvar
Publikováno v:
IEEE Access, Vol 9, Pp 151697-151712 (2021)
The number of deaths caused by alcohol-related diseases may be reduced by predicting alcohol use disorder (AUD). Many researchers have worked on AUD prediction using machine learning (ML) techniques. However, to the best of our knowledge, there is a
Externí odkaz:
https://doaj.org/article/3637a16e2a664c78b4e223243639a61e
Autor:
Mohammad Naghavi-Behzad, Thomas Schmidt, Amin Naemi, Marjan Mansourvar, Ali Ebrahimi, Uffe Kock Wiil
Publikováno v:
BMJ Open, Vol 11, Iss 11 (2021)
Objectives This systematic review aimed to assess the performance and clinical feasibility of machine learning (ML) algorithms in prediction of in-hospital mortality for medical patients using vital signs at emergency departments (EDs).Design A syste
Externí odkaz:
https://doaj.org/article/5e93a9e93711486e983e2ebbc926cfbd
Autor:
Marjan Mansourvar, Uffe Kock Wiil, Anette Søgaard Nielsen, Ghulam Mujtaba Shaikh, Ali Ebrahimi, Thomas Schmidt, Amin Naemi
Publikováno v:
IEEE Access, Vol 9, Pp 151697-151712 (2021)
Ebrahimi, A, Wiil, U K, Schmidt, T, Naemi, A, Nielsen, A S, Shaikh, G M & Mansourvar, M 2021, ' Predicting the Risk of Alcohol Use Disorder Using Machine Learning : A Systematic Literature Review ', IEEE Access, vol. 9, pp. 151697-151712 . https://doi.org/10.1109/ACCESS.2021.3126777
Ebrahimi, A, Wiil, U K, Schmidt, T, Naemi, A, Nielsen, A S, Shaikh, G M & Mansourvar, M 2021, ' Predicting the Risk of Alcohol Use Disorder Using Machine Learning : A Systematic Literature Review ', IEEE Access, vol. 9, pp. 151697-151712 . https://doi.org/10.1109/ACCESS.2021.3126777
The number of deaths caused by alcohol-related diseases may be reduced by predicting alcohol use disorder (AUD). Many researchers have worked on AUD prediction using machine learning (ML) techniques. However, to the best of our knowledge, there is a
Autor:
Amin Naemi, Mostafa Naemi, Romina Zarrabi Ekbatani, Thomas Schmidt, Ali Ebrahimi, Marjan Mansourvar, Uffe Kock Wiil
Publikováno v:
Advances in Sustainability Science and Technology ISBN: 9789811691003
Naemi, A, Naemi, M, Ekbatani, R Z, Schmidt, T, Ebrahimi, A, Mansourvar, M & Wiil, U K 2022, Forecasting the COVID-19 Spread in Iran, Italy, and Mexico Using Novel Nonlinear Autoregressive Neural Network and ARIMA-Based Hybrid Models . in Smart and Sustainable Technology for Resilient Cities and Communities . Springer, Advances in Sustainability Science and Technology, pp. 119-135 . https://doi.org/10.1007/978-981-16-9101-0_9
Naemi, A, Naemi, M, Ekbatani, R Z, Schmidt, T, Ebrahimi, A, Mansourvar, M & Wiil, U K 2022, Forecasting the COVID-19 Spread in Iran, Italy, and Mexico Using Novel Nonlinear Autoregressive Neural Network and ARIMA-Based Hybrid Models . in Smart and Sustainable Technology for Resilient Cities and Communities . Springer, Advances in Sustainability Science and Technology, pp. 119-135 . https://doi.org/10.1007/978-981-16-9101-0_9
This paper analyzes single and two-wave COVID-19 outbreaks using two novel hybrid models, which combine machine learning and statistical methods with Richards growth models, to simulate and forecast the spread of the infection. For this purpose, hist
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3142b115e2826056625ddd5406dafb8c
https://doi.org/10.1007/978-981-16-9101-0_9
https://doi.org/10.1007/978-981-16-9101-0_9
Autor:
Mostafa Naemi, Amin Naemi, Romina Zarrabi Ekbatani, Ali Ebrahimi, Thomas Schmidt, Uffe Kock Wiil
Publikováno v:
Naemi, M, Naemi, A, Ekbatani, R Z, Ebrahimi, A, Schmidt, T & Wiil, U K 2022, Modeling and Evaluating the Impact of Social Restrictions on the Spread of COVID-19 Using Machine Learning . in R J Howlett, L C Jain, J R Littlewood & M M Balas (eds), Smart and Sustainable Technology for Resilient Cities and Communities . Springer, Advances in Sustainability Science and Technology, pp. 107-118 . https://doi.org/10.1007/978-981-16-9101-0_8
Advances in Sustainability Science and Technology ISBN: 9789811691003
Advances in Sustainability Science and Technology ISBN: 9789811691003
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f6189e09db1b2f91185cc3000edde82e
https://portal.findresearcher.sdu.dk/da/publications/0ea88f29-e24c-4c74-be0e-4364174e4fd8
https://portal.findresearcher.sdu.dk/da/publications/0ea88f29-e24c-4c74-be0e-4364174e4fd8