Zobrazeno 1 - 10
of 10
pro vyhledávání: '"Hussam Kaka"'
Publikováno v:
Molecular Systems Biology, Vol 15, Iss 3, Pp 1-11 (2019)
Abstract Patient classification has widespread biomedical and clinical applications, including diagnosis, prognosis, and treatment response prediction. A clinically useful prediction algorithm should be accurate, generalizable, be able to integrate d
Externí odkaz:
https://doaj.org/article/0309d2c4a4e24c699be79e46aa0bf371
Autor:
Shraddha Pai, Philipp Weber, Ruth Isserlin, Hussam Kaka, Shirley Hui, Muhammad Ahmad Shah, Luca Giudice, Rosalba Giugno, Anne Krogh Nøhr, Jan Baumbach, Gary D. Bader
Publikováno v:
F1000Research, Vol 9 (2021)
Patient classification based on clinical and genomic data will further the goal of precision medicine. Interpretability is of particular relevance for models based on genomic data, where sample sizes are relatively small (in the hundreds), increasing
Externí odkaz:
https://doaj.org/article/bb8150438e25444db549391763589b1f
Publikováno v:
Diagnostic and Interventional Radiology, Vol 23, Iss 6, Pp 435-440 (2017)
PURPOSE:We aimed to determine the publication rate and factors predictive of publication of oral presentations at the annual meetings of the Cardiovascular and Interventional Radiology Society of Europe (CIRSE) and the Society of Interventional Radio
Externí odkaz:
https://doaj.org/article/a2701b5fc9ff41779c94c577001cc755
Autor:
Hussam Kaka, George Michalopoulos, Sujan Subendran, Kathleen Decker, Pascal Lambert, Marshall Pitz, Harminder Singh, Helen Chen
Cancer recurrence is the diagnosis of a second clinical episode of cancer after the first was considered cured. Identifying patients who had experienced cancer recurrence is an important task as it can be used to compare treatment effectiveness, meas
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ec61b7007e4f2a0d9c71ccb6bb97890a
https://doi.org/10.3233/shti220403
https://doi.org/10.3233/shti220403
Publikováno v:
Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques. 49:460-462
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::54fc93de5d69b0bacdfbea2622018bf0
https://doi.org/10.1017/cjn.2021.135
https://doi.org/10.1017/cjn.2021.135
Publikováno v:
Canadian Association of Radiologists Journal. 72:35-44
There have been many recently published studies exploring machine learning (ML) and deep learning applications within neuroradiology. The improvement in performance of these techniques has resulted in an ever-increasing number of commercially availab
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::40840262f58543a4da1adcdd099d4bb4
https://doi.org/10.1177/0846537120954293
https://doi.org/10.1177/0846537120954293
Publikováno v:
NAACL-HLT
Contextual word embedding models, such as BioBERT and Bio_ClinicalBERT, have achieved state-of-the-art results in biomedical natural language processing tasks by focusing their pre-training process on domain-specific corpora. However, such models do
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a34c307ebbe67cbb50b339c7f329fd4e
https://doi.org/10.18653/v1/2021.naacl-main.139
https://doi.org/10.18653/v1/2021.naacl-main.139
Publikováno v:
Molecular Systems Biology
Patient classification has widespread biomedical and clinical applications, including diagnosis, prognosis and treatment response prediction. A clinically useful prediction algorithm should be accurate, generalizable, be able to integrate diverse dat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fc086132397f8d858a47a84899d37078
https://pubmed.ncbi.nlm.nih.gov/30872331
https://pubmed.ncbi.nlm.nih.gov/30872331
Autor:
Shirley Hui, Hussam Kaka, Philipp Weber, Anne Krogh Nøhr, Ruth Isserlin, Jan Baumbach, Luca Giudice, Rosalba Giugno, Gary D. Bader, Shraddha Pai, Muhammad Ahmad Shah
Publikováno v:
F1000Research
Pai, S, Weber, P, Isserlin, R, Kaka, H, Hui, S, Shah, M A, Giudice, L, Giugno, R, Nøhr, A K, Baumbach, J & Bader, G D 2021, ' netDx : Software for building interpretable patient classifiers by multi-'omic data integration using patient similarity networks ', F1000Research, vol. 9, 1239 . https://doi.org/10.12688/f1000research.26429.2
Pai, S, Weber, P, Isserlin, R, Kaka, H, Hui, S, Shah, M A, Giudice, L, Giugno, R, Nøhr, A K, Baumbach, J & Bader, G D 2021, ' netDx : Software for building interpretable patient classifiers by multi-'omic data integration using patient similarity networks ', F1000Research, vol. 9, 1239 . https://doi.org/10.12688/f1000research.26429.2
Patient classification based on clinical and genomic data will further the goal of precision medicine. Interpretability is of particular relevance for models based on genomic data, where sample sizes are relatively small (in the hundreds), increasing
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ad27d0da7f2e76c9c917c8d52968bcb7
https://doi.org/10.12688/f1000research.26429.2
https://doi.org/10.12688/f1000research.26429.2
Autor:
Amir Haddad, Ophir Vinik, Hussam Kaka, Maithy Tran, Vinod Chandran, Zahi Touma, Maria Bagovich, Dafna D. Gladman, Mansour Somaily, Renise Ayearst
Publikováno v:
International journal of technology assessment in health care. 31(1-2)
Objectives: Clinical research data are often collected on paper and later inputted onto an electronic database. This method is time consuming and potentially introduces errors. Therefore, to make primary data collection more efficient and less error
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::15b3efce2196a0386ae3dc4ec3e35a81
https://pubmed.ncbi.nlm.nih.gov/25991410
https://pubmed.ncbi.nlm.nih.gov/25991410