Zobrazeno 1 - 10
of 609
pro vyhledávání: '"Nirantharakumar K"'
Publikováno v:
Drug Design, Development and Therapy, Vol Volume 13, Pp 2985-2996 (2019)
Christina Antza,1,* Krishnarajah Nirantharakumar,2,* Ioannis Doundoulakis,3 Abd A Tahrani,4–6 Konstantinos A Toulis2,313rd Department of Internal Medicine, Papageorgiou Hospital, Aristotle University, Thessaloniki, Greece; 2Institute of Applied Hea
Externí odkaz:
https://doaj.org/article/aed08980dfec4693994f60876b9b20f3
Autor:
Kamarajah, S. K., Kouli, O., Ahuja, S., Blackwell, S., Dhesi, J., Docherty, A., El‐Boghdadly, K., Glasbey, J. C., McLean, K. A., Moonesinghe, S. R., Morton, B., Moug, S., Nirantharakumar, K., Pinkney, T., Spencer, S., Yeung, J., Harrison, E. M., Bhangu, A. A., Morton, D. G., Knight, S. R.
Publikováno v:
Anaesthesia; Sep2024, Vol. 79 Issue 9, p945-956, 12p
Autor:
Chandan, J.S., Thomas, T., Lee, S., Marshall, T., Willis, B., Nirantharakumar, K. *, Gill, P.
Publikováno v:
In Journal of Thrombosis and Haemostasis March 2018 16(3):474-480
Autor:
Toulis, K.A., Hanif, W., Saravanan, P., Willis, B.H., Marshall, T., Kumarendran, B., Gokhale, K., Ghosh, S., Cheng, K.K., Narendran, P., Thomas, G.N., Nirantharakumar, K.
Publikováno v:
In Diabetes and Metabolism June 2017 43(3):211-216
Autor:
Dambha-Miller, Hajira, Farmer, Andrew, Nirantharakumar, K., Jackson, T., Yau, C., Walker, L., Buchan, I., Finer, S., Barnes, M.R., Reynolds, N.J., Jun, GT, Gangadharan, S, Fraser, Simon, Guthrie, Bruce
Recent advances in causal machine learning and wider artificial intelligence (AI) methods could provide new insights into the natural histories and potential prevention of clusters of multiple long-term conditions or multimorbidity (MLTC-M). When com
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od_______348::7fb7ee9a124bbc231ed95f84426641fa
https://eprints.soton.ac.uk/477692/
https://eprints.soton.ac.uk/477692/
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Autor:
Lee, S. I., Azcoaga-Lorenzo, A., Agrawal, U., Kennedy, J. I., Fagbamigbe, A. F., Hope, H., Subramanian, A., Anand, A., Taylor, B., Nelson-Piercy, C., Damase-Michel, C., Yau, C., Crowe, F., Santorelli, G., Eastwood, K-A., Vowles, Z., Loane, M., Moss, N., Brocklehurst, P., Plachcinski, R., Thangaratinam, S., Black, M., O'Reilly, D., Abel, K. M., Brophy, S., Nirantharakumar, K., McCowan, C., MuM-PreDiCT Group
Publikováno v:
BMC Pregnancy and Childbirth, Vol 22, Iss 1, Pp 1-15 (2022)
MuM-PreDiCT Group 2022, ' Epidemiology of pre-existing multimorbidity in pregnant women in the UK in 2018 : a population-based cross-sectional study ', BMC Pregnancy and Childbirth, vol. 22, no. 1, 120 . https://doi.org/10.1186/s12884-022-04442-3
MuM-PreDiCT Group 2022, ' Epidemiology of pre-existing multimorbidity in pregnant women in the UK in 2018: a population-based cross-sectional study ', BMC Pregnancy and Childbirth, vol. 22, no. 1, 120 . https://doi.org/10.1186/s12884-022-04442-3
BMC Pregnancy and Childbirth
BMC Pregnancy and Childbirth, 2022, 22 (1), pp.120. ⟨10.1186/s12884-022-04442-3⟩
MuM-PreDiCT Group 2022, ' Epidemiology of pre-existing multimorbidity in pregnant women in the UK in 2018 : a population-based cross-sectional study ', BMC Pregnancy and Childbirth, vol. 22, no. 1, 120 . https://doi.org/10.1186/s12884-022-04442-3
MuM-PreDiCT Group 2022, ' Epidemiology of pre-existing multimorbidity in pregnant women in the UK in 2018: a population-based cross-sectional study ', BMC Pregnancy and Childbirth, vol. 22, no. 1, 120 . https://doi.org/10.1186/s12884-022-04442-3
BMC Pregnancy and Childbirth
BMC Pregnancy and Childbirth, 2022, 22 (1), pp.120. ⟨10.1186/s12884-022-04442-3⟩
Background Although maternal death is rare in the United Kingdom, 90% of these women had multiple health/social problems. This study aims to estimate the prevalence of pre-existing multimorbidity (two or more long-term physical or mental health condi
Autor:
Nichols, L, Taverner, T, Crowe, F, Richardson, S, Yau, C, Kiddle, S, Kirk, P, Barrett, J, Nirantharakumar, K, Griffin, Simon, Edwards, D, Marshall, T
Introduction A number of analytic methods have been used in large datasets to group individuals into clusters by the presence of long-term health conditions. We investigate the reproducibility and validity of these methods in a large simulated datase
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::baa6040527aa4e2352d3253ff2d4623c