P08. 01 Building Personalized Follow-Up Care Through AI by
Bringing the Lung Cancer Patient, Data Scientist and Oncologist
Together

J 2021

P08. 01 Building Personalized Follow-Up Care Through AI by Bringing the Lung Cancer Patient, Data Scientist and Oncologist Together

TORRENTE, M., F. FRANCO, V. CALVO, A. Collazo LORDUY, E. MENASALVAS et. al.

Základní údaje

Originální název

P08. 01 Building Personalized Follow-Up Care Through AI by Bringing the Lung Cancer Patient, Data Scientist and Oncologist Together

Autoři

TORRENTE, M., F. FRANCO, V. CALVO, A. Collazo LORDUY, E. MENASALVAS, M. E. VIDAL, P. SOUSA, J. PIMENTAO, Vít NOVÁČEK (203 Česká republika, garant, domácí), P. MINERVINI, D. FEY, L. COSTABELLO, M. POCS a M. PROVENCIO

Vydání

Journal of Thoracic Oncology, Elsevier, 2021, 1556-1380

Další údaje

Jazyk

angličtina

Typ výsledku

Článek v odborném periodiku

Obor

10200 1.2 Computer and information sciences

Stát vydavatele

Nizozemské království

Utajení

není předmětem státního či obchodního tajemství

Odkazy

URL

Impakt faktor

Impact factor: 20.121

Organizační jednotka

Fakulta informatiky

DOI

http://dx.doi.org/10.1016/j.jtho.2021.08.294

UT WoS

000709606500294

Klíčová slova anglicky

machine learning; lung cancer; relapse; relapse prediction

Štítky

Artificial Intelligence, machine learning, medical informatics, oncology, relapse prediction

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 8. 11. 2024 15:53, doc. Mgr. Bc. Vít Nováček, PhD

Anotace

V originále

Survival rates of lung cancer patients were rather poor until recent decades, when screening protocols, diagnostic techniques improvement and novel therapeutic options were developed. This leads to a new challenge: to increase lung cancer patients’ post-treatment quality of life (QoL) and well-being. We here report on a first integration of an NLP framework for the analysis and integration of comprehensive eElectronic Health Records, genomic data, open data sources, wearable devices and QoL questionnaires, in order to determine the factors that predict poor health status and design personalized interventions that will improve the patient's QoL.

Citovat

TORRENTE, M., F. FRANCO, V. CALVO, A. Collazo LORDUY, E. MENASALVAS, M. E. VIDAL, P. SOUSA, J. PIMENTAO, Vít NOVÁČEK, P. MINERVINI, D. FEY, L. COSTABELLO, M. POCS a M. PROVENCIO. P08. 01 Building Personalized Follow-Up Care Through AI by Bringing the Lung Cancer Patient, Data Scientist and Oncologist Together. Journal of Thoracic Oncology. Elsevier, 2021, roč. 16, č. 10, s. 991-992. ISSN 1556-1380. Dostupné z: https://dx.doi.org/10.1016/j.jtho.2021.08.294.

@article{1848074,
   author = {Torrente, M. and Franco, F. and Calvo, V. and Lorduy, A. Collazo and Menasalvas, E. and Vidal, M. E. and Sousa, P. and Pimentao, J. and Nováček, Vít and Minervini, P. and Fey, D. and Costabello, L. and Pocs, M. and Provencio, M.},
   article_number = {10},
   doi = {http://dx.doi.org/10.1016/j.jtho.2021.08.294},
   keywords = {machine learning; lung cancer; relapse; relapse prediction},
   language = {eng},
   issn = {1556-1380},
   journal = {Journal of Thoracic Oncology},
   title = {P08. 01 Building Personalized Follow-Up Care Through AI by Bringing the Lung Cancer Patient, Data Scientist and Oncologist Together},
   url = {https://www.jto.org/article/S1556-0864(21)02717-9/fulltext},
   volume = {16},
   year = {2021}
}

TY  - JOUR
ID  - 1848074
AU  - Torrente, M. - Franco, F. - Calvo, V. - Lorduy, A. Collazo - Menasalvas, E. - Vidal, M. E. - Sousa, P. - Pimentao, J. - Nováček, Vít - Minervini, P. - Fey, D. - Costabello, L. - Pocs, M. - Provencio, M.
PY  - 2021
TI  - P08. 01 Building Personalized Follow-Up Care Through AI by Bringing the Lung Cancer Patient, Data Scientist and Oncologist Together
JF  - Journal of Thoracic Oncology
VL  - 16
IS  - 10
SP  - 991-992
EP  - 991-992
PB  - Elsevier
SN  - 15561380
KW  - machine learning
KW  - lung cancer
KW  - relapse
KW  - relapse prediction
UR  - https://www.jto.org/article/S1556-0864(21)02717-9/fulltext
N2  - Survival rates of lung cancer patients were rather poor until recent decades, when screening protocols, diagnostic techniques improvement and novel therapeutic options were developed. This leads to a new challenge: to increase lung cancer patients’ post-treatment quality of life (QoL) and well-being. We here report on a first integration of an NLP framework for the analysis and integration of comprehensive eElectronic Health Records, genomic data, open data sources, wearable devices and QoL questionnaires, in order to determine the factors that predict poor health status and design personalized interventions that will improve the patient's QoL.
ER  -

TORRENTE, M., F. FRANCO, V. CALVO, A. Collazo LORDUY, E. MENASALVAS, M. E. VIDAL, P. SOUSA, J. PIMENTAO, Vít NOVÁČEK, P. MINERVINI, D. FEY, L. COSTABELLO, M. POCS a M. PROVENCIO. P08. 01 Building Personalized Follow-Up Care Through AI by Bringing the Lung Cancer Patient, Data Scientist and Oncologist Together. \textit{Journal of Thoracic Oncology}. Elsevier, 2021, roč.~16, č.~10, s.~991-992. ISSN~1556-1380. Dostupné z: https://dx.doi.org/10.1016/j.jtho.2021.08.294.

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