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.

Basic information

Original name

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

Authors

TORRENTE, M., F. FRANCO, V. CALVO, A. Collazo LORDUY, E. MENASALVAS, M. E. VIDAL, P. SOUSA, J. PIMENTAO, Vít NOVÁČEK (203 Czech Republic, guarantor, belonging to the institution), P. MINERVINI, D. FEY, L. COSTABELLO, M. POCS and M. PROVENCIO

Edition

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

Other information

Language

English

Type of outcome

Článek v odborném periodiku

Field of Study

10200 1.2 Computer and information sciences

Country of publisher

Netherlands

Confidentiality degree

není předmětem státního či obchodního tajemství

References:

URL

Impact factor

Impact factor: 20.121

Organization unit

Faculty of Informatics

DOI

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

UT WoS

000709606500294

Keywords in English

machine learning; lung cancer; relapse; relapse prediction

Abstract

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 and 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, vol. 16, No 10, p. 991-992. ISSN 1556-1380. Available from: 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 and 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, vol.~16, No~10, p.~991-992. ISSN~1556-1380. Available from: https://dx.doi.org/10.1016/j.jtho.2021.08.294.

Detailed Information on Publication Record