Oncology

Lung cancer

Lung cancer

Lung cancer had the highest economic cost (€18.8 billion, 15% of overall cancer costs)1. Many of these patients have comorbidities but they are often underrepresented in clinical trials. As a result, the management is frequently suboptimal and not personalized, affecting also overall survival2.

Highest economic cost

bigmedilytics-lung-cancer- test-tubes

Underrepresented patients

Not personalized management

Low survival

Led by National Centre of Scientific Research “Demokritos”, the aim of this pilot is to improve the management of patients with lung cancer during their treatment, follow-up and in their last period of life. It will improve not only their experience and satisfaction, and main outcomes, but also save substantial health costs.

For this purpose, the goal of the project is to reduce admissions and readmission, thus reducing highly expensive emergency care and hospitalizations.

Why a big data approach is needed

It is essential to integrate multiple sources of data; analyse non-structured data, clinical notes and literature; extract patterns to predict toxicity, adverse effects and interactions of drugs; search large-scale text corpora.

Participants
ATC
Campus Excellence UPM
Hospital U Puerta Hierro SERMAS
Leibniz Universität Hannover
Leader
NCSR Demokritos

Publications

Integrating Speculation Detection and Deep Learning to Extract Lung Cancer Diagnosis from Clinical Notes

01/02/2021

Solarte Pabón, O.; Torrente, M.; Provencio, M.; Rodríguez-Gonzalez, A.; Menasalvas, E. Integrating Speculation Detection and Deep Learning to Extract Lung Cancer Diagnosis from Clinical Notes. Appl. Sci. 202111, 865. https://doi.org/10.3390/app11020865

Pilot 7: Lung cancer

04/10/2019

Poster at BigMedilytics event: “Big Data: Fueling the transformation of Europe’s Healthcare Sector”. September 4-5, 2019, Valencia, Spain


1 Source: Luengo-Fernandez, R., Leal, J., Gray, A., & Sullivan, R. (2013). Economic burden of cancer across the European Union: a population-based cost analysis. The Lancet Oncology, 14(12), 1165–1174.

2 Source: Søgaard M, Thomsen RW, Bossen KS, Sørensen HT, Nørgaard M. The impact of comorbidity on cancer survival: a review. Clinical Epidemiology. 2013;5(Suppl 1):3-29. doi:10.2147/CLEP.S47150

 

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Improving the healthcare continuum of lung cancer patients at all stages through big data analytics