Identification of smokingstatus from routine blood test results using deep neural network analysis.

N. Skjodt (Lethbridge, Canada), P. Mamoshina (Baltimore, United States of America), K. Kochetov (Baltimore, United States of America), F. Cortese (Boston, United States of America), A. Kovalchuk (Lethbridge, Canada), A. Aliper (Baltimore, United States of America), E. Putin (Baltimore, United States of America), M. Scheibye-Knudsen (Copenhagen, Denmark), C. Cantor (Boston, United States of America), A. Zhavoronkov (Baltimore, United States of America), O. Kovalchuk (Lethbridge, Canada)

Source: International Congress 2018 – Novel findings in biomarkers of tobacco use, exposure, hazards and genetics
Session: Novel findings in biomarkers of tobacco use, exposure, hazards and genetics
Session type: Oral Presentation
Number: 3814
Disease area: Airway diseases

Congress or journal article abstractWebcastSlide presentation

Rating: 0
You must login to grade this presentation.

Share or cite this content

Citations should be made in the following way:
N. Skjodt (Lethbridge, Canada), P. Mamoshina (Baltimore, United States of America), K. Kochetov (Baltimore, United States of America), F. Cortese (Boston, United States of America), A. Kovalchuk (Lethbridge, Canada), A. Aliper (Baltimore, United States of America), E. Putin (Baltimore, United States of America), M. Scheibye-Knudsen (Copenhagen, Denmark), C. Cantor (Boston, United States of America), A. Zhavoronkov (Baltimore, United States of America), O. Kovalchuk (Lethbridge, Canada). Identification of smokingstatus from routine blood test results using deep neural network analysis.. 3814

You must login to share this Presentation/Article on Twitter, Facebook, LinkedIn or by email.

Member's Comments

No comment yet.
You must Login to comment this presentation.


Related content which might interest you:
Smoking causes early biological aging: a deep neural network analysis of common blood test results
Source: International Congress 2018 – Novel findings in biomarkers of tobacco use, exposure, hazards and genetics
Year: 2018



Classification of spirometric tests results using neural network
Source: Annual Congress 2005 - Spirometry - now and in the future
Year: 2005


Improving lung cancer diagnosis from exhaled-breath analysis by adding clinical parameters to the artificial neural network
Source: International Congress 2019 – Diagnostic procedures and biology of lung cancer
Year: 2019



A fully automatic deep learning system for COVID-19 diagnostic and prognostic analysis
Source: Eur Respir J, 56 (2) 2000775; 10.1183/13993003.00775-2020
Year: 2020



A simplified approach to the interpretation of arterial blood gas analysis
Source: Breathe 2009; 6: 14-23
Year: 2009


Explaining predictions of an automated pulmonary function test interpretation algorithm
Source: International Congress 2019 – M-health/e-health I
Year: 2019

Identification of pleural infection microbiological patterns by applying next generation sequencing and bioinformatics analysis
Source: Virtual Congress 2020 – Antibiotic therapy for pneumonia and pleural infections
Year: 2020



A comparison of unsupervised methods based on dichotomous data to identify clusters of airways symptoms: latent class analysis and partitioning around medoids methods.
Source: International Congress 2018 – Airway disease: recent discoveries
Year: 2018


Comparison of artificial neural networks and a standardized questionnaire for the diagnosis of COPD.
Source: International Congress 2019 – Evaluation of basic science in airway diseases
Year: 2019


Are estimations of radiomic image markers dispensable due to recent deep learning findings?
Source: Eur Respir J, 54 (2) 1901185; 10.1183/13993003.01185-2019
Year: 2019



Screening tool for lung cancer based on feed-forward neural network regarding semiquantitative cytochemical analysis of alveolar macrophages
Source: Annual Congress 2009 - Bronchoalveolar lavage and phenotyping in diffuse parenchymal lung disease
Year: 2009


Visualization and quantitative analysis of the alveolar capillary network – Implications for lung developmental biology
Source: International Congress 2017 – Postnatal lung growth and development
Year: 2017

Application of machine learning algorithms to predict loss of asthma control: A post-hoc analysis of INCONTRO study
Source: Virtual Congress 2020 – Clinical characteristics and diagnostic tools for phenotyping asthma and COPD
Year: 2020


Quantitative bacterial analysis, validation of a simple method
Source: Eur Respir J 2004; 24: Suppl. 48, 80s
Year: 2004

Association of texture-based quantitative fibrotic patterns and pulmonary function test in a new validation set
Source: Annual Congress 2011 - Functional lung and pulmonary arterial imaging
Year: 2011

How can the data derived Bayesian network model help for screening of respiratory diseases?
Source: International Congress 2019 – Innovations in primary care assessment and management
Year: 2019


Decision tree models as a classifier of endothelial function based on strength, pulmonary and cardiac function in COPD: Preliminary analysis.
Source: Virtual Congress 2020 – Tapas of respiratory physiotherapy
Year: 2020


Functional chloride secretion, CFTR-sequencing and transcript analysis in patients with inconclusive results in sweat test and CFTR-mutation screening
Source: Eur Respir J 2005; 26: Suppl. 49, 402s
Year: 2005