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Automatic prediction of chronic obstructive pulmonary disease exacerbations through home telemonitoring of symptoms.

Abstract Chronic Obstructive Pulmonary Disease (COPD) is a progressive disease of the lung with a great prevalence and a remarkable socio-economic impact on patients and health systems. Early detection of exacerbations could diminish the adverse effects on patients' health and cut down costs burdened on patients with COPD. A group of 16 patients were telemonitored at home using a novel electronic daily symptoms questionnaire during a 6-months field trial. Recorded data were used to train and validate a Probabilistic Neural Network (PNN) classifier in order to enable the automatic prediction of exacerbations. The proposed system was able to predict COPD exacerbations early with a margin of 4.8 ? 1.8 days (average ? SD). Detection accuracy was 80.5% (33 out of 41 exacerbations were early detected); 78.8% (26 out of 33) of theses detected events were reported exacerbation and 87.5% (7 out of 8) were unreported episodes. The proposed questionnaire and the designed automatic classifier could support the early detection of COPD exacerbations of benefit to both physicians and patients.
PMID
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Authors

Mayor MeshTerms

Medical Records

Surveys and Questionnaires

User-Computer Interface

Keywords

Chronic Obstructive Pulmonary Disease

Probabilistic Neural Network

early detection

exacerbation

questionnaire

symptoms

telehealth

telemonitoring

Journal Title bio-medical materials and engineering
Publication Year Start
%A Fern?ndez-Granero, M. A.; S?nchez-Morillo, D.; Le?n-Jim?nez, A.; Crespo, L. F.
%T Automatic prediction of chronic obstructive pulmonary disease exacerbations through home telemonitoring of symptoms.
%J Bio-medical materials and engineering, vol. 24, no. 6, pp. 3825-3832
%D 00/2014
%V 24
%N 6
%M eng
%B Chronic Obstructive Pulmonary Disease (COPD) is a progressive disease of the lung with a great prevalence and a remarkable socio-economic impact on patients and health systems. Early detection of exacerbations could diminish the adverse effects on patients' health and cut down costs burdened on patients with COPD. A group of 16 patients were telemonitored at home using a novel electronic daily symptoms questionnaire during a 6-months field trial. Recorded data were used to train and validate a Probabilistic Neural Network (PNN) classifier in order to enable the automatic prediction of exacerbations. The proposed system was able to predict COPD exacerbations early with a margin of 4.8 ? 1.8 days (average ? SD). Detection accuracy was 80.5% (33 out of 41 exacerbations were early detected); 78.8% (26 out of 33) of theses detected events were reported exacerbation and 87.5% (7 out of 8) were unreported episodes. The proposed questionnaire and the designed automatic classifier could support the early detection of COPD exacerbations of benefit to both physicians and patients.
%K Aged, Aged, 80 and over, Diagnosis, Computer-Assisted, Diagnostic Self Evaluation, Female, Humans, Male, Medical Records, Middle Aged, Pattern Recognition, Automated, Pulmonary Disease, Chronic Obstructive, Recurrence, Remote Consultation, Reproducibility of Results, Self Care, Sensitivity and Specificity, Surveys and Questionnaires, User-Computer Interface
%P 3825
%L 3832
%Y 10.3233/BME-141212
%W PHY
%G AUTHOR
%R 2014.......24.3825F

@Article{Fern?ndez-Granero2014,
author="Fern{\'a}ndez-Granero, M. A.
and S{\'a}nchez-Morillo, D.
and Le{\'o}n-Jim{\'e}nez, A.
and Crespo, L. F.",
title="Automatic prediction of chronic obstructive pulmonary disease exacerbations through home telemonitoring of symptoms.",
journal="Bio-medical materials and engineering",
year="2014",
volume="24",
number="6",
pages="3825--3832",
keywords="Aged",
keywords="Aged, 80 and over",
keywords="Diagnosis, Computer-Assisted",
keywords="Diagnostic Self Evaluation",
keywords="Female",
keywords="Humans",
keywords="Male",
keywords="Medical Records",
keywords="Middle Aged",
keywords="Pattern Recognition, Automated",
keywords="Pulmonary Disease, Chronic Obstructive",
keywords="Recurrence",
keywords="Remote Consultation",
keywords="Reproducibility of Results",
keywords="Self Care",
keywords="Sensitivity and Specificity",
keywords="Surveys and Questionnaires",
keywords="User-Computer Interface",
abstract="Chronic Obstructive Pulmonary Disease (COPD) is a progressive disease of the lung with a great prevalence and a remarkable socio-economic impact on patients and health systems. Early detection of exacerbations could diminish the adverse effects on patients' health and cut down costs burdened on patients with COPD. A group of 16 patients were telemonitored at home using a novel electronic daily symptoms questionnaire during a 6-months field trial. Recorded data were used to train and validate a Probabilistic Neural Network (PNN) classifier in order to enable the automatic prediction of exacerbations. The proposed system was able to predict COPD exacerbations early with a margin of 4.8 {\textpm} 1.8 days (average {\textpm} SD). Detection accuracy was 80.5\% (33 out of 41 exacerbations were early detected); 78.8\% (26 out of 33) of theses detected events were reported exacerbation and 87.5\% (7 out of 8) were unreported episodes. The proposed questionnaire and the designed automatic classifier could support the early detection of COPD exacerbations of benefit to both physicians and patients.",
issn="1878-3619",
doi="10.3233/BME-141212",
url="http://www.ncbi.nlm.nih.gov/pubmed/25227099",
language="eng"
}

%0 Journal Article
%T Automatic prediction of chronic obstructive pulmonary disease exacerbations through home telemonitoring of symptoms.
%A Fern?ndez-Granero, M. A.
%A S?nchez-Morillo, D.
%A Le?n-Jim?nez, A.
%A Crespo, L. F.
%J Bio-medical materials and engineering
%D 2014
%V 24
%N 6
%@ 1878-3619
%G eng
%F Fern?ndez-Granero2014
%X Chronic Obstructive Pulmonary Disease (COPD) is a progressive disease of the lung with a great prevalence and a remarkable socio-economic impact on patients and health systems. Early detection of exacerbations could diminish the adverse effects on patients' health and cut down costs burdened on patients with COPD. A group of 16 patients were telemonitored at home using a novel electronic daily symptoms questionnaire during a 6-months field trial. Recorded data were used to train and validate a Probabilistic Neural Network (PNN) classifier in order to enable the automatic prediction of exacerbations. The proposed system was able to predict COPD exacerbations early with a margin of 4.8 ? 1.8 days (average ? SD). Detection accuracy was 80.5% (33 out of 41 exacerbations were early detected); 78.8% (26 out of 33) of theses detected events were reported exacerbation and 87.5% (7 out of 8) were unreported episodes. The proposed questionnaire and the designed automatic classifier could support the early detection of COPD exacerbations of benefit to both physicians and patients.
%K Aged
%K Aged, 80 and over
%K Diagnosis, Computer-Assisted
%K Diagnostic Self Evaluation
%K Female
%K Humans
%K Male
%K Medical Records
%K Middle Aged
%K Pattern Recognition, Automated
%K Pulmonary Disease, Chronic Obstructive
%K Recurrence
%K Remote Consultation
%K Reproducibility of Results
%K Self Care
%K Sensitivity and Specificity
%K Surveys and Questionnaires
%K User-Computer Interface
%U http://dx.doi.org/10.3233/BME-141212
%U http://www.ncbi.nlm.nih.gov/pubmed/25227099
%P 3825-3832

PT Journal
AU Fern?ndez-Granero, MA
   S?nchez-Morillo, D
   Le?n-Jim?nez, A
   Crespo, LF
TI Automatic prediction of chronic obstructive pulmonary disease exacerbations through home telemonitoring of symptoms.
SO Bio-medical materials and engineering
JI Biomed Mater Eng
PY 2014
BP 3825
EP 3832
VL 24
IS 6
DI 10.3233/BME-141212
LA eng
DE Aged; Aged, 80 and over; Diagnosis, Computer-Assisted; Diagnostic Self Evaluation; Female; Humans; Male; Medical Records; Middle Aged; Pattern Recognition, Automated; Pulmonary Disease, Chronic Obstructive; Recurrence; Remote Consultation; Reproducibility of Results; Self Care; Sensitivity and Specificity; Surveys and Questionnaires; User-Computer Interface
AB Chronic Obstructive Pulmonary Disease (COPD) is a progressive disease of the lung with a great prevalence and a remarkable socio-economic impact on patients and health systems. Early detection of exacerbations could diminish the adverse effects on patients' health and cut down costs burdened on patients with COPD. A group of 16 patients were telemonitored at home using a novel electronic daily symptoms questionnaire during a 6-months field trial. Recorded data were used to train and validate a Probabilistic Neural Network (PNN) classifier in order to enable the automatic prediction of exacerbations. The proposed system was able to predict COPD exacerbations early with a margin of 4.8 ? 1.8 days (average ? SD). Detection accuracy was 80.5% (33 out of 41 exacerbations were early detected); 78.8% (26 out of 33) of theses detected events were reported exacerbation and 87.5% (7 out of 8) were unreported episodes. The proposed questionnaire and the designed automatic classifier could support the early detection of COPD exacerbations of benefit to both physicians and patients.
ER

PMID- 25227099
OWN - NLM
STAT- MEDLINE
DA  - 20140917
DCOM- 20150624
LR  - 20151119
IS  - 1878-3619 (Electronic)
IS  - 0959-2989 (Linking)
VI  - 24
IP  - 6
DP  - 2014
TI  - Automatic prediction of chronic obstructive pulmonary disease exacerbations
      through home telemonitoring of symptoms.
PG  - 3825-32
LID - 10.3233/BME-141212 [doi]
AB  - Chronic Obstructive Pulmonary Disease (COPD) is a progressive disease of the lung
      with a great prevalence and a remarkable socio-economic impact on patients and
      health systems. Early detection of exacerbations could diminish the adverse
      effects on patients' health and cut down costs burdened on patients with COPD. A 
      group of 16 patients were telemonitored at home using a novel electronic daily
      symptoms questionnaire during a 6-months field trial. Recorded data were used to 
      train and validate a Probabilistic Neural Network (PNN) classifier in order to
      enable the automatic prediction of exacerbations. The proposed system was able to
      predict COPD exacerbations early with a margin of 4.8 +/- 1.8 days (average +/-
      SD). Detection accuracy was 80.5% (33 out of 41 exacerbations were early
      detected); 78.8% (26 out of 33) of theses detected events were reported
      exacerbation and 87.5% (7 out of 8) were unreported episodes. The proposed
      questionnaire and the designed automatic classifier could support the early
      detection of COPD exacerbations of benefit to both physicians and patients.
FAU - Fernandez-Granero, M A
AU  - Fernandez-Granero MA
AD  - Biomedical Engineering and Telemedicine Research Group, University of Cadiz,
      Cadiz, Spain.
FAU - Sanchez-Morillo, D
AU  - Sanchez-Morillo D
AD  - Biomedical Engineering and Telemedicine Research Group, University of Cadiz,
      Cadiz, Spain.
FAU - Leon-Jimenez, A
AU  - Leon-Jimenez A
AD  - Pulmonology and Allergy Unit, Puerta del Mar University Hospital, Cadiz, Spain.
FAU - Crespo, L F
AU  - Crespo LF
AD  - Biomedical Engineering and Telemedicine Research Group, University of Cadiz,
      Cadiz, Spain.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
PL  - Netherlands
TA  - Biomed Mater Eng
JT  - Bio-medical materials and engineering
JID - 9104021
SB  - IM
MH  - Aged
MH  - Aged, 80 and over
MH  - Diagnosis, Computer-Assisted/*methods
MH  - Diagnostic Self Evaluation
MH  - Female
MH  - Humans
MH  - Male
MH  - *Medical Records
MH  - Middle Aged
MH  - Pattern Recognition, Automated/methods
MH  - Pulmonary Disease, Chronic Obstructive/*diagnosis
MH  - Recurrence
MH  - Remote Consultation/*methods
MH  - Reproducibility of Results
MH  - Self Care/*methods
MH  - Sensitivity and Specificity
MH  - *Surveys and Questionnaires
MH  - *User-Computer Interface
OTO - NOTNLM
OT  - Chronic Obstructive Pulmonary Disease
OT  - Probabilistic Neural Network
OT  - early detection
OT  - exacerbation
OT  - questionnaire
OT  - symptoms
OT  - telehealth
OT  - telemonitoring
EDAT- 2014/09/18 06:00
MHDA- 2015/06/25 06:00
CRDT- 2014/09/18 06:00
AID - 0Q4K48760256H511 [pii]
AID - 10.3233/BME-141212 [doi]
PST - ppublish
SO  - Biomed Mater Eng. 2014;24(6):3825-32. doi: 10.3233/BME-141212.
TY  - JOUR
AU  - Fern?ndez-Granero, M. A.
AU  - S?nchez-Morillo, D.
AU  - Le?n-Jim?nez, A.
AU  - Crespo, L. F.
PY  - 2014//
TI  - Automatic prediction of chronic obstructive pulmonary disease exacerbations through home telemonitoring of symptoms.
T2  - Biomed Mater Eng
JO  - Bio-medical materials and engineering
SP  - 3825
EP  - 3832
VL  - 24
IS  - 6
KW  - Aged
KW  - Aged, 80 and over
KW  - Diagnosis, Computer-Assisted
KW  - Diagnostic Self Evaluation
KW  - Female
KW  - Humans
KW  - Male
KW  - Medical Records
KW  - Middle Aged
KW  - Pattern Recognition, Automated
KW  - Pulmonary Disease, Chronic Obstructive
KW  - Recurrence
KW  - Remote Consultation
KW  - Reproducibility of Results
KW  - Self Care
KW  - Sensitivity and Specificity
KW  - Surveys and Questionnaires
KW  - User-Computer Interface
N2  - Chronic Obstructive Pulmonary Disease (COPD) is a progressive disease of the lung with a great prevalence and a remarkable socio-economic impact on patients and health systems. Early detection of exacerbations could diminish the adverse effects on patients' health and cut down costs burdened on patients with COPD. A group of 16 patients were telemonitored at home using a novel electronic daily symptoms questionnaire during a 6-months field trial. Recorded data were used to train and validate a Probabilistic Neural Network (PNN) classifier in order to enable the automatic prediction of exacerbations. The proposed system was able to predict COPD exacerbations early with a margin of 4.8 ? 1.8 days (average ? SD). Detection accuracy was 80.5% (33 out of 41 exacerbations were early detected); 78.8% (26 out of 33) of theses detected events were reported exacerbation and 87.5% (7 out of 8) were unreported episodes. The proposed questionnaire and the designed automatic classifier could support the early detection of COPD exacerbations of benefit to both physicians and patients.
SN  - 1878-3619
UR  - http://dx.doi.org/10.3233/BME-141212
UR  - http://www.ncbi.nlm.nih.gov/pubmed/25227099
ID  - Fern?ndez-Granero2014
ER  - 
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