Using Data-Driven Rules to Predict Mortality in Severe Community Acquired Pneumonia
Identifieur interne : 000947 ( Main/Exploration ); précédent : 000946; suivant : 000948Using Data-Driven Rules to Predict Mortality in Severe Community Acquired Pneumonia
Auteurs : Chuang Wu [États-Unis] ; Roni Rosenfeld [États-Unis] ; Gilles Clermont [États-Unis]Source :
- PLoS ONE [ 1932-6203 ] ; 2014.
Descripteurs français
- KwdFr :
- Adolescent, Bases de données factuelles, Courbe ROC, Hospitalisation, Humains, Indice de gravité médicale, Infections communautaires (), Infections communautaires (microbiologie), Infections communautaires (mortalité), Intelligence artificielle, Modèles statistiques, Pneumopathie infectieuse (), Pneumopathie infectieuse (microbiologie), Pneumopathie infectieuse (mortalité), Prise de décision, Taux de survie, Études de cohortes, Évaluation de résultat (soins).
- MESH :
- microbiologie : Infections communautaires, Pneumopathie infectieuse.
- mortalité : Infections communautaires, Pneumopathie infectieuse.
- Adolescent, Bases de données factuelles, Courbe ROC, Hospitalisation, Humains, Indice de gravité médicale, Infections communautaires, Intelligence artificielle, Modèles statistiques, Pneumopathie infectieuse, Prise de décision, Taux de survie, Études de cohortes, Évaluation de résultat (soins).
English descriptors
- KwdEn :
- Adolescent, Artificial Intelligence, Cohort Studies, Community-Acquired Infections (classification), Community-Acquired Infections (microbiology), Community-Acquired Infections (mortality), Databases, Factual, Decision Making, Hospitalization, Humans, Models, Statistical, Outcome Assessment (Health Care), Pneumonia (classification), Pneumonia (microbiology), Pneumonia (mortality), ROC Curve, Severity of Illness Index, Survival Rate.
- MESH :
- classification : Community-Acquired Infections, Pneumonia.
- microbiology : Community-Acquired Infections, Pneumonia.
- mortality : Community-Acquired Infections, Pneumonia.
- Adolescent, Artificial Intelligence, Cohort Studies, Databases, Factual, Decision Making, Hospitalization, Humans, Models, Statistical, Outcome Assessment (Health Care), ROC Curve, Severity of Illness Index, Survival Rate.
Abstract
Prediction of patient-centered outcomes in hospitals is useful for performance benchmarking, resource allocation, and guidance regarding active treatment and withdrawal of care. Yet, their use by clinicians is limited by the complexity of available tools and amount of data required. We propose to use Disjunctive Normal Forms as a novel approach to predict hospital and 90-day mortality from instance-based patient data, comprising demographic, genetic, and physiologic information in a large cohort of patients admitted with severe community acquired pneumonia. We develop two algorithms to efficiently learn Disjunctive Normal Forms, which yield easy-to-interpret rules that explicitly map data to the outcome of interest. Disjunctive Normal Forms achieve higher prediction performance quality compared to a set of state-of-the-art machine learning models, and unveils insights unavailable with standard methods. Disjunctive Normal Forms constitute an intuitive set of prediction rules that could be easily implemented to predict outcomes and guide criteria-based clinical decision making and clinical trial execution, and thus of greater practical usefulness than currently available prediction tools. The Java implementation of the tool JavaDNF will be publicly available.
Url:
DOI: 10.1371/journal.pone.0089053
PubMed: 24699007
PubMed Central: 3974677
Affiliations:
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Le document en format XML
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Pneumonia</title><author><name sortKey="Wu, Chuang" sort="Wu, Chuang" uniqKey="Wu C" first="Chuang" last="Wu">Chuang Wu</name><affiliation wicri:level="4"><nlm:aff id="aff1"><addr-line>School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America</addr-line></nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania</wicri:regionArea><placeName><region type="state">Pennsylvanie</region><settlement type="city">Pittsburgh</settlement></placeName><orgName type="university">Université Carnegie-Mellon</orgName></affiliation></author><author><name sortKey="Rosenfeld, Roni" sort="Rosenfeld, Roni" uniqKey="Rosenfeld R" first="Roni" last="Rosenfeld">Roni Rosenfeld</name><affiliation wicri:level="4"><nlm:aff id="aff1"><addr-line>School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America</addr-line></nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania</wicri:regionArea><placeName><region type="state">Pennsylvanie</region><settlement type="city">Pittsburgh</settlement></placeName><orgName type="university">Université Carnegie-Mellon</orgName></affiliation></author><author><name sortKey="Clermont, Gilles" sort="Clermont, Gilles" uniqKey="Clermont G" first="Gilles" last="Clermont">Gilles Clermont</name><affiliation wicri:level="4"><nlm:aff id="aff2"><addr-line>Departments of Critical Care Medicine, Industrial Engineering, and Mathematics, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America</addr-line></nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Departments of Critical Care Medicine, Industrial Engineering, and Mathematics, University of Pittsburgh, Pittsburgh, Pennsylvania</wicri:regionArea><placeName><region type="state">Pennsylvanie</region><settlement type="city">Pittsburgh</settlement></placeName><orgName type="university">Université de Pittsburgh</orgName></affiliation></author></analytic><series><title level="j">PLoS ONE</title><idno type="eISSN">1932-6203</idno><imprint><date when="2014">2014</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Adolescent</term><term>Artificial Intelligence</term><term>Cohort Studies</term><term>Community-Acquired Infections (classification)</term><term>Community-Acquired Infections (microbiology)</term><term>Community-Acquired Infections (mortality)</term><term>Databases, Factual</term><term>Decision Making</term><term>Hospitalization</term><term>Humans</term><term>Models, Statistical</term><term>Outcome Assessment (Health Care)</term><term>Pneumonia (classification)</term><term>Pneumonia (microbiology)</term><term>Pneumonia (mortality)</term><term>ROC Curve</term><term>Severity of Illness Index</term><term>Survival Rate</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Adolescent</term><term>Bases de données factuelles</term><term>Courbe ROC</term><term>Hospitalisation</term><term>Humains</term><term>Indice de gravité médicale</term><term>Infections communautaires ()</term><term>Infections communautaires (microbiologie)</term><term>Infections communautaires (mortalité)</term><term>Intelligence artificielle</term><term>Modèles statistiques</term><term>Pneumopathie infectieuse ()</term><term>Pneumopathie infectieuse (microbiologie)</term><term>Pneumopathie infectieuse (mortalité)</term><term>Prise de décision</term><term>Taux de survie</term><term>Études de cohortes</term><term>Évaluation de résultat (soins)</term></keywords><keywords scheme="MESH" qualifier="classification" xml:lang="en"><term>Community-Acquired Infections</term><term>Pneumonia</term></keywords><keywords scheme="MESH" qualifier="microbiologie" xml:lang="fr"><term>Infections communautaires</term><term>Pneumopathie infectieuse</term></keywords><keywords scheme="MESH" qualifier="microbiology" xml:lang="en"><term>Community-Acquired Infections</term><term>Pneumonia</term></keywords><keywords scheme="MESH" qualifier="mortality" xml:lang="en"><term>Community-Acquired Infections</term><term>Pneumonia</term></keywords><keywords scheme="MESH" qualifier="mortalité" xml:lang="fr"><term>Infections communautaires</term><term>Pneumopathie infectieuse</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Adolescent</term><term>Artificial Intelligence</term><term>Cohort Studies</term><term>Databases, Factual</term><term>Decision Making</term><term>Hospitalization</term><term>Humans</term><term>Models, Statistical</term><term>Outcome Assessment (Health Care)</term><term>ROC Curve</term><term>Severity of Illness Index</term><term>Survival Rate</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Adolescent</term><term>Bases de données factuelles</term><term>Courbe ROC</term><term>Hospitalisation</term><term>Humains</term><term>Indice de gravité médicale</term><term>Infections communautaires</term><term>Intelligence artificielle</term><term>Modèles statistiques</term><term>Pneumopathie infectieuse</term><term>Prise de décision</term><term>Taux de survie</term><term>Études de cohortes</term><term>Évaluation de résultat (soins)</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p>Prediction of patient-centered outcomes in hospitals is useful for performance benchmarking, resource allocation, and guidance regarding active treatment and withdrawal of care. Yet, their use by clinicians is limited by the complexity of available tools and amount of data required. We propose to use Disjunctive Normal Forms as a novel approach to predict hospital and 90-day mortality from instance-based patient data, comprising demographic, genetic, and physiologic information in a large cohort of patients admitted with severe community acquired pneumonia. We develop two algorithms to efficiently learn Disjunctive Normal Forms, which yield easy-to-interpret rules that explicitly map data to the outcome of interest. Disjunctive Normal Forms achieve higher prediction performance quality compared to a set of state-of-the-art machine learning models, and unveils insights unavailable with standard methods. Disjunctive Normal Forms constitute an intuitive set of prediction rules that could be easily implemented to predict outcomes and guide criteria-based clinical decision making and clinical trial execution, and thus of greater practical usefulness than currently available prediction tools. The Java implementation of the tool JavaDNF will be publicly available.</p></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Levy, Mm" uniqKey="Levy M">MM Levy</name></author><author><name sortKey="Fink, Mp" uniqKey="Fink M">MP Fink</name></author><author><name sortKey="Marshall, Jc" uniqKey="Marshall J">JC Marshall</name></author><author><name sortKey="Abraham, E" uniqKey="Abraham E">E Abraham</name></author><author><name sortKey="Angus, D" uniqKey="Angus D">D Angus</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Angus, Dc" uniqKey="Angus D">DC Angus</name></author><author><name sortKey="Linde Zwirble, Wt" uniqKey="Linde Zwirble W">WT Linde-Zwirble</name></author><author><name sortKey="Lidicker, J" uniqKey="Lidicker J">J Lidicker</name></author><author><name sortKey="Clermont, G" uniqKey="Clermont G">G Clermont</name></author><author><name sortKey="Carcillo, J" uniqKey="Carcillo J">J 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Dean</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Angus, Dc" uniqKey="Angus D">DC Angus</name></author><author><name sortKey="Laterre, Pf" uniqKey="Laterre P">PF Laterre</name></author><author><name sortKey="Helterbrand, J" uniqKey="Helterbrand J">J Helterbrand</name></author><author><name sortKey="Ely, Ew" uniqKey="Ely E">EW Ely</name></author><author><name sortKey="Ball, De" uniqKey="Ball D">DE Ball</name></author></analytic></biblStruct><biblStruct></biblStruct><biblStruct><analytic><author><name sortKey="Kasal, J" uniqKey="Kasal J">J Kasal</name></author><author><name sortKey="Jovanovic, Z" uniqKey="Jovanovic Z">Z Jovanovic</name></author><author><name sortKey="Clermont, G" uniqKey="Clermont G">G Clermont</name></author><author><name sortKey="Weissfeld, La" uniqKey="Weissfeld L">LA Weissfeld</name></author><author><name sortKey="Kaplan, V" uniqKey="Kaplan V">V Kaplan</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Bernard, Gr" uniqKey="Bernard G">GR Bernard</name></author><author><name sortKey="Vincent, Jl" uniqKey="Vincent J">JL Vincent</name></author><author><name sortKey="Laterre, Pf" uniqKey="Laterre P">PF Laterre</name></author><author><name sortKey="Larosa, Sp" uniqKey="Larosa S">SP LaRosa</name></author><author><name sortKey="Dhainaut, Jf" uniqKey="Dhainaut J">JF Dhainaut</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Panacek, Ea" uniqKey="Panacek E">EA Panacek</name></author><author><name sortKey="Marshall, Jc" uniqKey="Marshall J">JC Marshall</name></author><author><name sortKey="Albertson, Te" uniqKey="Albertson T">TE Albertson</name></author><author><name sortKey="Johnson, Dh" uniqKey="Johnson D">DH Johnson</name></author><author><name sortKey="Johnson, S" uniqKey="Johnson S">S Johnson</name></author></analytic></biblStruct></listBibl></div1></back></TEI><affiliations><list><country><li>États-Unis</li></country><region><li>Pennsylvanie</li></region><settlement><li>Pittsburgh</li></settlement><orgName><li>Université Carnegie-Mellon</li><li>Université de Pittsburgh</li></orgName></list><tree><country name="États-Unis"><region name="Pennsylvanie"><name sortKey="Wu, Chuang" sort="Wu, Chuang" uniqKey="Wu C" first="Chuang" last="Wu">Chuang Wu</name></region><name sortKey="Clermont, Gilles" sort="Clermont, Gilles" uniqKey="Clermont G" first="Gilles" last="Clermont">Gilles Clermont</name><name sortKey="Rosenfeld, Roni" sort="Rosenfeld, Roni" uniqKey="Rosenfeld R" first="Roni" last="Rosenfeld">Roni Rosenfeld</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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