Automated Medical Diagnosis with Fuzzy Stochastic Models: Monitoring Chronic Diseases
Identifieur interne : 001067 ( Hal/Corpus ); précédent : 001066; suivant : 001068Automated Medical Diagnosis with Fuzzy Stochastic Models: Monitoring Chronic Diseases
Auteurs : Laurent Jeanpierre ; François CharpilletSource :
- Acta Biotheoretica [ 0001-5342 ] ; 2004.
Abstract
As the world population ages, the patients per physician ratio keeps on increasing. This is even more important in the domain of chronic pathologies where people usually are monitored for years and need regular consultations. To address this problem, we propose an automated system to monitor a patient population, detecting anomalies in instantaneous data and in their temporal evolution, so that it could alert physicians. By handling autonomously the population of well-being patients and by drawing the physicians' attention to the patients-at-risk, the system allows physicians for spending comparatively more time with patients who need their services. In such a system, the interaction between the patients, the diagnosis module, and the physicians is very important. We based this system on a combination of stochastic models, fuzzy filters, and strong medical semantics. We particularly focused on a particular tele-medicine application: the Diatelic Project. Its objective is to monitor chronic kidney-insufficient patients and to detect hydration troubles. During two years, physicians from the ALTIR have conducted a prospective randomized study of the system. This experiment clearly shows that the proposed system is really beneficent to the patients' health.
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<front><div type="abstract" xml:lang="en">As the world population ages, the patients per physician ratio keeps on increasing. This is even more important in the domain of chronic pathologies where people usually are monitored for years and need regular consultations. To address this problem, we propose an automated system to monitor a patient population, detecting anomalies in instantaneous data and in their temporal evolution, so that it could alert physicians. By handling autonomously the population of well-being patients and by drawing the physicians' attention to the patients-at-risk, the system allows physicians for spending comparatively more time with patients who need their services. In such a system, the interaction between the patients, the diagnosis module, and the physicians is very important. We based this system on a combination of stochastic models, fuzzy filters, and strong medical semantics. We particularly focused on a particular tele-medicine application: the Diatelic Project. Its objective is to monitor chronic kidney-insufficient patients and to detect hydration troubles. During two years, physicians from the ALTIR have conducted a prospective randomized study of the system. This experiment clearly shows that the proposed system is really beneficent to the patients' health.</div>
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<idno type="stamp" n="LORIA-CSAI" p="LORIA">Systèmes complexes et intelligence artificielle</idno>
<idno type="stamp" n="LORIA">LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications</idno>
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<idno type="stamp" n="UNIV-LORRAINE">Université de Lorraine</idno>
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<notesStmt><note type="commentary">Article dans revue scientifique avec comité de lecture. internationale.</note>
<note type="audience" n="2">International</note>
<note type="popular" n="0">No</note>
<note type="peer" n="1">Yes</note>
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<sourceDesc><biblStruct><analytic><title xml:lang="en">Automated Medical Diagnosis with Fuzzy Stochastic Models: Monitoring Chronic Diseases</title>
<author role="aut"><persName><forename type="first">Laurent</forename>
<surname>Jeanpierre</surname>
</persName>
<email>laurent.jeanpierre1@free.fr</email>
<idno type="halAuthorId">130763</idno>
<orgName ref="#struct-364475"></orgName>
<affiliation ref="#struct-2355"></affiliation>
</author>
<author role="aut"><persName><forename type="first">François</forename>
<surname>Charpillet</surname>
</persName>
<email>francois.charpillet@loria.fr</email>
<idno type="idHal">francois-charpillet</idno>
<idno type="halAuthorId">129571</idno>
<orgName ref="#struct-300009"></orgName>
<affiliation ref="#struct-2355"></affiliation>
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<monogr><idno type="localRef">A04-R-209 || jeanpierre04a</idno>
<idno type="halJournalId" status="VALID">9555</idno>
<idno type="issn">0001-5342</idno>
<idno type="eissn">1572-8358</idno>
<title level="j">Acta Biotheoretica</title>
<imprint><publisher>Springer Verlag</publisher>
<biblScope unit="volume">52</biblScope>
<biblScope unit="issue">4</biblScope>
<biblScope unit="pp">291-311</biblScope>
<date type="datePub">2004</date>
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</monogr>
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<profileDesc><langUsage><language ident="en">English</language>
</langUsage>
<textClass><keywords scheme="author"><term xml:lang="it">modèles de processus stochastiques</term>
<term xml:lang="it">medical monitoring</term>
<term xml:lang="it">stochastic processes</term>
<term xml:lang="it">diagnosis</term>
<term xml:lang="it">fuzzy filters</term>
<term xml:lang="it">diagnostic</term>
<term xml:lang="it">filtrage flou</term>
<term xml:lang="it">suivi médical</term>
</keywords>
<classCode scheme="halDomain" n="info.info-oh">Computer Science [cs]/Other [cs.OH]</classCode>
<classCode scheme="halTypology" n="ART">Journal articles</classCode>
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<abstract xml:lang="en">As the world population ages, the patients per physician ratio keeps on increasing. This is even more important in the domain of chronic pathologies where people usually are monitored for years and need regular consultations. To address this problem, we propose an automated system to monitor a patient population, detecting anomalies in instantaneous data and in their temporal evolution, so that it could alert physicians. By handling autonomously the population of well-being patients and by drawing the physicians' attention to the patients-at-risk, the system allows physicians for spending comparatively more time with patients who need their services. In such a system, the interaction between the patients, the diagnosis module, and the physicians is very important. We based this system on a combination of stochastic models, fuzzy filters, and strong medical semantics. We particularly focused on a particular tele-medicine application: the Diatelic Project. Its objective is to monitor chronic kidney-insufficient patients and to detect hydration troubles. During two years, physicians from the ALTIR have conducted a prospective randomized study of the system. This experiment clearly shows that the proposed system is really beneficent to the patients' health.</abstract>
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