Knowledge-based incremental induction of clinical algorithms

The current approaches for the induction of medical procedural knowledge suffer from several drawbacks: the structures produced may not be explicit medical structures, they are only based on statistical measures that do not necessarily respect medical criteria which can be essential to guarantee med...

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Main Author: López Vallverdú, Joan Albert
Other Authors: Riaño Ramos, David
Format: Doctoral Thesis
Language:English
Published: Universitat Rovira i Virgili 2012
Subjects:
004
61
Online Access:http://hdl.handle.net/10803/97210
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spelling ndltd-TDX_URV-oai-www.tdx.cat-10803-972102013-07-12T06:17:09ZKnowledge-based incremental induction of clinical algorithmsLópez Vallverdú, Joan AlbertBackground KnowledgeClinical algorithmsKnowledge inductionIncrementality00461The current approaches for the induction of medical procedural knowledge suffer from several drawbacks: the structures produced may not be explicit medical structures, they are only based on statistical measures that do not necessarily respect medical criteria which can be essential to guarantee medical correct structures, or they are not prepared to deal with the incremental arrival of new data. In this thesis we propose a methodology to automatically induce medically correct clinical algorithms (CAs) from hospital databases. These CAs are represented according to the SDA knowledge model. The methodology considers relevant background knowledge and it is able to work in an incremental way. The methodology has been tested in the domains of hypertension, diabetes mellitus and the comborbidity of both diseases. As a result, we propose a repository of background knowledge for these pathologies and provide the SDA diagrams obtained. Later analyses show that the results are medically correct and comprehensible when validated with health care professionals.Universitat Rovira i VirgiliRiaño Ramos, DavidUniversitat Rovira i Virgili. Departament d'Enginyeria Informàtica i Matemàtiques2012-12-14info:eu-repo/semantics/doctoralThesisinfo:eu-repo/semantics/publishedVersion205 p.application/pdfhttp://hdl.handle.net/10803/97210TDX (Tesis Doctorals en Xarxa)enginfo:eu-repo/semantics/openAccessADVERTIMENT. L'accés als continguts d'aquesta tesi doctoral i la seva utilització ha de respectar els drets de la persona autora. Pot ser utilitzada per a consulta o estudi personal, així com en activitats o materials d'investigació i docència en els termes establerts a l'art. 32 del Text Refós de la Llei de Propietat Intel·lectual (RDL 1/1996). Per altres utilitzacions es requereix l'autorització prèvia i expressa de la persona autora. En qualsevol cas, en la utilització dels seus continguts caldrà indicar de forma clara el nom i cognoms de la persona autora i el títol de la tesi doctoral. No s'autoritza la seva reproducció o altres formes d'explotació efectuades amb finalitats de lucre ni la seva comunicació pública des d'un lloc aliè al servei TDX. Tampoc s'autoritza la presentació del seu contingut en una finestra o marc aliè a TDX (framing). Aquesta reserva de drets afecta tant als continguts de la tesi com als seus resums i índexs.
collection NDLTD
language English
format Doctoral Thesis
sources NDLTD
topic Background Knowledge
Clinical algorithms
Knowledge induction
Incrementality
004
61
spellingShingle Background Knowledge
Clinical algorithms
Knowledge induction
Incrementality
004
61
López Vallverdú, Joan Albert
Knowledge-based incremental induction of clinical algorithms
description The current approaches for the induction of medical procedural knowledge suffer from several drawbacks: the structures produced may not be explicit medical structures, they are only based on statistical measures that do not necessarily respect medical criteria which can be essential to guarantee medical correct structures, or they are not prepared to deal with the incremental arrival of new data. In this thesis we propose a methodology to automatically induce medically correct clinical algorithms (CAs) from hospital databases. These CAs are represented according to the SDA knowledge model. The methodology considers relevant background knowledge and it is able to work in an incremental way. The methodology has been tested in the domains of hypertension, diabetes mellitus and the comborbidity of both diseases. As a result, we propose a repository of background knowledge for these pathologies and provide the SDA diagrams obtained. Later analyses show that the results are medically correct and comprehensible when validated with health care professionals.
author2 Riaño Ramos, David
author_facet Riaño Ramos, David
López Vallverdú, Joan Albert
author López Vallverdú, Joan Albert
author_sort López Vallverdú, Joan Albert
title Knowledge-based incremental induction of clinical algorithms
title_short Knowledge-based incremental induction of clinical algorithms
title_full Knowledge-based incremental induction of clinical algorithms
title_fullStr Knowledge-based incremental induction of clinical algorithms
title_full_unstemmed Knowledge-based incremental induction of clinical algorithms
title_sort knowledge-based incremental induction of clinical algorithms
publisher Universitat Rovira i Virgili
publishDate 2012
url http://hdl.handle.net/10803/97210
work_keys_str_mv AT lopezvallverdujoanalbert knowledgebasedincrementalinductionofclinicalalgorithms
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