[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-665377-105":59,"doc-detail-665377-en":130},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":123,"head_meta":125,"extra_data":127,"updated_unix":129},105,"en","influence-diagrams-for-medical-decision-problems-some-limitations-and-proposed-solutions","Influence diagrams for medical decision problems - Some limitations and proposed solutions","","The paper addresses difficulties encountered when solving two medical decision problems using influence diagrams, focusing on representating and operating with decomposable utility functions, calibrating human experts and explaining the reasoning conveyed by influence diagrams, and handling partially ordered decisions. It presents the specific limitations observed during implementation and the solutions adopted, supported by extensions to Elvira to better model medical uncertainty and decision structure.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/influence-diagrams-for-medical-decision-problems-some-limitations-and-proposed-solutions/665377/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/influence-diagrams-for-medical-decision-problems-some-limitations-and-proposed-solutions/665377.png","ImageObject",300,407,{"name":92,"@type":93},"dabgoudw","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-30","2026-10-29",true,{"@type":102,"interactionType":103,"userInteractionCount":14},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What kinds of difficulties does the paper report when building medical influence diagrams?","Question",{"text":112,"@type":113},"It highlights challenges in representing and computing with decomposable utility functions, calibrating human experts and explaining the reasoning of the diagrams, and dealing with partially ordered decisions.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"Why were decomposable (super-value) utility nodes introduced?",{"text":117,"@type":113},"They reduce the burden on experts by avoiding assessment of many utility parameters, improve computational efficiency by handling larger utility structures, and help produce policies that depend on fewer variables.",{"name":119,"@type":110,"acceptedAnswer":120},"How does the Elvira system support explanation for influence diagrams?",{"text":121,"@type":113},"It extends explanation facilities from Bayesian networks to influence diagrams, showing resulting policies and providing numerical and graphical information in each node, such as probabilities and expected utilities.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},665377,1793301842,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":14,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":14,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":129,"read_time":24},3985747860484,"https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c","Influence diagrams for medical decision problems: Some limitations and proposed solutions¤  \nManuel Luque and Francisco Javier Diez Dept. Inteligencia Artificial. UNED 28040 Madrid. Spain [mluque@bec.uned.es](mluque@bec.uned.es), [fjdiez@dia.uned.es](fjdiez@dia.uned.es)  \nCarlos Disdier  \nPulmonary Section San Pedro Alcantara Hospital 1004 Caceres. Spain [cdisdier@separ.es](cdisdier@separ.es)  \nAbstract  \nWhen trying to solve two medical decision prob  \nlems we have encountered several difficulties: how to represent and operate with decomposable utility functions, how to calibrate our human experts and explain them the “reasoning” of our influence diagrams, and how to deal with partially ordered decisions. This paper describes these difficulties and the solutions we have adopted.  \n1 Introduction  \nOne of the medical problems we are currently working on is the mediastinal staging of non-small cell lung cancer. There are several tests available, such as computed tomography scan (CT scan), transbronchial needle aspiration (TBNA), mediastinoscopy (MED) and others, which have different characteristics of sensitivity, specificity, morbidity and mortality. The other problem on which we are working is the management of mild head injury.  \nInfluence diagrams are a framework which serves as an effective modeling tool for decision problems. An influence diagram (ID) [3], consists of a directed acyclic graph having three kinds of nodes: decision (graphically represented by squares or rectangles), chance (circles or ovals), and utilities (diamonds) . Each decision node represents to actions under the direct control of the decision maker. Each chance node represents a random variable. In medical IDs, utility nodes represent medical outcomes and costs (morbidity, mortality, economic cost...) .  \nThe quantitative information that defines an ID is given by assigning to each chance node Xi a probability distribution p(Xi jpa(Xi )) ; where pa(Xi ) represents the parents of the node Xi in the graph, and assigning to the utility node U a function Ã(pa(U )) . The objective of the evaluation of an influence diagram is obtaining a policy for each decision, which prescribes a set of optimal actions for the decision maker. The policy for each decision is a function of the variables that are known when the decision is made.  \n¤ This research has been supported by the Spanish Ministry of Science and Technology under grant TIC-2001-2973-C05-04 .  \n2 Limitations of influence diagrams for medical decision problems  \nThis section describes the difficulties we have found when building those IDs and how we have extended Elvira to cope with them. Elvira 1 is a Java tool to construct probabilistic decision support systems. Elvira works with Bayesian networks (BN) and influence diagrams and it can operate with discrete and continuous variables. It has an easy Graphical User Interface (GUI) for constructing BNsand IDs.  \n2.1 Decomposable utilities  \nAn essential component of an influence diagram is the utility function. In its original formulation [3], each ID had only one utility node, which entails several disadvantages. First, the human expert has to assess more parameters. Second, the bigger the utility function the more time and memory space is required for the computational evaluation of the ID. Third, policies tend to include more variables than when using decomposable utility functions.  \nIn order to overcome these shortcomings, Tatman and Shachter [5] introduced a new kind of utility node, called super-value nodes, which represent a function of their parents’ utilities, and proposed an algorithm for evaluating such generalized IDs.  \nThe first limitation we encountered when building our medical IDs is that current software tools do not admit super-value nodes. At most, they accept several utility nodes under the assumption that the global utility is the sum of all of them. For this reason, we extended Elvira’s GUI and format so that it could cope with super-value nodes","cbCaisqgg0gRP9dp","https://ap.wps.com/l/cbCaisqgg0gRP9dp","pdf",54920,"English","# Introduction\n## Influence diagrams for medical decision problems\n# Limitations of influence diagrams for medical decision problems\n## Decomposable utilities\n## Explanation in influence diagrams","[{\"question\":\"What kinds of difficulties does the paper report when building medical influence diagrams?\",\"answer\":\"It highlights challenges in representing and computing with decomposable utility functions, calibrating human experts and explaining the reasoning of the diagrams, and dealing with partially ordered decisions.\"},{\"question\":\"Why were decomposable (super-value) utility nodes introduced?\",\"answer\":\"They reduce the burden on experts by avoiding assessment of many utility parameters, improve computational efficiency by handling larger utility structures, and help produce policies that depend on fewer variables.\"},{\"question\":\"How does the Elvira system support explanation for influence diagrams?\",\"answer\":\"It extends explanation facilities from Bayesian networks to influence diagrams, showing resulting policies and providing numerical and graphical information in each node, such as probabilities and expected utilities.\"}]","Influence diagrams for medical decision problems - Some limitations and proposed solutions | PDF"]