HYPOTHESES MANAGEMENT FOR DISORDER DIAGNOSIS IN A HIERARCHICAL FRAMEWORK

Author(s):  
JAVIER DIAZ ◽  
SANDRA SANDRI ◽  
MARIA RIFQI ◽  
BERNADETTE BOUCHON-MEUNIER

We propose the use of a knowledge based framework for diagnosis in which the knowledge base consists of particular instances of general hierarchical disorder models. We study how to select which manifestation (symptom, malfunction) to query in order to reduce a set of competing diagnosis hypotheses (disorders), none of them completely satisfying, considering only the observed manifestations. We propose to use general information about the order in which competing disorder models should be probed first to guide us on the task of selecting which particular disorder instances to try to confirm first. We propose to then order which manifestation instances to probe, the presence or absence of which will help us to either confirm or eliminate that hypothesis, according to the principle that "(manifestation) instances that share some characteristics with the instance of manifestation that generated the whole process, but which completely disagree in relation to other characteristics" should be probed first.

2021 ◽  
Author(s):  
Marciane Mueller ◽  
Rejane Frozza ◽  
Liane Mählmann Kipper ◽  
Ana Carolina Kessler

BACKGROUND This article presents the modeling and development of a Knowledge Based System, supported by the use of a virtual conversational agent called Dóris. Using natural language processing resources, Dóris collects the clinical data of patients in care in the context of urgency and hospital emergency. OBJECTIVE The main objective is to validate the use of virtual conversational agents to properly and accurately collect the data necessary to perform the evaluation flowcharts used to classify the degree of urgency of patients and determine the priority for medical care. METHODS The agent's knowledge base was modeled using the rules provided for in the evaluation flowcharts comprised by the Manchester Triage System. It also allows the establishment of a simple, objective and complete communication, through dialogues to assess signs and symptoms that obey the criteria established by a standardized, validated and internationally recognized system. RESULTS Thus, in addition to verifying the applicability of Artificial Intelligence techniques in a complex domain of health care, a tool is presented that helps not only in the perspective of improving organizational processes, but also in improving human relationships, bringing professionals and patients closer. The system's knowledge base was modeled on the IBM Watson platform. CONCLUSIONS The results obtained from simulations carried out by the human specialist allowed us to verify that a knowledge-based system supported by a virtual conversational agent is feasible for the domain of risk classification and priority determination of medical care for patients in the context of emergency care and hospital emergency.


2016 ◽  
Vol 28 (2) ◽  
pp. 241-251 ◽  
Author(s):  
Luciane Lena Pessanha Monteiro ◽  
Mark Douglas de Azevedo Jacyntho

The study addresses the use of the Semantic Web and Linked Data principles proposed by the World Wide Web Consortium for the development of Web application for semantic management of scanned documents. The main goal is to record scanned documents describing them in a way the machine is able to understand and process them, filtering content and assisting us in searching for such documents when a decision-making process is in course. To this end, machine-understandable metadata, created through the use of reference Linked Data ontologies, are associated to documents, creating a knowledge base. To further enrich the process, (semi)automatic mashup of these metadata with data from the new Web of Linked Data is carried out, considerably increasing the scope of the knowledge base and enabling to extract new data related to the content of stored documents from the Web and combine them, without the user making any effort or perceiving the complexity of the whole process.


Author(s):  
Sarah Bouraga ◽  
Ivan Jureta ◽  
Stéphane Faulkner ◽  
Caroline Herssens

Knowledge-Base Recommendation (or Recommender) Systems (KBRS) provide the user with advice about a decision to make or an action to take. KBRS rely on knowledge provided by human experts, encoded in the system and applied to input data, in order to generate recommendations. This survey overviews the main ideas characterizing a KBRS. Using a classification framework, the survey overviews KBRS components, user problems for which recommendations are given, knowledge content of the system, and the degree of automation in producing recommendations.


2014 ◽  
Vol 5 (2) ◽  
Author(s):  
Kaitlin Bova ◽  
Sara Bova ◽  
Kevin Hill ◽  
Mark Dixon ◽  
Diana Ivankovich ◽  
...  

Objectives: To evaluate a weblog (blog)-based course introducing pharmacogenetics (PGt) and personalized medicine (PM) relative to freshmen pharmacy students' knowledge base. Methods: Incoming freshmen pharmacy students were invited by email to enroll in a one semester-hour, elective, on-line blog-based course entitled "Personal Genome Evaluation". The course was offered during the students' first semester in college. A topic list related to PGt and PM was developed by a group of faculty with topics being presented via the blog once or twice weekly through week 14 of the 15 week semester. A pre-course and post-course survey was sent to the students to compare their knowledge base relative to general information, drug response related to PGt, and PM. Results: Fifty-one freshmen pharmacy students enrolled in the course and completed the pre-course survey and 49 of the 51 students completed the post-course survey. There was an increase in the students' general, PGt and PM knowledge base as evidenced by a statistically significant higher number of correct responses for 17 of 21 questions on the post-course survey as compared to the pre-course survey. Notably, following the course, students had an increased knowledge base relative to "genetic privacy", drug dosing based on metabolizer phenotype, and the breadth of PM, among other specific points. Conclusions: The study indicated that introducing PGt and PM via a blog format was feasible, increasing the students' knowledge of these emerging areas. The blog format is easily transferable and can be adopted by colleges/schools to introduce PGt and PM.   Type: Case Study


Author(s):  
Kumudu Jayawardhana

The burgeoning literature postulates that a firm’s degree of openness for external parties in building its knowledge base undoubtedly enables it gaining competitive advantage though a little attention has been devoted to investigating the phenomena from small and medium enterprise (SME) perspective. Accordingly, this study investigates how open innovation orientation leads nurturing greater innovation and acquiring greater sustainable goals and specifically, how entrepreneurial orientation and resource bricolage facilitate the whole process. Drawing upon a sample of 442 SMEs, the study followed a quantitative approach to investigate the focal research question. The results reveal that open innovation orientation of SMEs significantly influences on nurturing greater innovation and attaining sustainable goals in long-run while the entrepreneurial orientation drives the whole process. The study also finds that the resource bricolage plays a significant role in converting SMEs more open innovation oriented and fostering greater innovation. By doing so, this study provides noteworthy theoretical and managerial insights.


1990 ◽  
Vol 80 (6B) ◽  
pp. 1833-1851 ◽  
Author(s):  
Thomas C. Bache ◽  
Steven R. Bratt ◽  
James Wang ◽  
Robert M. Fung ◽  
Cris Kobryn ◽  
...  

Abstract The Intelligent Monitoring System (IMS) is a computer system for processing data from seismic arrays and simpler stations to detect, locate, and identify seismic events. The first operational version processes data from two high-frequency arrays (NORESS and ARCESS) in Norway. The IMS computers and functions are distributed between the NORSAR Data Analysis Center (NDAC) near Oslo and the Center for Seismic Studies (Center) in Arlington, Virginia. The IMS modules at NDAC automatically retrieve data from a disk buffer, detect signals, compute signal attributes (amplitude, slowness, azimuth, polarization, etc.), and store them in a commercial relational database management system (DBMS). IMS makes scheduled (e.g., hourly) transfers of the data to a separate DBMS at the Center. Arrival of new data automatically initiates a “knowledge-based system (KBS)” that interprets these data to locate and identify (earthquake, mine blast, etc.) seismic events. This KBS uses general and area-specific seismological knowledge represented in rules and procedures. For each event, unprocessed data segments (e.g., 7 min for regional events) are retrieved from NDAC for subsequent display and analyst review. The interactive analysis modules include integrated waveform and map display/manipulation tools for efficient analyst validation or correction of the solutions produced by the automated system. Another KBS compares the analyst and automatic solutions to mark overruled elements of the knowledge base. Performance analysis statistics guide subsequent changes to the knowledge base so it improves with experience. The IMS is implemented on networked Sun workstations, with a 56 kbps satellite link bridging the NDAC and Center computer networks. The software architecture is modular and distributed, with processes communicating by messages and sharing data via the DBMS. The IMS processing requirements are easily met with major processes (i.e., signal processing, KBS, and DBMS) on separate Sun 4/2xx workstations. This architecture facilitates expansion in functionality and number of stations. The first version was operated continuously for 8 weeks in late-1989. The Center functions were then transferred to NDAC for subsequent operation. Later versions will be distributed among NDAC, Scripps/IGPP (San Diego), and the Center to process data from many stations and arrays. The IMS design is ambitious in its integration of many new computer technologies, but the operational performance of the first version demonstrates its validity. Thus, IMS provides a new generation of automated seismic event monitoring capability.


Author(s):  
Samir Rohatgi ◽  
James H. Oliver ◽  
Stuart S. Chen

Abstract This paper describes the development of OPGEN (Opportunity Generator), a computer based system to help identify areas where a knowledge based system (KBS) might be beneficial, and to evaluate whether a suitable system could be developed in that area. The core of the system is a knowledge base used to carry out the identification and evaluation functions. Ancillary functions serve to introduce and demonstrate KBS technology to enhance the overall effectiveness of the system. All aspects of the development, from knowledge acquisition through to testing are presented in this paper.


Author(s):  
Ming Dong ◽  
Jianzhong Cha ◽  
Mingcheng E

Abstract In this paper, we realize knowledge-based discrete event simulation model’s representation, reasoning and implementation by means of object-oriented(OO) frame language. Firstly, a classes library of simulation models is built by using the OO frame language. And then, behaviours of simulation models can be generated by inference engines reasoning about knowledge base. Lastly, activity cycle diagrams can be used to construct simulation network logic models by connecting the components classes of simulation models. This kind of knowledge-based simulation models can effectively solve the modeling problems of complex and ill-structure systems.


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