Developing a system for advanced monitoring and intelligent drug administration in critical care units using ontologies

Selected paper of the 16th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems, 2012 September 10-12, San Sebastian, Spain[Abstract] When a patient enters an intensive care unit (ICU), either after surgery or due to a serious clinical condition, his vital signs are continually changing, forcing the medical experts to make rapid and complex decisions, which frequently imply modifications on the dosage of drugs being administered. Life of patients at critical units depends largely on the wisdom of such decisions. However, the human factor is sometimes a source of mistakes that lead to incorrect or inaccurate actions. This work presents an expert system based on a domain ontology that acquires the vital parameters from the patient monitor, analyzes them and provides the expert with a recommendation regarding the treatment that should be administered. If the expert agrees, the system modifies the drug infusion rates being supplied at the infusion pumps in order to improve the patient's physiological status. The system is being developed at the IMEDIR Center (A Coruña, Spain) and it is being tested at the cardiac intensive care unit (CICU) of the Meixoeiro Hospital (Vigo, Spain), which is a specific type of ICU exclusively aimed to treat patients who have underwent heart surgery or that are affected by a serious coronary disorder.Instituto de Salud Carlos III; FIS-PI10/02180Programa Iberoamericano de Ciencia y Tecnología para el Desarrollo; ref. 209RT0366Galicia. Consellería de Cultura, Educación e Ordenación Universitaria; CN2012/217Galicia. Consellería de Cultura, Educación e Ordenación Universitaria; CN2011/034Galcia. Consellería de Cultura, Educación e Ordenación Universitaria; CN2012/21

Finding Knowledge from Indonesian Traditional Medicine using Semantic Web Rule Language

One of the natural resources in Indonesia is a lot of plants which can be used in healing diseases. Thosekinds of plants can be used in “Jamu”. Jamu is a name given to traditional medicine in Indonesia. Usually Jamu is composed from several plants as ingredients. Particularly, some parts of the plant like the leaves, roots, or branches have different purpose in Jamu. Nowadays the knowledge about Jamu can be known by building Ontology. Ontology can be built and developed to enrich the content. Knowledge in Ontology is built by several rules using Semantic Web Rule Language (SWRL).Knowledge gained from SWRL is easily searchable so that users can double check the results obtained

Defining architectures for recommended systems for medical treatment. A Systematic Literature Review

This paper presents a Systematic Literature Review(SLR) related to recommender system for medical treatment, aswell as analyze main elements that may provide flexible, accurate,and comprehensive recommendations. To do so, a SLR researchmethodology obey. As a result, 12 intelligent recommendersystems related to prescribing medication were classed dependingto specific criteria. We assessed and analyze these medicinerecommender systems and enumerate the challenges. After studyingselected papers, our study concentrated on two researchquestions concerning the availability of medicine recommendersystems for physicians and the features these systems should have.Further research is encouraged in order to build an intelligentrecommender system based on the features analyzed in this work

RECOMED: A Comprehensive Pharmaceutical Recommendation System

A comprehensive pharmaceutical recommendation system was designed based on the patients and drugs features extracted from Drugs.com and Druglib.com. First, data from these databases were combined, and a dataset of patients and drug information was built. Secondly, the patients and drugs were clustered, and then the recommendation was performed using different ratings provided by patients, and importantly by the knowledge obtained from patients and drug specifications, and considering drug interactions. To the best of our knowledge, we are the first group to consider patients conditions and history in the proposed approach for selecting a specific medicine appropriate for that particular user. Our approach applies artificial intelligence (AI) models for the implementation. Sentiment analysis using natural language processing approaches is employed in pre-processing along with neural network-based methods and recommender system algorithms for modeling the system. In our work, patients conditions and drugs features are used for making two models based on matrix factorization. Then we used drug interaction to filter drugs with severe or mild interactions with other drugs. We developed a deep learning model for recommending drugs by using data from 2304 patients as a training set, and then we used data from 660 patients as our validation set. After that, we used knowledge from critical information about drugs and combined the outcome of the model into a knowledge-based system with the rules obtained from constraints on taking medicine.Comment: 39 pages, 14 figures, 13 table

Using conceptual graphs for clinical guidelines representation and knowledge visualization

The intrinsic complexity of the medical domain requires the building of some tools to assist the clinician and improve the patient’s health care. Clinical practice guidelines and protocols (CGPs) are documents with the aim of guiding decisions and criteria in specific areas of healthcare and they have been represented using several languages, but these are difficult to understand without a formal background. This paper uses conceptual graph formalism to represent CGPs. The originality here is the use of a graph-based approach in which reasoning is based on graph-theory operations to support sound logical reasoning in a visual manner. It allows users to have a maximal understanding and control over each step of the knowledge reasoning process in the CGPs exploitation. The application example concentrates on a protocol for the management of adult patients with hyperosmolar hyperglycemic state in the Intensive Care Unit

Ontology-based clinical decision support system applied on diabetes

Master's thesis Information- and communication technology IKT590 - University of Agder 2017Medical diagnosis is a multi-step process which is complex as it requires the consideration of many factors. Additionally, the accuracy of diagnosis varies depending on the skill and knowledge a physician has in the medical field. Using ICT solution, the physicians can be assisted so that they can make an accurate decision. Many applications have been developed to enhance physician performance and improve the patient outcome, however, the quality of these applications varies depending on knowledge representation methodology and reasoning approach adopted. Nowadays, ontology is being used in many clinical decision support as it formally represents the concepts and relationships of terms associated with medical domains which in turn improves the information processing, retrieval, and decision support. However, some of these applications do not explain their reasoning process; their knowledge base may not be built using the standard medical ontologies and they build ontology but fail to develop an application that a physician can use. The main objective of this thesis was to investigate how an ontology and its generic tools can be used to overcome the above challenges; how diagnostic can be formally represented and to implement a clinical decision support system that uses that ontology and diagnostic criteria to assist physician when making a diagnosis of diabetes mellitus. The ontology extends DDO and has been designed in protégé 5.0.0 OWL-DL. 19 rules were created based on diabetes diagnostic criteria by using jena rule syntax and forward chaining inference. Furthermore, the system uses the jena rule engine to reason with patient data from ontology against the rules defined in a rule file to generate a patient diagnosis, recommendation and decision explanation based on the patient vital signs and blood glucose test result. As result, 4 patient’s case was successful diagnosed; recommendation and decision explanation produced by the system undoubtedly matched the patient diagnosis. Overall system result is the same as the one expected by the physician, thus, makes it an expert system. The ontology supports the interoperability between CDSS and healthcare system and can be used for the management of the patient. Keywords: Clinical decision support system, diagnostic criteria, semantic web, ontology, diagnosi

Learning Analytics and Recommender Systems toward Remote Experimentation

This paper presents a process based on learning analytics and recom- mender systems to provide suggestions to students about remote laboratories ac- tivities in order to scaffold their performance. For this purpose, the records of remote experiments from the VISIR project were analyzed taking into account one of its installations. Each record is composed of requests containing the as- sembled circuits and the configurations of the measuring equipment, as well as the response provided by the measurement server that evaluates whether a par- ticular request can be performed or not. With the log analysis, it was possible to obtain information in order to determine some initial statistics and provide clues about the student’s behavior during the experiments. Using the concept of rec- ommendation, a service is proposed through request analysis and returns to the students more precise information about possible mistakes in the assembly of circuits or configurations. The process as a whole proves consistent in what re- gards its ability to provide suggestions to the students as they conduct the exper- iments. Furthermore, with the log, relevant information can be offered to teach- ers, thus assisting them in developing strategies to positively impact student’s learning.info:eu-repo/semantics/publishedVersio