Masaki Kurematsu

Virtual doctor system (VDS): medical decision reasoning based on physical and mental ontologies

Human computer Interaction based on emotional modelling and physical views, collectively; has been investigated and reported in this paper. Two types of ontology have been presented to formalize a patient state: mental ontology reflecting the patient mental behavior due to certain disorder and physical ontology reflecting the observed physical collected exhibited consequences of such disorder. These two types of ontology have been mapped and aligned using OWL-S and SWRL for reasoning purposes. We have constructed an integrated computerized model which reflects a human diagnostician as computer model and through it, an integrated interaction between that model and the real human user (patient) is utilized for 1st stage diagnosis purposes. The diagnostician knowledge has been utilized through UMLS for testing, and the integrated mapping of the two views been represented through OWLS framework. The reasoning instantiation is done using SWRL and RACER integrated on Protege 4.

Fuzzy reasoning for medical diagnosis-based aggregation on different ontologies

The paper discusses reasoning application for decision making in medical diagnosis. This is to reason on medical concepts that are viewed on two type ontologies; namely physical and mental. We highlighted in this position paper issues on fuzzy reasoning by aggregating two types of ontologies that are used to formalize a patient state: mental ontology reflecting the patient mental behavior due to certain disorder and physical ontology reflecting the observed physical behavior exhibited through disorder. Similarity matching is used to find the similarity between fuzzy set reflected to mental fuzzy ontology, and physical fuzzy ontology. The alignment is projected on medical ontology to rank attributes for decision making. We apply aggregate function for ranking attributes related to physical object. In the same time, we apply harmonic power average aggregate function fuzzy for ranking attributes related to mental objects. The alignment of these two aggregate function produce weighted ranking order fuzzy set for medical decision making for diagnosis. The paper highlights these issues as new challenges extending intelligence reasoning of VDS.

Multiviews ontologies alignment for medical based reasoning ontology based reasoning for VDS

Two views representation for patient diagnosis is presented, to reason to examine medical status for patients. Emotional modeling and physical views, collectively; have been investigated and reported in this paper. These two types of ontology have been presented to formalize a patient state: mental ontology reflecting the patient mental behavior due to certain disorder and physical ontology reflecting the observed physical behavior exhibited through disorder. These two types of ontologies have been mapped and aligned for reasoning using a simple Bayesian Network for causal reasoning to define what we call as simple case diagnosis. We have constructed an integrated computerized model which reflects a human diagnostician as computer model and through it; an integrated interaction between that model and the real human user (patient) is utilized for 1st stage diagnosis purposes‥

A framework for integrating a decision tree learning algorithm and cluster analysis

We proposed a modified decision tree learning algorithm to improve this algorithm in this paper. Our proposed approach classifies given data set by a traditional decision tree learning algorithm and cluster analysis and selects whichever is better according to information gain. In order to evaluate our approach, we did an experiment using program-generated data sets. We compared ID3 which is one of well-known decision tree learning algorithm to our approach about the recall ratio in this experiment. Experimental result shows the recall ratio of our approach is similar than the recall ratio of a traditional decision tree learning algorithm. Though we can not show the advantage of our approach according to the experiment, we show it is worth using cluster analysis to make a decision tree. In future, we have to evaluate our approach according to cross-validation method using big and complex data sets in order to say the advantage of our approach. We think our approach is not good for all data set, so we try to find the situation which our approach is better than other approaches according to the experimental results. In addition to, we have to show how to explain a decision tree by our approach to keep the readability of a decision tree.

Virtual Doctor System (VDS) and Ontology Based Reasoning for Medical Diagnosis

VDS is a system built as intelligent thinking support for assisting medical doctor in a hospital to do medical diagnosis based on the avatar of that doctor. The medical knowledge is also collected from the doctor based on his/her experience in diagnosis. The avatar construction is mimicking real doctor. The avatar interacts with patients through their voices, and other sensors to read patient mental state and physical state that are used in aligned manner to assess the patient sickness states through Bayesian network. The physical view is represented as physical ontology. The mental view is represented as mental ontology. These two ontologies aligned on medical knowledge for diagnosis and reasoning based on similarities computation. These two types of ontologies have been mapped and aligned for reasoning using a simple Bayesian Network for causal reasoning to find related query decision case based diagnosis collected from expert doctors. The system is implemented and tested. We have constructed an integrated computerized model which reflects a human diagnostician and through it; an integrated interaction between that model and the real human user (patient) is utilized for 1 st stage diagnosis purposes recalled as simple cases.

Multiviews ontologies based reasoning for medical diagnosis in VDS

This paper examined issues on reasoning in Virtual Doctor System based on two views representations for patient diagnosis. This is reason on similar medical concepts that are viewed on the presented ontology, to reason on medical status for patients. These two types of ontology have been presented to formalize a patient state: mental ontology reflecting the patient mental behavior due to certain disorder and physical ontology reflecting the observed physical behavior exhibited through disorder. These diagnosis issues are represented through concepts. Patient observations are reflected on these concepts for similarity calculation that produce decision making based query. These two types of ontologies have been mapped and aligned for reasoning using a simple Bayesian Network for causal reasoning to find related query decision case based diagnosis collected from expert doctors‥ We have constructed an integrated computerized model which reflects a human diagnostician as computer model and through it; an integrated interaction between that model and the real human user (patient) is utilized for 1st stage diagnosis purposes.

Virtual Human Interaction based on Emotional Cognition

This paper is presenting progress status of our project named as emotion based reasoning for constructing a virtual emotional interactive human model. It is based on a person model, whose emotional characteristic been extracted from his work and other physiological, observations. A prototype of the system has been constructed for such experiment and based on famous Japanese writer namely Miyazawa Kenji.

Trends in Applied Knowledge-Based Systems and Data Science

The Information Mining Engineering (IME) understands in processes, methodologies, tasks and techniques used to: organize, control and manage the task of finding knowledge patterns in information bases. A relevant task is selecting the data mining algorithms to use, which it is left to the expertise of the information mining engineer, developing it in a non-structured way. In this paper we propose an Information Mining Project Development Process Model (D-MoProPEI) which provides an integrated view in the selection of Information Mining Processes Based on Intelligent Systems (IMPbIS) within the Modeling Phase of the proposed Process Model through a Systematic Deriving Methodology.

Fuzzy reasoning decision making on multiviews fuzzy ontologies alignment

We highlighted in this position paper issues on fuzzy reasoning. The paper discusses reasoning application for decision making in medical diagnosis. This is to reason on medical concepts that are viewed on two type ontologies; namely physical and mental. These two types of ontology have been presented to formalize a patient state: mental ontology reflecting the patient mental behavior due to certain disorder and physical ontology reflecting the observed physical behavior exhibited through disorder. Patient observations are reflected on these concepts for similarity calculation that produce decision making based query. We have investigated multi criteria decision making technique to provide better decision on the attributes collected from patient for medical diagnosis. We apply fuzzy AHP (Analytical Hierarchy Process) for ranking attributes related to physical object. In the same time, we apply bipolar fuzzy decision making logic for ranking attributes related to mental objects. The paper highlights these issues as new challenges extending intelligence reasoning of VDS.

Virtual Doctor System (VDS): Framework on Reasoning Issues: Ontology Based Reasoning for Virtual Doctor System

Human computer Interaction based on emotional modelling and physical views, collectively; has been investigated and reported in this paper. Two types of ontology have been presented to formalize a patient state: mental ontology reflecting the patient mental behavior due to certain disorder and physical ontology reflecting the observed physical behavior exhibited through disorder. These two types of ontology have been mapped and aligned for reasoning using a simple Bayesian Network for causal reasoning to define what we call as simple case diagnosis. We have constructed an integrated computerized model which reflects a human diagnostician as computer model and through it; an integrated interaction between that model and the real human user (patient) is utilized for 1st stage diagnosis purposes.