치과보철 치료에 대한 공유의사결정 지원 시스템의 개발

학위논문 (박사)-- 서울대학교 대학원 : 치의학과, 2013. 2. 김명기.공유의사결정 (Shared decision making)은 의사의 의학적인 근거와 환자의 선호도를 상호참조하여, 치료에 대한 의사결정에 환자를 참여시키려는 접근법이다. 이는 환자중심 진료의 윤리성뿐만 아니라, 의사결정 과정에서의 질적인 향상을 가져온다. 이런 접근법은 비교적 활발히 연구되었으나, 실제로 치과 임상환경에서 사용할 수 있는 도구는 거의 개발되지 않았다. 이에 본 연구는 치과보철 치료에 대한 공유의사결정 지원시스템을 제안하고자 한다. 이 시스템은 치료 방법을 결정하는데 요구되는 임상적인 지식을 온톨로지 기술을 기반으로 추론하여, 공유의사결정의 토대가 되는 임상적인 대안들을 제시한다. 그 대안들 중에서 최종 의사결정을 내리기 위해 환자의 선호도를 중요하게 고려하였고, 우선순위 결정을 위해서 AHP (Analytic hierarchy process) 방법을 이용하였다. 이 시스템은 치과보철 치료에 대한 환자 선호도에 따른 근거중심의 치료대안들을 시각적으로 보여줄 수 있도록 웹 어플리케이션의 형태로 개발하였다. 이는 의사와 환자간의 공유의사결정을 위한 상호작용의 토대가 될 수 있으며, 환자에게 적합한 치료방법을 결정하는데 도움을 줄 수 있을 것이다.Shared decision making (SDM) is an approach in which doctor-patient communication regarding available evidence and patient preferences is facilitated to enable the patient to participate in treatment decisions. SDM affords not only the inclusion of the ethical diversities involved in patient-centered care, but also the quality improvements in decision-making process. Though SDM has been studied extensively, there have been few practical implementations in real clinical environments. In this paper, we propose a shared decision-making system with its focus on dental restorative treatment planning. In our system, restorative treatment alternatives for SDM were generated by employing an ontology that had captured the clinical knowledge required for treatments. We considered patient preferences for treatment as an important support for mutual agreements between the patient and the doctor on healthcare decisions. We constructed a consistent and robust hierarchy of preferences using the analytic hierarchy process (AHP) method, to help determine treatment priorities. On the basis of our proposed system, we developed a Web-based application for the visualization of evidence-based treatment recommendations with preference-based weights. We tested our system using a scenario to illustrate how doctors and patients can make shared decisions. The application is of high value in supporting SDM between doctors and patients, and expedites effective treatments and enhanced patient satisfaction.1. 서론 1 1.1. 연구의 필요성 1 1.2. 연구의 목적 4 1.3. 연구 개발의 과정 5 1.4. 논문의 개요 6 2. 연구 배경 8 2.1. 임상 의사결정 지원 시스템 8 2.2. 공유의사결정 지원 시스템 11 2.2.1. 온톨로지 14 2.2.2. AHP (Analytic Hierarchy Process) 19 3. 연구 방법 24 3.1. 시스템의 설계 24 3.2. 온톨로지 디자인 26 3.2.1. 온톨로지 개발 과정 26 3.2.2. 치과보철 치료분야에서의 온톨로지의 기초 28 3.2.3. 질병-치료-해부학적 개념의 상호적 연결 30 3.2.4. 개념 표현 40 3.2.5. 요약 52 3.3. AHP 적용 과정 56 3.4. 구현 62 4. 연구 결과 65 4.1. 온톨로지의 일관성 분석 65 4.2. 임상 시나리오 67 4.2.1. 임상 시나리오 1 67 4.2.2. 임상 시나리오 2 71 5. 고찰 75 6. 결론 81 참고문헌 82 부록 91Docto

Modélisation des signes dans les ontologies biomédicales pour l'aide au diagnostic.

Introduction : Établir un diagnostic médical fiable requiert l identification de la maladie d un patient sur la base de l observation de ses signes et symptômes. Par ailleurs, les ontologies constituent un formalisme adéquat et performant de représentation des connaissances biomédicales. Cependant, les ontologies classiques ne permettent pas de représenter les connaissances liées au processus du diagnostic médical : connaissances probabilistes et connaissances imprécises et vagues. Matériel et méthodes : Nous proposons des méthodes générales de représentation des connaissances afin de construire des ontologies adaptées au diagnostic médical. Ces méthodes permettent de représenter : (a) Les connaissances imprécises et vagues par la discrétisation des concepts (définition de plusieurs catégories distinctes à l aide de valeurs seuils ou en représentant les différentes modalités possibles). (b) Les connaissances probabilistes (les sensibilités et les spécificités des signes pour les maladies, et les prévalences des maladies pour une population donnée) par la réification des relations ayant des arités supérieures à 2. (c) Les signes absents par des relations et (d) les connaissances liées au processus du diagnostic médical par des règles SWRL. Un moteur d inférences abductif et probabiliste a été conçu et développé. Ces méthodes ont été testées à l aide de dossiers patients réels. Résultats : Ces méthodes ont été appliquées à trois domaines (les maladies plasmocytaires, les urgences odontologiques et les lésions traumatiques du genou) pour lesquels des modèles ontologiques ont été élaborés. L évaluation a permis de mesurer un taux moyen de 89,34% de résultats corrects. Discussion-Conclusion : Ces méthodes permettent d avoir un modèle unique utilisable dans le cadre des raisonnements abductif et probabiliste, contrairement aux modèles proposés par : (a) Fenz qui n intègre que le mode de raisonnement probabiliste et (b) García-crespo qui exprime les probabilités hors du modèle ontologique. L utilisation d un tel système nécessitera au préalable son intégration dans le système d information hospitalier pour exploiter automatiquement les informations du dossier patient électronique. Cette intégration pourrait être facilitée par l utilisation de l ontologie du système.Introduction: Making a reliable medical diagnosis requires the identification of the patient s disease based on the observation of signs. Moreover, ontologies provide an adequate and efficient formalism for medical knowledge representation. However, classical ontologies do not allow representing knowledge associated with medical reasoning such as probabilistic, imprecise, or vague knowledge. Material and methods: In the current work, general knowledge representation methods are proposed. They aim at building ontologies fitting to medical diagnosis. They allow to represent: (a) imprecise or vague knowledge by discretizing concepts (definition of several distinct categories thanks to threshold values or by representing the various possible modalities), (b) probabilistic knowledge (sensitivity, specificity and prevalence) by reification of relations of arity greater than 2, (c) absent signs by relations and (d) medical reasoning and reasoning on the absent signs by SWRL rules. An abductive reasoning engine and a probabilistic reasoning engine were designed and implemented. The methods were evaluated by use of real patient records. Results: These methods were applied to three domains (the plasma cell diseases, the dental emergencies and traumatic knee injuries) for which the ontological models were developed. The average rate of correct diagnosis was 89.34 %. Discussion-Conclusion: In contrast with other methods proposed by Fenz and García-crespo, the proposed methods allow to have a unique model which can be used both for abductive and probabilistic reasoning. The use of such a system will require beforehand its integration in the hospital information system for the automatic exploitation of the electronic patient record. This integration might be made easier by the use of the ontology on which the system is based.RENNES1-Bibl. électronique (352382106) / SudocSudocFranceF