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Biomedical subjects

J Bouaud

Publications and source records attributed to J Bouaud.

17 recordsLinked to original sources
PubMed2005

Guideline-based modeling of therapeutic strategies in the special case of chronic diseases.

Despite the availability of evidence-based clinical practice guidelines in most countries, patients with chronic diseases are still generally inadequately managed. One difficulty lies in the optimal synchronization of a patient with the guideline therapeutic strategy, especially when the history of her past treatments does not follow the recommended sequence of therapies. We propose a formal model to represent guideline-based therapeutic strategies as a two-level decision tree. The clinical level is used to identify a patient-specific clinical situation, on the basis of key elements of clinical examination (complication of hypertension, associated diseases). The therapeutic level is derived from the formalization of guideline-based strategies first represented as bidimensional matrices structured in lines of therapy and levels of therapeutic intention. A revised version based on the ordering of levels of therapeutic combination is then developed. The aim is to dynamically provide the best next step of treatment from the patient's therapeutic-history-based customization of the general therapeutic sequence established in the guideline for the corresponding clinical situation. A preliminary in vitro evaluation of the system on actual clinical cases showed a positive impact on physician compliance with a significant increase from 16 to 57%.

Canada↗
PubMed2004

Reminder-based or on-demand decision support systems: a preliminary study in primary care with the management of hypertension.

ASTI is a guideline-based decision support system for therapeutic prescribing in primary care with two modes of interaction. The "critic mode" operates as a reminder system to detect non guideline-compliant physician drug orders, whereas the "guided mode" operates on demand and provides physician guidance to help her establishing best recommended drug prescriptions for the management of hypertension. A preliminary evaluation study was conducted with 10 GPs to test the complementary nature of both modes of decision support. Results tend to validate our assumption that reminder-based interaction is appropriate for simple cases and that physicians are willing to use on-demand systems as clinical situations become more complex.

Decision Support Systems, Clinical↗
PubMed2002

Automatic generation of a metamodel from an existing knowledge base to assist the development of a new analogous knowledge base.

Knowledge acquisition is a key step in the development of knowledge-based systems and methods have been proposed to help elicitating a domain-specific task model from a generic task model. We explored how an existing validated knowledge base (KB) represented by a decision tree could be automatically processed to infer a higher level domain-specific task model. On-codoc is a guideline-based decision support system applied to breast cancer therapy. Assuming task identity and ontological proximity between breast and lung cancer domains, the generalization of the breast can-cer KB should allow to build a metamodel to serve as a guide for the elaboration of a new specific KB on lung cancer. Two types of parametrized generalization methods based on tree structure simplification and ontological abstraction were used. We defined a similarity distance and a generalization coefficient to select the best metamodel identified as the closest to the original decision tree of the most generalized metamodels.

Artificial Intelligence↗
PubMed2001

ONCODOC: a successful experiment of computer-supported guideline development and implementation in the treatment of breast cancer.

Originally published as textual documents, clinical practice guidelines have poorly penetrated medical practice because their editorial properties do not allow the reader to easily solve, at the point of care, a given medical problem. However, despite the proliferation of implemented clinical practice guidelines as decision support systems providing an easy access to patient-centered information, there is still little evidence of high physician compliance to guidelines recommendations. Apart from physicians' psychological reluctance, the incompleteness of guideline knowledge and the impreciseness of the terms used, another reason may be that, although suited to average patients, clinical practice guideline recommendations are not a substitute for the physician-controlled clinical judgement that should be applied to each actual individual patient. Therefore, computer-based approaches based on the automation of context-free operationalization of guideline knowledge, although providing uniform optimal strategies to problem-focused care delivery, may generate inappropriate inferences for a specific patient that the physician does not follow in practice. Rather than providing automated decision support, ONCODOC allows the clinician to control the operationalization of guideline knowledge through his hypertextual reading of a knowledge base encoded as a decision tree. In this way, he has the opportunity to interpret the information provided in the context of his patient, therefore, controlling his categorization to the closest matching formal patient. Experimented in life-size ONCODOC demonstrated good appropriation of the system by physicians with significantly high scores of compliance. We successfully tested the implemented strategy and the knowledge base in a second medical institution, giving then a noticeable example of reuse and sharing of encoded guideline knowledge across institutions.

Artificial Intelligence↗
PubMed2001

A before-after study using OncoDoc, a guideline-based decision support-system on breast cancer management: impact upon physician prescribing behaviour.

Guideline-based decision support systems have been developed to influence the prescribing behaviour of clinicians, but they have not yet shown to increase physician compliance with best practices in routine. OncoDoc is a non-automated system that allows flexibility in guideline interpretation to obtain best patient-specific recommendations at the point of care. OncoDoc is applied to breast cancer management. We have experimented the system at the Institut Gustave Roussy with a before-after study in which treatment decisions for breast cancer patients were measured before and after using the system in order to evaluate its impact upon physicians' prescribing behaviour. After 4 months, 127 decisions were recorded. Physicians compliance with OncoDoc was significantly improved (p < 10(-4) ) to reach 85.03% after using the system. Comparison of initial and final decisions showed that physicians modified their prescription in 31% of the cases. Clinical trial accrual rate increased of 50%, though not statistically significant because estimated on small figures.

Artificial Intelligence↗
PubMed2001

ASTI: a guideline-based drug-ordering system for primary care.

Existing computer-based ordering systems for physicians provide effective drug-centered checks but offer little assistance for optimizing the overall patient-centered treatment strategy. Evidence-based clinical practice guidelines have been developed to disseminate state-of-the-art information concerning treatment strategy but these guidelines are poorly used in routine practice. The ASTI project aims to design a guideline-based ordering system to enable general practitioners to avoid prescription errors and to improve compliance with best therapeutic practices. The " critic mode " operates as a background process and corrects the physician's prescription on the basis of automatically triggered elementary rules that account for isolated guideline recommendations. The " guided mode " directs the physician to the best treatment by browsing a comprehensive guideline knowledge base represented as a decision tree. A first prototype, applied to hypertension, is currently under development.

Decision Support Systems, Clinical↗
PubMed1999

Users' evaluation of OncoDoc, a breast cancer therapeutic guideline delivered at the point of care.

Despite the dissemination of computer-based "clinical practice guidelines" as decision support systems, low practical compliance rates are still observed. The reason commonly invoked is that such recommendations, suited to average patients, are not rules for all the patients. Rather than providing automatic decision support, OncoDoc allows the clinician to operationalize the implemented breast cancer therapeutic expertise through his hypertextual reading of the knowledge base. In this way, he has the opportunity to interpret the information provided in the context of his patient therefore controlling his categorization to the closest appropriate "average patient". After a four-month real-life experimentation of the system, a survey was conducted among the users. The observed compliance, significantly higher than the best figures found in the literature, and the clinicians objective and subjective evaluation of the system reinforced the implementation choices adopted in OncoDoc.

Artificial Intelligence↗
PubMed1998

From text to knowledge: a unifying document-centered view of analyzed medical language.

Although medical language processing (MLP) has achieved some success, the actual use and dissemination of data extracted from free text by MLP systems is still very limited. We claim that the adoption of an 'enriched-document' paradigm (or 'document-centered' view) can help to address this issue. We present this paradigm and explain how it can be implemented, then discuss its expected benefits both for end-users and MLP researchers.

Artificial Intelligence↗
PubMed1998

Hypertextual navigation operationalizing generic clinical practice guidelines for patient-specific therapeutic decisions.

Despite the proliferation of implemented clinical practice guidelines, there is still little evidence of physicians compliance to formal standards. The ONCODOC project proposes a framework for elaborating generic decision support guidelines in a document-based paradigm with a knowledge-based approach. It has been first applied to assist clinicians in the treatment of breast cancer patients. Therapeutic expertise has been encoded as a decision tree. The decision process is driven by the clinician who interactively browses a hypertext version of the decision tree. During the navigation, he incrementally assigns values to decision parameters on the basis of his free interpretation of his patient's condition and thus builds a clinical context leading to patient-specific therapeutic recommendations. These guidelines are distributed on a hospital intranet and are evaluated at the point of care in an oncology department.

Breast Neoplasms↗
PubMed1997

Corpus-based identification and refinement of semantic classes.

Medical Language Processing (MLP), especially in specific domains, requires fine-grained semantic lexica. We examine whether robust natural language processing tools used on a representative corpus of a domain help in building and refining a semantic categorization. We test this hypothesis with ZELLIG, a corpus analysis tool. The first clusters we obtain are consistent with a model of the domain, as found in the SNOMED nomenclature. They correspond to coarse-grained semantic categories, but isolate as well lexical idiosyncrasies belonging to the clinical sub-language. Moreover, they help categorize additional words.

Classification↗
PubMed1997

Evaluating a normalized conceptual representation produced from natural language patient discharge summaries.

The Menelas project aimed to produce a normalized conceptual representation from natural language patient discharge summaries. Because of the complex and detailed nature of conceptual representations, evaluating the quality of output of such a system is difficult. We present the method designed to measure the quality of Menelas output, and its application to the state of the French Menelas prototype as of the end of the project. We examine this method in the framework recently proposed by Friedman and Hripcsak. We also propose two conditions which enable to reduce the evaluation preparation workload.

Abstracting and Indexing↗
PubMed1996

Navigating through a document-centered electronic medical record: a mock-up based on WWW technology.

Current WWW technology facilitates the development of "hypertext" applications. A hospital-wide study of users' requirements in France led to a document-centered approach to the patient Electronic Medical Record (EMR). In order to refine such a specification, and taking advantage of WWW technology, we have developed a running mock-up of a document-based EMR from an actual paper-based patient record. Synthesis documents were added and linked to original replicated paper documents to form a hypertextual EMR. The mock-up has been presented to health care professional boards to gather their remarks and wishes, and then enhanced accordingly. The current version reflects (part of) their requirements for an EMR, and is presented in this paper.

Clinical Medicine↗
PubMed1995

A multi-lingual architecture for building a normalised conceptual representation from medical language.

The overall goal of MENELAS is to provide better access to the information contained in natural language patient discharge summaries (PDSs), through the design and implementation of a prototype able to analyse medical texts. The approach taken by MENELAS is based on the following key principles: (i) to maximise the usefulness of natural language analysis and the usability of its results, the output of natural language analysis must be a normalised conceptual representation of medical information; and (ii) to maximise the reuse of resources, language analysis should be domain-independent and conceptual representation should be language-independent. This paper discusses the results obtained and the issues raised when implementing these principles during the project.

Artificial Intelligence↗
PubMed1995

Issues in the structuring and acquisition of an ontology for medical language understanding.

Medical natural language understanding basically aims at representing the contents of medical texts in a formal, conceptual representation. The understanding process itself increasingly relies on a body of domain knowledge, generally expressed in the same conceptual formalism. The design of such a conceptual representation is a key knowledge-acquisition issue. When representing knowledge, the most important point is to ensure that the formal exploitation of the knowledge representation conforms to its meaning in the domain. We examined some methodological and theoretical principles to enforce this conformity. These principles result from our experience in MENELAS, a medical language understanding project.

Artificial Intelligence↗
PubMed1993

Structuration and acquisition of medical knowledge. Using UMLS in the conceptual graph formalism.

The use of a taxonomy, such as the concept type lattice (CTL) of Conceptual Graphs, is a central structuring piece in a knowledge-based system. The knowledge it contains is constantly used by the system, and its structure provides a guide for the acquisition of other pieces of knowledge. We show how UMLS can be used as a knowledge resource to build a CTL and how the CTL can help the process of acquisition for other kinds of knowledge. We illustrate this method in the context of the MENELAS natural language understanding project.

Artificial Intelligence↗
PubMedDate not supplied

[Patient-centered consultation of "good practice guidelines": OncoDoc, a decision support system for the management of breast cancer patients].

Beyond considerations of cost-effectiveness, clinical practice guidelines (CPG) can reduce practice variations and thus improve the quality of care. However, despite the proliferation of implemented CPG and their wide diffusion thanks to Internet-based technologies, physicians' compliance with formal standards is weak. Developed according to a document-based paradigm, OncoDoc proposes an original framework for implementing CPG. Domain knowledge has been encoded as a decision tree whose branches are both exclusive and exhaustive. This generic knowledge is operationalized at the point of care by the interactive building, through hypertextual navigation, of a patient-based clinical context leading to specific therapeutic recommendations. OncoDoc has first been applied to the management of breast cancer patients and demonstrated within a full-scale experimentation in a clinical setting a compliance of 80 per cent.

Breast Neoplasms↗