Biomedical subjects
Publications and source records attributed to J F Boisvieux.
UNLABELLED: MOSAIC is a set of tools for the segmentation of multiple aligned DNA sequences into homogeneous zones. The segmentation is based on the distribution of mutational events along the alignment. As an example, the analysis of one repeated sequence belonging to the subtelomeric regions of the yeast genome is presented. AVAILABILITY: Free access from ftp://ftp.biomath.jussieu.fr/pub/papers/MOSAIC
The hidden Markov model (HMM) was used to identify recurrent short 3D structural building blocks (SBBs) describing protein backbones, independently of any a priori knowledge. Polypeptide chains are decomposed into a series of short segments defined by their inter-alpha-carbon distances. Basically, the model takes into account the sequentiality of the observed segments and assumes that each one corresponds to one of several possible SBBs. Fitting the model to a database of non-redundant proteins allowed us to decode proteins in terms of 12 distinct SBBs with different roles in protein structure. Some SBBs correspond to classical regular secondary structures. Others correspond to a significant subdivision of their bounding regions previously considered to be a single pattern. The major contribution of the HMM is that this model implicitly takes into account the sequential connections between SBBs and thus describes the most probable pathways by which the blocks are connected to form the framework of the protein structures. Validation of the SBBs code was performed by extracting SBB series repeated in recoding proteins and examining their structural similarities. Preliminary results on the sequence specificity of SBBs suggest promising perspectives for the prediction of SBBs or series of SBBs from the protein sequences.
In this paper Bayesian networks modelling is applied to a multidimensional model of depression. The characterization of the probabilistic model exploits expert knowledge to associate latent concentrations of neurotransmitters and symptoms. An evolution perspective is also considered. Specific criteria are introduced to detect the influence of the latent variable on the observation of symptoms. The Bayesian analysis is carried out using Gibbs sampling technique which is implemented in the BUGS software. The estimation phase leads to the selection of symptoms entering into the definition of behavioral syndromes. Results on real data are discussed. The last section deals with simulation experiments. Simulation results confirm our methodological choices. Results of the paper can enlarge to the central problem of the management of latent variables in Bayesian networks modelling.
MOTIVATION: Complete genomic sequences will become available in the future. New methods to deal with very large sequences (sizes beyond 100 kb) efficiently are required. One of the main aims of such work is to increase our understanding of genome organization and evolution. This requires studies of the locations of regions of similarity. RESULTS: We present here a new tool, ASSIRC ('Accelerated Search for SImilarity Regions in Chromosomes'), for finding regions of similarity in genomic sequences. The method involves three steps: (i) identification of short exact chains of fixed size, called 'seeds', common to both sequences, using hashing functions; (ii) extension of these seeds into putative regions of similarity by a 'random walk' procedure; (iii) final selection of regions of similarity by assessing alignments of the putative sequences. We used simulations to estimate the proportion of regions of similarity not detected for particular region sizes, base identity proportions and seed sizes. This approach can be tailored to the user's specifications. We looked for regions of similarity between two yeast chromosomes (V and IX). The efficiency of the approach was compared to those of conventional programs BLAST and FASTA, by assessing CPU time required and the regions of similarity found for the same data set. AVAILABILITY: Source programs are freely available at the following address: ftp://ftp.biologie.ens. fr/pub/molbio/assirc.tar.gz CONTACT: vincens@biologie.ens.fr, hazout@urbb.jussieu.fr
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.
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.
The patient record is a repository for knowledge about a patient. Work in Artificial Intelligence and knowledge representation has evidenced the intrinsic difficulty of formalizing knowledge for computer processing. It is therefore not a surprise that most attempts at computerizing the patient record have only had a limited degree of success or applicability. We claim that this is due to the fact that medicine is an empirical domain, and thus fundamentally resists formalization. Therefore, the only way medical knowledge can be fully expressed is through natural languages which is indeed what clinicians actually use. We proposed and designed an electronic medical record which adheres to this hypothesis and where structured documents play a prominent role.
MOTIVATION: The approaches usually used for building large genetic maps consist of dividing the marker set into linkage groups and provide local orders that can be tested by multi-point linkage analysis. To deal with the limitations of these approaches, a strategy taking the marker set into account globally is defined. RESULTS: The paper presents a new approach called 'Bi-Dimensional Scaling Map (BDS-Map) for inferring marker orders and distances in genetic maps based on the use of an additional dimension orthogonal to the map into which markers are projected. Dynamical forces based on a two-point analysis are applied to tend to optimize the marker locations in space. The efficiency of the approach is exemplified on real data (16 and 70 markers on chromosomes 6 and 2, respectively) and simulated data (50 maps of 70 markers).
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.
Monitoring patients hospitalized in hemato-oncology departments to undergo clinical protocols of therapy is a complex task. The main difficulty arises in the management of the oncology protocol as well as in the management of critical episodes of acute illness which frequently occur due to high toxicity of the used antimitotics. This problem of controlling a patient's condition can be conceptualized within the control theory paradigm as the task of controlling a process whose state changes over time and can deviate unacceptably from a normal range. Upon request of a French clinical department of hemato-oncology, we developed an intelligent patient monitor named SEPIA to assist clinicians in this task. Following the control theory analogy at the level of knowledge bases design, we have modeled the medical knowledge as control information to represent the medical actions, and state information is used as feedback control to characterize the patient's state. After a general presentation of SEPIA, the analogies between control theory actors and SEPIA's components are specified. The article then focuses on state variables representation. The data reduction process, activated as new data are given to the system, is described. A simulated running session is finally presented to illustrate the whole reasoning process.
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.
Monitoring patients hospitalized in hemato-oncology departments to undergo clinical protocols of therapy is a complex task. The main difficulty arises in the follow-up of the oncology protocol and in the management of critical episodes of acute illness which frequently occur due to the high toxicity of the antimitotics used. This problem can be conceptualized within the control theory paradigm as the task of controlling a process whose state can deviate unacceptably from a normal range. Following the control theory analogy at the level of knowledge bases design, we have modeled the medical knowledge as control information to represent the medical actions, and state information is used as a feedback control to readjust the command.
The monitoring and treatment of patients in a care unit is a complex task in which even the most experienced clinicians can make errors. A hemato-oncology department in which patients undergo chemotherapy asked for a computerized system able to provide intelligent and continuous support in this task. One issue in building such a system is the definition of a control architecture able to manage, in real time, a treatment plan containing prescriptions and protocols in which temporal constraints are expressed in various ways, that is, which supervises the treatment, including controlling the timely execution of prescriptions and suggesting modifications to the plan according to the patient's evolving condition. The system to solve these issues, called SEPIA, has to manage the dynamic, processes involved in patient care. Its role is to generate, in real time, commands for the patient's care (execution of tests, administration of drugs) from a plan, and to monitor the patient's state so that it may propose actions updating the plan. The necessity of an explicit time representation is shown. We propose using a linear time structure towards the past, with precise and absolute dates, open towards the future, and with imprecise and relative dates. Temporal relative scales are introduced to facilitate knowledge representation and access.
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.
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.
In 1985, an assessment of arterial hypertension treatment in insulin-treated diabetic patient gave disappointing results. In 1988, we carried out another study in order to assess the impact of new antihypertensive drugs (angiotensin converting enzyme inhibitors and calcium antagonists) on the management of arterial hypertension and to identify patients in whom strict normal blood pressure control is mandatory. Seven hundred and fifty four patients were selected. The prevalence of arterial hypertension was 38.4 p. 100 (n = 290). Two hundred and thirty five patients (31.2 p. 100) were on antihypertensive treatment: monotherapy: 60.4 p. 100 (n = 142), bitherapy: 30.6 p. 100 (n = 72), tritherapy: 9 p. 100 (n = 21). In descending order of frequency, the following drugs were used: angiotensin converting enzyme inhibitors, calcium antagonists, diuretics, cardio-selective beta-blockers, central acting agents. Blood pressure values significantly decreased (148/83 mmHg, in 1988, vs 157/85 mmHg, in 1985, p less than 0.05). However, 20 p. 100 of the patients still had blood pressure values greater than or equal to 160 and/or 95 mmHg with or without antihypertensive treatment, and on average, blood pressure values remained higher in patients with antihypertensive treatment than in those without (148/83 mmHg vs 131/77 mmHg, p less than 0.001). Patients with urinary albumin excretion above or equal 30 mg/24 h compared to those with normal albuminuria had significant higher values of blood pressure, glycosylated haemoglobin and blood lipids (p less than 0.01). Only 51 p. 100 of these patients, received an antihypertensive treatment. This study emphasizes the difficulty of antihypertensive treatment in insulin-treated diabetic patients and the necessity to improve education in patients with high risk for widespread angiopathy, and particularly those with increased urinary albumin excretion.
In small series of selected hypertensive diabetic patients the short-term benefit of antihypertensive treatments is well documented. The purpose of this study was to analyse the management of arterial hypertension in a cohort of 612 insulin-treated diabetic out-patients routinely and consecutively examined in a diabetologic clinic between January 1, 1985 and April 1, 1986. The prevalence of arterial hypertension (i.e. patients with blood pressure values greater than or equal to 160 and/or 95 mmHg or on antihypertensive treatment) was 38 per cent (232 patients). One hundred and eighty-two patients (29.7 per cent) received an antihypertensive treatment (one drug 78 per cent, two drugs 17 per cent, three drugs 5 per cent). In decreasing order of frequency these drugs were: beta-blockers, diuretics, central acting agents, angiotensin-converting enzyme inhibitors and calcium antagonists. In treated hypertensive diabetic patients the mean +/- SD systolic blood pressure (157.1 +/- 19.1 mmHg) and diastolic pressure (85.3 +/- 9.3 mmHg) remained higher than in patients without antihypertensive treatment (133.4 +/- 17.2 and 77.8 +/- 8.3 mmHg respectively). One hundred and forty-two diabetic patients still had blood pressure values greater than or equal to 160 and/or 95 mmHg during visits; 92 were on antihypertensive treatment, 50 were untreated. In hypertensive diabetic patients the mean total glycosylated haemoglobin (HbA1) level was higher than in normotensive diabetic patients (9 +/- 1.6 versus 8.6 +/- 1.8; P less than 0.05). Hypertensive insulin-treated diabetics are the most seriously ill patients, they are under inadequate care. Wrong choice of antihypertensive drugs, incorrect goals of blood pressure reduction, lack of information and education to improve compliance were the main reasons for the poor results of this study.
An Expert System (ES) has been connected to a database management system for the management and follow-up of hypertensive patients. The patient data base, called Artemis, contains approximately 18,000 medical records. About 90% of the initial informations used by the ES is contained in the medical records of the Artemis data base. The knowledge base consists of 870 rules. A first group of rules allows the description of knowledge structures (hierachies, graphs and mutual exclusions). The second group consists of production rules which describe the dynamic reasoning of the expert. The inference engine uses a combination of forward and backward chaining. The ES produce diagnostic hypotheses (possible causes of hypertension) and therapeutic suggestions before and after requiring additional information (patient supplementary interrogation, biological or radiological investigations). The evaluation of the diagnostic performance of the ES was made on 40 confirmed cases of secondary hypertension (SH) and 40 cases of essential hypertension (EH). The initial initial diagnosis, just after the forward chaining step, was correct in 17 cases of SH and 32 cases of EH. The final diagnosis proposed after several steps of forward and backward chaining was correct in 37 cases (92%) of SH and 36 (90%) of EH. Averages of 5 (EH) and 8 (SH) questions were formulated by the ES to reach the final diagnosis. The integration of the ES to the database is expected to facilitate the validation of the knowledge base and to enhance its overall acceptability. Whether or not such an integration will be useful and accepted as a complementary tool by physicians remains however an open question.