[Completeness assessment of the Breton registry of congenital abnormalities: A checking tool based on hospital discharge data]. / Contrôle automatisé de l'exhaustivité du registre breton des malformations congénitales à partir des données des bases médico-administratives des établissements de santé.

BACKGROUND: Exhaustiveness is required for registries. In the Breton registry of congenital abnormalities, cases are recorded at the source. We use hospital discharge data in order to verify the completeness of the registry. In this paper, we present a computerized tool for completeness assessment applied to the Breton registry. METHODS: All the medical information departments were solicited once a year, asking for infant medical stays for newborns alive at one year old and for mother's stays if not. Files were transmitted by secure messaging and data were processed on a secure server. An identity-matching algorithm was applied and a similarity score calculated. When the record was not linked automatically or manually, the medical record had to be consulted. The exhaustiveness rate was assessed using the capture recapture method and the proportion of cases matched manually was used to assess the identity matching algorithm. RESULTS: The computerized tool bas been used in common practice since June 2012 by the registry investigators. The results presented concerned the years 2011 and 2012. There were 470 potential cases identified from the hospital discharge data in 2011 and 538 in 2012, 35 new cases were detected in 2011 (32 children born alive and 3 stillborn), and 33 in 2012 (children born alive). There were respectively 85 and 137 false-positive cases. The theorical exhaustiveness rate reached 91% for both years. The rate of exact matching amounted to 68%; 6% of the potential cases were linked manually. CONCLUSION: Hospital discharge databases contribute to the quality of the registry even though reports are made at the source. The implemented tool facilitates the investigator's work. In the future, use of the national identifying number, when allowed, should facilitate linkage between registry data and hospital discharge data.

Computational integrative physiology: at the convergence of the life and computational sciences.

OBJECTIVE: In this paper we outline how Computational Integrative Physiology (CIP) can help unravel the mechanisms of normal and pathological biological processes. Our objective is to illustrate how CIP is firmly grounded on the life and computational sciences. METHOD: After describing a general theoretical frame-work for CIP, we will center our discussion on cardiac rhythmic disorders with a particular focus on the Long QT syndrome that will serve as a case example. Within this context, we will describe multi-scale processes in biological, medical and in general mathematical terms, starting from the control of gene expression to the electrical activity of the entire heart. We will therefore proceed from the smaller microscopic scales to the larger macroscopic ones. In doing so, we will illustrate, at least in a qualitative sense, how CIP can be accomplished by showing some of the relations that can exist between mathematical variables characterizing models of different space-scales. CONCLUSION: We will conclude by putting forth how CIP and the related fields of bioinformatics and medical informatics are necessary to derive meaningful knowledge from the huge and exponentially growing biological and medical data.

An interactive 3D anisotropic cellular automata model of the heart.

A 3D cellular anisotropic automata model with modifiable geometry is described. The modeling parameters include grain size, fiber orientation, and free-wall and septal thickness. From this modifiable model, three specific models corresponding to normal heart, left ventricular hypertrophy, and ventricular dilatation were generated. Each model is a conduction and propagation model in which the atria, the major atrial vessel bases, the ventricles, and the specialized conduction system are represented. Muscle tissues are modeled as bundles of fibers with anisotropic conduction speed of the activation wavefronts. Regional variations of conduction, refractory gradients, and regional potential gradients can also be specified before each simulation. Each element has adaptive properties with respect to cycle length and to the prematurity of incoming impulses. Action potentials can be specified for each cell and an equivalent source formulation is carried out to simulate the vectorcardiogram and the corresponding 12-standard-lead electrocardiogram.

A qualitative model for computer-assisted instruction in cardiology.

CARDIOLAB is an interactive computational framework dedicated to teaching and computer-aided diagnosis in cardiology. The framework embodies models that simulate the heart's electrical activity. They constitute the core of a Computer-Assisted Instruction (CAI) program intended to teach, in a multimedia environment, the concepts underlying rhythmic disorders and cardiac diseases. The framework includes a qualitative model (QM) which is described in this paper. During simulation using QM, dynamic sequences representing impulse formation and conduction processes are produced along with the corresponding qualitative descriptions. The corresponding electrocardiogram (ECG) and ladder diagram are also produced, and thus, both qualitative notions and quantitative facts can be taught via the model. We discuss how qualitative models in particular, and computational models in general can enhance the teaching capability of CAI programs.

Spatio-temporal reasoning for multi-scale modeling in cardiology.

In this paper, we present an overview of the CARDIOLAB environment where the heart's electrical activity is modeled at distinct space and time scales. CARDIOLAB uses three models, two of which are based on cellular automata, and the third on qualitative simulation. They are combined around a blackboard for multi-scale quantitative and qualitative modeling of cardiac electrical activity. A general account on how spatio-temporal representation and reasoning methods are applied to produce heuristic associations is also presented.

A cellular automata model of the heart and its coupling with a qualitative model.

Cellular Automata (CA) models offer a good compromise between computational complexity and biological plausibility while qualitative models have expressive power for explicitly describing dynamic processes. In this paper we present a 2D CA model and its coupling with a qualitative model. The CA model includes elements characterizing muscle, nodal tissue, and bypass conduction. Each element exhibits adaptive properties to cycle length and to the prematurity of incoming impulses. A crude electrocardiogram is also simulated via an equivalent source formulation. Arrhythmias such as the Wenckebach phenomenon, atrial flutter, or extrasystole-triggered tachyarrhythmias can be simulated using relatively simple models when they incorporate the fast conduction system with muscle tissue and when the model elements exhibit adaptive properties. We then illustrate how a CA model can be coupled to a qualitative model to produce a system that combines the fine grained description of CA models with the high level interpretative role of qualitative models.

Introducing spatio-temporal reasoning into the inverse problem in electroencephalography.

Studying the Brain's Electrical and Magnetic Signals (BEMS) requires the contribution of many area of research that include anatomy, neurophysiology and electromagnetic theory. NEUROLAB is a framework dedicated to the study of brain disorders. Upon completion, it should provide users with an intelligent computational environment that incorporates qualitative and quantitative models of the brain and head, and a model for representing and reasoning about time and space. Spatio-temporal knowledge of a given problem is represented as a constraint network where to each node of the network are attached temporal and spatial variables that must satisfy the constraints defined by the arch labels connecting the nodes. In this paper, we show how temporal reasoning can be combined with spatial descriptions to produce different scenarios of possible seizure spread. These scenarios can provide a priori information for the inverse problem the role of which is to localise the sources of the observed BEMS.

Deep knowledge and computer-assisted instruction in cardiology.

In this paper, we describe a qualitative heart model that is part of a computing environment, CARDIOLAB, and whose role includes the diagnosis and Computer Assisted Instruction (CAI) in cardiology. The model is based on a "deep knowledge" approach to diagnosis. Deep knowledge representations model the inner works of complex physical systems. Explicit representations of system components, component functions, and behavior allow a principled form of reasoning that extends the classical rule-based, first-generation expert systems. One of the main advantage of model-based diagnosis resides in the possibility of providing explanations to observed facts or measured data. This feature can be incorporated into CAI programs with similar benefits.