Laurent Lecornu

Development framework for a patient-centered record

Patient records have been developed to support the physician-oriented medical activity scheme. One recommended yet rarely studied alternative, expected to improve healthcare, is the patient-centered record. We propose a development framework for such record, which includes domain-specific database models at the conceptual level, analyzing the fundamental role of complementary information destined to ensure proper patient understanding of related clinical situations. A patient-centered awareness field study of user requirements and medical workflow was carried out in three medical services and two technical units to identify the most relevant elements of the framework, and compared to the definitions of a theoretical approach. Three core data models - centered on the patient, medical personnel, and complementary patient information, corresponding to the determined set of entities, information exchanges and actors roles, constitute the technical recommendations of the development framework. An open source proof of concept prototype was developed to show the model feasibility. The resulting patient-centered record development framework implies particular medical personnel contributions to supply complementary information.

REFEROCOD: A probabilistic method to medical coding support

Choosing diagnosis codes is a non-intuitive operation for the practitioner. Mistakes are frequent with severe consequences on healthcare evaluation and funding. French physicians have to assign a code for everything they do and they are not spared with these kinds of errors. We propose a tool named REFEROCOD to support the medical coding task in order to minimize errors without losing time, by suggesting a list of codes in accordance with the physician activities and of the patient medical context. The proposed method uses probabilistic knowledge and indicates the probability to have a proper diagnosis code considering the realized procedure, age, sex and other information available in the discharge abstract.

Extraction of vessel contours in angiograms by simultaneous tracking of the two edges

A new method is presented for the tracking of vessel contours in X-ray angiographic images by means of graph theory. A model of blood vessels has been incorporated in our method which is applied to both subtracted and unsubtracted angiographic images with good accuracy and efficiency.<<ETX>>

A Methodology to Evaluate Important Dimensions of Information Quality in Systems

Assessing the quality of the information proposed by an information system has become one of the major research topics in the last two decades. A quick literature survey shows that a significant number of information quality frameworks are proposed in different domains of application: management information systems, web information systems, information fusion systems, and so forth. Unfortunately, they do not provide a feasible methodology that is both simple and intuitive to be implemented in practice. In order to address this need, we present in this article a new information quality methodology. Our methodology makes use of existing frameworks and proposes a three-step process capable of tracking the quality changes through the system. In the first step and as a novelty compared to existing studies, we propose decomposing the information system into its elementary modules. Having access to each module allows us to locally define the information quality. Then, in the second step, we model each processing module by a quality transfer function, capturing the module’s influence over the information quality. In the third step, we make use of the previous two steps in order to estimate the quality of the entire information system. Thus, our methodology allows informing the end-user on both output quality and local quality. The proof of concept of our methodology has been carried out considering two applications: an automatic target recognition system and a diagnosis coding support system.

Medical diagnosis by possibilistic classification reasoning

In medicine, diagnostic reasoning refers to the approaches used by physicians with the aim of achieving a medical diagnosis concerning a given patient. This paper presents a new approach of medical decision support systems. The proposed approach is based on the use of possibility theory as a global framework, including knowledge representation (as a possibilistic pair of measures: Necessity, Possibility); and, building a possibilistic medical knowledge base (to be exploited in order to make a diagnostic decision (classification of new medical cases)). The efficiency validation of the proposed approach is conducted using an Endoscopic Knowledge and Case Base systems. Obtained results confirm that the proposed approach constitutes an efficient tool in terms of medical knowledge representation and possibilistic diagnostic reasoning.

Simultaneous tracking of the two edges of linear structures

A new approach is presented for the detection of linear structures boundaries. The initial objective is the structure detection and its analysis, especially the diameter estimation. The main idea of the method is to use a uniform cost algorithm adapted to the two borders simultaneous tracking. The node represents a set of two edges (a right border and a left border linked by a diameter vector). The cost of the node is calculated from the contrast of the structure border, the angle between the diameter vector and the local edge gradient vectors and the curvature of the path. In order to reduce the search complexity, some heuristics are introduced. For each detected structure, a starting node and research area are defined. The preliminary results obtained on synthetic images and on DSA images are very interesting. The method allows the detection to be performed through the branching structures and overlapping ones. In addition, simulation studies show the robustness of the method with respect to noise.<<ETX>>

ANTEROCOD: Actuarial survival curves applied to medical coding support for chronic diseases

For the practitioner, choosing diagnosis codes is a non-intuitive operation. Mistakes are frequent, causing severe consequences on healthcare performance evaluation and funding. French physicians have to assign a code to all their activities and are frequently prone to these errors. Given that most of the time and particularly for chronic diseases indexed information is already available, we propose a tool named AnterOcod, in order to support the medical coding task. It suggests the list of most relevant plausible codes, predicted from the patients earlier hospital stays, according to a set of previously utilized diagnosis codes. Our method applies the estimation of code reappearance rates, based on an equivalent approach to actuarial survival curves. Around 33% of the expected correct diagnosis codes were retrieved in this manner, after evaluating 998 discharge abstracts, significantly improving the coding task.

An Efficient Association Rule Mining Algorithm for Classification

In this paper, we propose a new Association Rule Mining algorithm for Classification (ARMC). Our algorithm extracts the set of rules, specific to each class, using a fuzzy approach to select the items and does not require the user to provide thresholds. ARMC is experimentaly evaluated and compared to state of the art classification algorithms, namely CBA, PART and RIPPER. Results of experiments on standard UCI benchmarks show that our algorithm outperforms the above mentionned approaches in terms of mean accuracy.

Quality Analysis of Sensors Data for Personal Health Records on Mobile Devices

Data collected by multiple physiological sensors are being increasingly used for wellness monitoring or disease management, within a pervasiveness context facilitated by the massive use of mobile devices. These abundant complementary raw data are challenging to understand and process, because of their voluminous and heterogeneous nature, as well as the data quality issues that could impede their utilization. This chapter examines the main data quality questions concerning six frequently used physiological sensors—glucometer, scale, blood pressure meter, heart rate meter, pedometer, and thermometer—as well as patient observations that may be associated to a given set of measurements. We discuss specific details that are either overlooked in the literature or avoided by data exploration and information extraction algorithms, but have significant importance to properly preprocess these data. Making use of different types of formalized knowledge, according to the characteristics of physiological measurement devices, relevant data handled by a Personal Health Record on a mobile device, are evaluated from a data quality perspective, considering data deficiencies factors, consequences, and reasons. We propose a general scheme for sensors data quality characterization adapted to a pervasive scenario.

Information quality measurement of medical encoding support based on usability

Medical encoding support systems for diagnoses and medical procedures are an emerging technology that begins to play a key role in billing, reimbursement, and health policies decisions. A significant problem to exploit these systems is how to measure the appropriateness of any automatically generated list of codes, in terms of fitness for use, i.e. their quality. Until now, only information retrieval performance measurements have been applied to estimate the accuracy of codes lists as quality indicator. Such measurements do not give the value of codes lists for practical medical encoding, and cannot be used to globally compare the quality of multiple codes lists. This paper defines and validates a new encoding information quality measure that addresses the problem of measuring medical codes lists quality. It is based on a usability study of how expert coders and physicians apply computer-assisted medical encoding. The proposed measure, named ADN, evaluates codes Accuracy, Dispersion and Noise, and is adapted to the variable length and content of generated codes lists, coping with limitations of previous measures. According to the ADN measure, the information quality of a codes list is fully represented by a single point, within a suitably constrained feature space. Using one scheme, our approach is reliable to measure and compare the information quality of hundreds of codes lists, showing their practical value for medical encoding. Its pertinence is demonstrated by simulation and application to real data corresponding to 502 inpatient stays in four clinic departments. Results are compared to the consensus of three expert coders who also coded this anonymized database of discharge summaries, and to five information retrieval measures. Information quality assessment applying the ADN measure showed the degree of encoding-support system variability from one clinic department to another, providing a global evaluation of quality measurement trends.