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Objective In this work, we propose an ontology-driven approach to identify events and their attributes from episodes of care included in medical reports written in Italian. For this language, shared resources for clinical information extraction are not easily accessible. Materials and methods The corpus considered in this work includes 5432 non-annotated medical reports belonging to patients with rare arrhythmias. To guide the information extraction process, we built a domain-specific ontology that includes the events and the attributes to be extracted, with related regular expressions. The ontology and the annotation system were constructed on a development set, while the performance was evaluated on an independent test set. As a gold standard, we considered a manually curated hospital database named TRIAD, which stores most of the information written in reports. Results The proposed approach performs well on the considered Italian medical corpus, with a percentage of correct annotations above 90% for most considered clinical events. We also assessed the possibility to adapt the system to the analysis of another language (i.e., English), with promising results. Discussion and conclusion Our annotation system relies on a domain ontology to extract and link information in clinical text. We developed an ontology that can be easily enriched and translated, and the system performs well on the considered task. In the future, it could be successfully used to automatically populate the TRIAD database.

Background and objective The evaluation of the clinical status of a patient is frequently based on the temporal evolution of some parameters, making the detection of temporal patterns a priority in data analysis. Temporal abstraction (TA) is a methodology widely used in medical reasoning for summarizing and abstracting longitudinal data. Methods This paper describes JTSA (Java Time Series Abstractor), a framework including a library of algorithms for time series preprocessing and abstraction and an engine to execute a workflow for temporal data processing. The JTSA framework is grounded on a comprehensive ontology that models temporal data processing both from the data storage and the abstraction computation perspective. The JTSA framework is designed to allow users to build their own analysis workflows by combining different algorithms. Thanks to the modular structure of a workflow, simple to highly complex patterns can be detected. The JTSA framework has been developed in Java 1.7 and is distributed under GPL as a jar file. Results JTSA provides: a collection of algorithms to perform temporal abstraction and preprocessing of time series, a framework for defining and executing data analysis workflows based on these algorithms, and a GUI for workflow prototyping and testing. The whole JTSA project relies on a formal model of the data types and of the algorithms included in the library. This model is the basis for the design and implementation of the software application. Taking into account this formalized structure, the user can easily extend the JTSA framework by adding new algorithms. Results are shown in the context of the EU project MOSAIC to extract relevant patterns from data coming related to the long term monitoring of diabetic patients. Conclusions The proof that JTSA is a versatile tool to be adapted to different needs is given by its possible uses, both as a standalone tool for data summarization and as a module to be embedded into other architectures to select specific phenotypes based on TAs in a large dataset.

Objective In this paper we present the clinical deployment and evaluation of a computerized system, EMOSTAT, aimed at improving the quality of hemodialysis sessions. EMOSTAT automatically imports data from the hemodialysis monitoring software tools and analyzes the delivered treatment looking at six clinically relevant parameters. Failures-to-adhere (FtAs) to the planned treatment are detected and reported to the care-givers. Methods EMOSTAT has been used for more than seven years in the management of a dialysis service located in Mede, Italy. A total of 72 patients were monitored and 21 251 dialyses were collected. Data analysis is performed on the periods 2002–2005 and 2005–2008, corresponding to two different software releases. Results The system had been exploited into everyday clinical practice for the entire considered period. The number of FtAs significantly decreased along the first period: the bulk blood flow FtAs decreased after the introduction of the system. Hemodialysis sessions lasted longer in the second period. Co-occurring FtAs, highlighting the presence of complex FtAs patterns, were also detected. Conclusions EMOSTAT provides an effective way to re-focus the attention of the dialysis department on the treatment plan and on its implementation. The automatic data collection and the design philosophy of EMOSTAT allowed the routine use of the system.

This paper describes a software tool that reconstructs entire genealogies from data collected from different and heterogeneous sources, including municipal and parish records archived over centuries. The tool exploits a record linkage algorithm relying on a rule-based data matching approach. It applies a general strategy for managing the ambiguities due to missing, imprecise or erroneous input data. The process follows an iterative approach that combines automatic pedigree reconstruction with software-empowered human data revision to improve the quality and the accuracy of the results and to optimize the matching rules.The paper discusses the results obtained by reconstructing the entire genealogy of the population of the Val Borbera, a geographically isolated valley in Northern Italy. The genealogy could be reconstructed from data going back as far as the XVI century. The resulting pedigree includes 75,994 trios, 58.9% of which belonging to a unique big family, reconstructed over 13 generations.

Objective:: This paper describes the temporal data mining aspects of a research project that deals with the definition of methods and tools for the assessment of the clinical performance of hemodialysis (HD) services, on the basis of the time series automatically collected during hemodialysis sessions. Methods:: Intelligent data analysis and temporal data mining techniques are applied to gain insight and to discover knowledge on the causes of unsatisfactory clinical results. In particular, two new methods for association rule discovery and temporal rule discovery are applied to the time series. Such methods exploit several pre-processing techniques, comprising data reduction, multi-scale filtering and temporal abstractions. Results:: We have analyzed the data of more than 5800 dialysis sessions coming from 43 different patients monitored for 19 months. The qualitative rules associating the outcome parameters and the measured variables were examined by the domain experts, which were able to distinguish between rules confirming available background knowledge and unexpected but plausible rules. Conclusion:: The new methods proposed in the paper are suitable tools for knowledge discovery in clinical time series. Their use in the context of an auditing system for dialysis management helped clinicians to improve their understanding of the patients' behavior.

In the context of the EU funded Telematic Management of Insulin-Dependent Diabetes Mellitus (T-IDDM) project, we have designed, developed and evaluated a telemedicine system for insulin dependent diabetic patients management. The system relies on the integration of two modules, a Patient Unit (PU) and a Medical Unit (MU), able to communicate over the Internet and the Public Switched Telephone Network. Using the PU, patients are allowed to automatically download their monitoring data from the blood glucose monitoring device, and to send them to the hospital data-base; moreover, they are supported in their every day self monitoring activity. The MU provides physicians with a set of tools for data visualization, data analysis and decision support, and allows them to send messages and/or therapeutic advice to the patients. The T-IDDM service has been evaluated through the application of a formal methodology, and has been used by European patients and physicians for about 18 months. The results obtained during the project demonstration, even if obtained on a pilot study of 12 subjects, show the feasibility of the T-IDDM telemedicine service, and seem to substantiate the hypothesis that the use of the system could present an advantage in the management of insulin dependent diabetic patients, by improving communications and, potentially, clinical outcomes.

This paper describes the application of a method for the intelligent analysis of clinical time series in the diabetes mellitus domain. Such a method is based on temporal abstractions and relies on the following steps: (i) 'pre-processing' of raw data through the application of suitable filtering techniques; (ii) 'extraction' from the pre-processed data of a set of abstract episodes (temporal abstractions); and (iii) 'post-processing' of temporal abstractions; the post-processing phase results in a new set of features that embeds high level information on the patient dynamics. The derived features set is used to obtain new knowledge through the application of machine learning algorithms. The paper describes in detail the application of this methodology and presents some results obtained on simulated data and on a data-set of four diabetic patients monitored for >1 year.

We propose a system for teleconsultation in Insulin Dependent Diabetes Mellitus (IDDM) management, accessible through the use of the net. The system is able to collect monitoring data, to analyze them through a set of tools, and to suggest a therapy adjustment in order to tackle the identified metabolic problems and to fit the patient's needs. The therapy revision has been implemented through the Episodic Skeletal Planning Methodi, it generates an advice and employs it to modify the current therapeutic protocol, presenting to the physician a set of feasible solutions, among which she can choose the new one.

This paper describes a telemedicine system for diabetic patients management, presenting its architecture, the technical solutions adopted and the methodologies on which it is based. The system, designed to provide decision support in a distributed environment, is composed of two modules, a Patient Unit and a Medical Unit, connected by telecommunication services. We outline how the two modules can interact to perform an effective monitoring and a cooperative control of glucose metabolism. In particular, we detail the data analysis tasks performed by the two units and how the results are exploited to assist patients and physicians in revising and adjusting the therapeutic protocol. We will finally describe the current prototypical implementation of the system that uses HTTP as the communication protocol and HTML pages as the graphical user interface.

A computer-based assistant for monitoring a patient's clinical course requires the use of tools able to handle temporal issues. Thus, methodologies coming from two historically distinct worlds need to be combined: the traditional world of Data Base Management Systems (DBMS) and the world of Knowledge-Based Systems (KBS). This paper describes an intelligent system designed to assist the clinical staff in the management of a monitoring protocol for infections in heart transplant recipients. The system consists of a DBMS designed for the management of patient clinical data and of a KBS which is capable of reasoning about the large amount of data and embodied in a temporal model based on time-points and intervals. Moreover, the system aims at providing a synthetic view of a patient's clinical history and some diagnostic and therapeutic suggestions. The KBS retrieves findings stored in the data base and creates a complex taxonomy of objects representing a Temporal Network of important events and episodes noted in the patient history; then, from this temporal representation, it develops its reasoning based on medical knowledge represented using frames and production rules. The system is implemented on a Fourth Generation System tool (4GS) and a KBS shell, both running on an IBM PC AT compatible platform.

A mathematical model of iron metabolism is presented. It comprises the following iron pools within the body: transferrin-bound iron in the plasma.iron in circulating red cells and their bone marrow precursors, iron in mucosal, parenchimal and reticuloendothelial cells. The control exerted by a hormone, called erythropoietin, on bone marrow utilisation of iron for hemoglobin synthesis is taken into account. The model so obtained consists of a system of differential equations of retarded type. Most model parameters can be estimated from radiotracer experiments, others can be measured and numerical values can be assigned to the remaining ones making few reasonable assumptions consistent with the available physiological knowledge. Iron metabolism behavior under different therapeutical treatments was simulated. Model predictions were compared to experimental data collected in clinical routine.