Ten Years of Medical Informatics and Standards Support for Clinical Research in an Infectious Diseases Network

BACKGROUND

It is 30 years since evidence-based medicine became a great support for individual clinical expertise in daily practice and scientific research. Electronic systems can be used to achieve the goal of collecting data from heterogeneous datasets and to support multicenter clinical trials. The Ligurian Infectious Diseases Network (LIDN) is a web-based platform for data collection and reuse originating from a regional effort and involving many professionals from different fields.

OBJECTIVES

The objective of this work is to present an integrated system of ad hoc interfaces and tools that we use to perform pseudonymous clinical data collection, both manually and automatically, to support clinical trials.

METHODS

The project comprehends different scenarios of data collection systems, according to the degree of information technology of the involved centers. To be compliant with national regulations, the last developed connection is based on the standard Clinical Document Architecture Release 2 by Health Level 7 guidelines, interoperability is supported by the involvement of a terminology service.

RESULTS

Since 2011, the LIDN platform has involved more than 8,000 patients from eight different hospitals, treated or under treatment for at least one infectious disease among human immunodeficiency virus (HIV), hepatitis C virus, severe acute respiratory syndrome coronavirus 2, and tuberculosis. Since 2013, systems for the automatic transfer of laboratory data have been updating patients' information for three centers, daily. Direct communication was set up between the LIDN architecture and three of the main national cohorts of HIV-infected patients.

CONCLUSION

The LIDN was originally developed to support clinicians involved in the project in the management of data from HIV-infected patients through a web-based tool that could be easily used in primary-care units. Then, the developed system grew modularly to respond to the specific needs that arose over a time span of more than 10 years.

A neuralized feature engineering method for entity relation extraction

Making full use of semantic and structure information in a sentence is critical to support entity relation extraction. Neural networks use stacked neural layers to perform designated feature transformations and can automatically extract high-order abstract feature representations from raw inputs. However, because a sentence usually contains several pairs of named entities, the networks are weak when encoding semantic and structure information of a relation instance. In this paper, we propose a neuralized feature engineering approach for entity relation extraction. This approach enhances the neural network by manually designed features, which have the advantage of using prior knowledge and experience developed in feature-based models. Neuralized feature engineering encodes manually designed features into distributed representations to increase the discriminability of a neural network. Experiments show that this approach considerably improves the performance compared to that of neural networks or feature-based models alone, exceeding state-of-the-art performance by more than 8% and 16.5% in terms of F1-score on the ACE corpus and the Chinese literature text corpus, respectively.

I-Maculaweb: A Tool to Support Data Reuse in Ophthalmology

This paper intends to present a Web-based application to collect and manage clinical data and clinical trials together in a unique tool. I-maculaweb is a user-friendly Web-application designed to manage, share, and analyze clinical data from patients affected by degenerative and vascular diseases of the macula. The unique and innovative scientific and technological elements of this project are the integration with individual and population data, relevant for degenerative and vascular diseases of the macula. Clinical records can also be extracted for statistical purposes and used for clinical decision support systems. I-maculaweb is based on an existing multilevel and multiscale data management model, which includes general principles that are suitable for several different clinical domains. The database structure has been specifically built to respect laterality, a key aspect in ophthalmology. Users can add and manage patient records, follow-up visits, treatment, diagnoses, and clinical history. There are two different modalities to extract records: one for the patient’s own center, in which personal details are shown and the other for statistical purposes, where all center’s anonymized data are visible. The Web-platform allows effective management, sharing, and reuse of information within primary care and clinical research. Clear and precise clinical data will improve understanding of real-life management of degenerative and vascular diseases of the macula as well as increasing precise epidemiologic and statistical data. Furthermore, this Web-based application can be easily employed as an electronic clinical research file in clinical studies.

A Molecular Solution to the Three-Partition Problem

Given a set of numbers, the three-partition problem is to divide them into disjoint triplets that all have the same sum. The problem is NP-complete. This paper presents an algorithm to solve this problem using the biomolecular computing approach. The algorithm uses a distinctive encoding technique that depends on the numbers values which omits the need to an adder to find the sum. The algorithm is explained and an analysis of its complexity in terms of time, the number of strands, number of tubes, and the longest library strand used is presented. A simulation of the algorithm is implemented and tested. This algorithm further proves the ability of molecular computing in solving hard problems.