Aims and Scope

The Open Bioinformatics Journal is an Open Access online journal, which publishes research articles, reviews/mini-reviews, letters, clinical trial and guest edited single topic issues in all areas of bioinformatics and computational biology. The coverage includes biomedicine, focusing on large data acquisition, analysis and curation, computational and statistical methods for the modeling and analysis of biological data, and descriptions of new algorithms and databases.


The Open Bioinformatics Journal, a peer reviewed journal, is an important and reliable source of current information on the developments in the field. The emphasis will be on publishing quality articles rapidly and freely available worldwide.


Recent Articles

The Development of the Vital Signs Tele-monitoring System for the Elderly by Using ‘UML’ Language and the Interoperability Standard ‘Continua’

Sofiene Mansouri

Aims:

We aim to develope a system allowing the telemonitoring of the elderly health status. Medical telemonitoring can make life easy and safe for elderly. The goal of this project is the development of a medical telemonitoring application.

Background:

Medical telemonitoring can make life easy and safe for elderly.

Objective:

The goal of this project is the development of a medical telemonitoring application.

Methods:

In this paper we exposed the different steps of the developing of a medical telemonitoring system designed for the elderly. We studied the medical needs and the system specifications. We used the UML language. Then we detailed the designed system with a total respect to the standard for the interoperability of connected medical equipment, Continua. We presented printed screens of the realized interfaces.

Results:

We realized an application based on web development, more specifically development of a management application for medical telemonitoring.

Conclusion:

In terms of perspectives, we aim to integrate security protocols in the developed system, integrate the data sent from the sensors into an E, H & R (HER) and send the patient file to a H, I & S (HIS).


October 26, 2020
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Editor's Choice

Data Mining Approach to Identify Disease Cohorts from Primary Care Electronic Medical Records: A Case of Diabetes Mellitus

Ebenezer S. Owusu Adjah, Olga Montvida, Julius Agbeve, Sanjoy K. Paul

Background:

Identification of diseased patients from primary care based electronic medical records (EMRs) has methodological challenges that may impact epidemiologic inferences.

Objective:

To compare deterministic clinically guided selection algorithms with probabilistic machine learning (ML) methodologies for their ability to identify patients with type 2 diabetes mellitus (T2DM) from large population based EMRs from nationally representative primary care database.

Methods:

Four cohorts of patients with T2DM were defined by deterministic approach based on disease codes. The database was mined for a set of best predictors of T2DM and the performance of six ML algorithms were compared based on cross-validated true positive rate, true negative rate, and area under receiver operating characteristic curve.

Results:

In the database of 11,018,025 research suitable individuals, 379 657 (3.4%) were coded to have T2DM. Logistic Regression classifier was selected as best ML algorithm and resulted in a cohort of 383,330 patients with potential T2DM. Eighty-three percent (83%) of this cohort had a T2DM code, and 16% of the patients with T2DM code were not included in this ML cohort. Of those in the ML cohort without disease code, 52% had at least one measure of elevated glucose level and 22% had received at least one prescription for antidiabetic medication.

Conclusion:

Deterministic cohort selection based on disease coding potentially introduces significant mis-classification problem. ML techniques allow testing for potential disease predictors, and under meaningful data input, are able to identify diseased cohorts in a holistic way.


December 12, 2017
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