An Ontology Based Remote Patient Monitoring Framework for Nigerian Healthcare System

Abstract

There is a rapid increase in the demand of healthcare resources in Nigeria mainly healthcare personnel and healthcare facilities. This is due to the prevalence of chronic diseases such as endemic malaria, arthritis, hypertension, diabetes; an upsurge in the rate of avoidable deaths as well as an exponential increase in the population. However, healthcare resources in Nigeria are insufficient. For instance, in recent times, the ratio of doctors to the inhabitants is 1: 4,857. Moreover, more than 60% of the Nigerian populace resides in rural areas where there are extreme shortages of healthcare practitioners and healthcare facilities due to geographical isolation and lack of opportunities. Hence, the low ratio of healthcare practitioners to patients causes a heavy workload on the healthcare practitioners. This however leads to medical errors as healthcare providers work under intense pressure to attend to the medical needs of their patients. This in turns leads to considerable loss of lives. In order to ameliorate this situation, this paper proposes an ontology based framework that will enable healthcare providers in Nigeria to continuously monitor their patients" health remotely outside the settings of the hospital. This will reduce the workload of the healthcare providers, assist them in decision making process as well as reduce the long waiting hours of the patients within the hospital environment. This framework is also designed to tackle the challenge of semantic interoperability facing healthcare systems around the globe.

Comparative Study on the Prediction of Symptomatic and Climatic based Malaria Parasite Counts Using Machine Learning Models

“…The performance of the models were analysed using the performance metrics of accuracy, sensitivity, specificity, false positives and false negatives in equations (3)(4)(5)(6). The result is depicted in Table 3 and Figure 2 showing Artificial Neural Network_Class 0 (ANN_0) with feed forward and back-propagation algorithm produced optimal 48.33% accuracy, 60.61% Sensitivity and 45.58% Specificity, FPR 0.5442% and FNR 0.3939%.…”

“…Malaria parasites can also be transmitted from a mother to her unborn baby (congenitally), by blood transfusions and by sharing needles used to inject drugs [3]. Malaria infection has a vast outbreak especially in tropical regions: an upsurge in the rate of avoidable deaths as well as an exponential increase in the population [4]. In some part of the world, malaria parasites have developed resistance to insecticides and antibiotics [5].…”

Taxonomy Learning from Health Care Social Communities to Improve EHR Implementation

“…We believe that the insufficient involvement of users in the development of standards is a significant cause for the current shortage and the unsatisfying coverage found in domain ontologies [8][20] [21].…”