Conceptual graph-based knowledge representation for supporting reasoning in African traditional medicine

Although African patients use both conventional or modern and traditional healthcare simultaneously, it has been proven that 80% of people rely on African traditional medicine (ATM). ATM includes medical activities stemming from practices, customs and traditions which were integral to the distinctive African cultures. It is based mainly on the oral transfer of knowledge, with the risk of losing critical knowledge. Moreover, practices differ according to the regions and the availability of medicinal plants. Therefore, it is necessary to compile tacit, disseminated and complex knowledge from various Tradi-Practitioners (TP) in order to determine interesting patterns for treating a given disease. Knowledge engineering methods for traditional medicine are useful to model suitably complex information needs, formalize knowledge of domain experts and highlight the effective practices for their integration to conventional medicine. The work described in this paper presents an approach which addresses two issues. First it aims at proposing a formal representation model of ATM knowledge and practices to facilitate their sharing and reusing. Then, it aims at providing a visual reasoning mechanism for selecting best available procedures and medicinal plants to treat diseases. The approach is based on the use of the Delphi method for capturing knowledge from various experts which necessitate reaching a consensus. Conceptual graph formalism is used to model ATM knowledge with visual reasoning capabilities and processes. The nested conceptual graphs are used to visually express the semantic meaning of Computational Tree Logic (CTL) constructs that are useful for formal specification of temporal properties of ATM domain knowledge. Our approach presents the advantage of mitigating knowledge loss with conceptual development assistance to improve the quality of ATM care (medical diagnosis and therapeutics), but also patient safety (drug monitoring)

Application of User Profiling on Ontology Module Extraction for Medical portals

One fit all for approach for searching and ranking discovered knowledge on the Internet does not cater for the diverse variety of users and user groups with different preferences, information needs and priorities. This is of a particular case in the National electronic Library of Infection in the UK (NeLI, www.neli.org.uk) accessed by a number of medical professionals with different preferences and medical information needs. We define personal and group profiles, based on user-specified interests, and develop an ontology module extraction service defining the key area of the infection ontology of a particular relevance to each user group. In this paper we discuss how ontology modularisation can improve the NeLI portal by providing customised alert, recommender service and specialitycustomised browsing tree structure

Large scale biomedical texts classification: a kNN and an ESA-based approaches

With the large and increasing volume of textual data, automated methods for identifying significant topics to classify textual documents have received a growing interest. While many efforts have been made in this direction, it still remains a real challenge. Moreover, the issue is even more complex as full texts are not always freely available. Then, using only partial information to annotate these documents is promising but remains a very ambitious issue. MethodsWe propose two classification methods: a k-nearest neighbours (kNN)-based approach and an explicit semantic analysis (ESA)-based approach. Although the kNN-based approach is widely used in text classification, it needs to be improved to perform well in this specific classification problem which deals with partial information. Compared to existing kNN-based methods, our method uses classical Machine Learning (ML) algorithms for ranking the labels. Additional features are also investigated in order to improve the classifiers' performance. In addition, the combination of several learning algorithms with various techniques for fixing the number of relevant topics is performed. On the other hand, ESA seems promising for this classification task as it yielded interesting results in related issues, such as semantic relatedness computation between texts and text classification. Unlike existing works, which use ESA for enriching the bag-of-words approach with additional knowledge-based features, our ESA-based method builds a standalone classifier. Furthermore, we investigate if the results of this method could be useful as a complementary feature of our kNN-based approach.ResultsExperimental evaluations performed on large standard annotated datasets, provided by the BioASQ organizers, show that the kNN-based method with the Random Forest learning algorithm achieves good performances compared with the current state-of-the-art methods, reaching a competitive f-measure of 0.55% while the ESA-based approach surprisingly yielded reserved results.ConclusionsWe have proposed simple classification methods suitable to annotate textual documents using only partial information. They are therefore adequate for large multi-label classification and particularly in the biomedical domain. Thus, our work contributes to the extraction of relevant information from unstructured documents in order to facilitate their automated processing. Consequently, it could be used for various purposes, including document indexing, information retrieval, etc.Comment: Journal of Biomedical Semantics, BioMed Central, 201

A Systematic Review on Improving Health Literacy in Rural Africa Using Mobile Serious Games

Driven by an increase in the availability of cheap low-cost mobile phones and a jump in the number of telecom subscribers, the African gaming world is booming. Most importantly, it has opened an opportunity for rural communities to have an almost identical mobile phone experience than people living in urban areas. It has also opened an opportunity to leverage this high penetration of mobile devices to design mobile-based applications such as mobile serious games. The latter assists individuals living in these communities to modify, change or shape their behaviors and attitudes desirably. This paper reviews mobile serious games in healthcare education, especially those intended to improve health literacy in rural Africa. The challenges and issues encountered in the design and use of persuasive mobile games as a tool can promote behavior change among people living in the rural African communities

A “Futures Literacy” Framework for Understanding the Future of Mobile Health Development in Africa

Sub-Saharan Africa is known to feature some of the weakest healthcare systems in the world. The expanding field of mobile technology in healthcare over the past years, commonly known as mHealth, has been considered to have potential leverage for supporting and improving healthcare systems, especially in the disadvantaged areas, if people are literate enough to autonomously use them. However, implementing new technologies in African healthcare systems has not always considered local realities. Many African’ countries are facing challenges to capitalize on these opportunities. For instance, the lack of planning, foresight, and anticipation may affect the resources available for the implementation of mHealth. This chapter argues that exploring future scenarios can be a key point to successfully designing and implementing Health Literacy Mobile technologies for a sustainable healthcare system in Africa. The UNESCO Futures Literacy (FL) approach can contribute as a valuable foresight tool to anticipate “the future” of mobile health in Africa. Being “future literate” empowers the imagination and enhances the ability of African peoples and countries to prepare and co-invent inclusive health technologies that contribute to achieving both the agenda 2063 of the Africa Union and the UNESCOs 2022-2029 strategy. Overall, FL could become a catalyst to make new technologies tools of “liberation technology” and “justice technology” for Africa

Building an ontology of cardio-vascular diseases for concept-based information retrieval

International audienc

An Alignment-Based Implementation of a Holistic Ontology Integration Method

Despite the intense research activity in the last two decades, ontology integration still presents a number of challenging issues. As ontologies are continuously growing in number, complexity and size and are adopted within open distributed systems such as the Semantic Web, integration becomes a central problem and has to be addressed in a context of increasing scale and heterogeneity. In this paper, we describe a holistic alignment-based method for customized ontology integration. The holistic approach proposes additional challenges as multiple ontologies are jointly integrated at once, in contrast to most common approaches that perform an incremental pairwise ontology integration. By applying consolidated techniques for ontology matching, we investigate the impact on the resulting ontology. The proposed method takes multiple ontologies as well as pairwise alignments and returns a refactored/non-refactored integrated ontology that faithfully preserves the original knowledge of the input ontologies and alignments. We have tested the method on large biomedical ontologies from the LargeBio OAEI track. Results show effectiveness, and overall, a decreased integration cost over multiple ontologies.•OIAR and AROM are two implementations of the proposed method.•OIAR creates a bridge ontology, and AROM creates a fully merged ontology.•The implementation includes the option of ontology refactoring