Current status of digital health interventions in the health system in Burkina Faso.

BACKGROUND: Digital health is being used as an accelerator to improve the traditional healthcare system, aiding countries in achieving their sustainable development goals. Burkina Faso aims to harmonize its digital health interventions to guide its digital health strategy for the coming years. The current assessment represents upstream work to steer the development of this strategic plan. METHODS: This was a quantitative, descriptive study conducted between September 2022 and April 2023. It involved a two-part survey: a self-administered questionnaire distributed to healthcare information managers in facilities, and direct interviews conducted with software developers. This was complemented by a documentary review of the country's strategic and standards documents on digital transformation. RESULTS: Burkina Faso possesses a relatively comprehensive collection of governance documents pertaining to digital transformation. The study identified a total of 35 digital health interventions. Analysis showed that 89% of funding originated from technical and financial partners as well as the private sector. While the use of open-source technologies for the development of the applications, software, or platforms used to implement these digital health interventions is well established (77%), there remains a deficiency in the integration of data from different platforms. Furthermore, the classification of digital health interventions revealed an uneven distribution between the different elements across domains: the health system, the classification of digital health interventions (DHI), and the subsystems of the National Health Information System (NHIS). Most digital health intervention projects are still in the pilot phase (66%), with isolated electronic patient record initiatives remaining incomplete. Within the public sector, these records typically take the form of electronic registers or isolated specialty records in a hospital. Within the private sector, tool implementation varies based on expressed needs. Challenges persist in adhering to interoperability norms and standards during tool design, with minimal utilization of the data generated by the implemented tools. CONCLUSION: This study provides an insightful overview of the digital health environment in Burkina Faso and highlights significant challenges regarding intervention strategies. The findings serve as a foundational resource for developing the digital health strategic plan. By addressing the identified shortcomings, this plan will provide a framework for guiding future digital health initiatives effectively.

Advancing drug-target interaction prediction: a comprehensive graph-based approach integrating knowledge graph embedding and ProtBert pretraining.

BACKGROUND: The pharmaceutical field faces a significant challenge in validating drug target interactions (DTIs) due to the time and cost involved, leading to only a fraction being experimentally verified. To expedite drug discovery, accurate computational methods are essential for predicting potential interactions. Recently, machine learning techniques, particularly graph-based methods, have gained prominence. These methods utilize networks of drugs and targets, employing knowledge graph embedding (KGE) to represent structured information from knowledge graphs in a continuous vector space. This phenomenon highlights the growing inclination to utilize graph topologies as a means to improve the precision of predicting DTIs, hence addressing the pressing requirement for effective computational methodologies in the field of drug discovery. RESULTS: The present study presents a novel approach called DTIOG for the prediction of DTIs. The methodology employed in this study involves the utilization of a KGE strategy, together with the incorporation of contextual information obtained from protein sequences. More specifically, the study makes use of Protein Bidirectional Encoder Representations from Transformers (ProtBERT) for this purpose. DTIOG utilizes a two-step process to compute embedding vectors using KGE techniques. Additionally, it employs ProtBERT to determine target-target similarity. Different similarity measures, such as Cosine similarity or Euclidean distance, are utilized in the prediction procedure. In addition to the contextual embedding, the proposed unique approach incorporates local representations obtained from the Simplified Molecular Input Line Entry Specification (SMILES) of drugs and the amino acid sequences of protein targets. CONCLUSIONS: The effectiveness of the proposed approach was assessed through extensive experimentation on datasets pertaining to Enzymes, Ion Channels, and G-protein-coupled Receptors. The remarkable efficacy of DTIOG was showcased through the utilization of diverse similarity measures in order to calculate the similarities between drugs and targets. The combination of these factors, along with the incorporation of various classifiers, enabled the model to outperform existing algorithms in its ability to predict DTIs. The consistent observation of this advantage across all datasets underlines the robustness and accuracy of DTIOG in the domain of DTIs. Additionally, our case study suggests that the DTIOG can serve as a valuable tool for discovering new DTIs.

Health information systems in developing countries: case of African countries.

BACKGROUND: In developing countries, health information system (HIS) is experiencing more and more difficulties to produce quality data. The lack of reliable health related information makes it difficult to develop effective health policies. In order to understand the organization of HIS in African countries, we undertook a literature review. METHODS: Our study was conducted using the PubMed and Scopus bibliographic search engines. The inclusion criteria were: (i) all articles published between 2005 and 2019, (ii) articles including in their title the keywords "health", "information", "systems", "system", "africa", "developing countries", "santé", "pays en développement", "Afrique", (iii) articles that are written in English or French, (iv) which deals with organizational and technical issues about HIS in African countries. RESULTS: Fourteen retrieved articles out of 2492 were included in the study, of which 13 (92.9%) were qualitative. All of them dealt with issues related to HIS in 12 African countries. All 12 countries (100.0%) had opted for a data warehouse approach to improve their HIS. This approach, supported by the DHIS2 system, has enabled providing reliable data. However, 11 out of the 12 countries (92.0%) frameworks were aligned with funding donors' strategies and lacked any national strategy. CONCLUSION: This study suggests that the lack of a national health information management strategy will always be a threat to HIS performance in African countries. Ideally, rigorous upstream thinking to strengthen HIS governance should be undertaken by defining and proposing a coherent conceptual framework to analyze and guide the development and integration of digital applications into HIS over the long term.

État des lieux du système d'information sanitaire du Gabon.

OBJECTIVE: The need for an efficient and reliable health information system motivated Gabon, with the support of the World Bank, to finance a project to reinforce its health information system. An audit of this system was required to report on the reality of the existing system and tools. METHODS: Over the course of two periods, May to September 2016 and April to May 2018, information was collected using both qualitative and quantitative approaches. In this framework, were carried out successively: participative workshops including health actors in Gabon, a survey of health care workers, an analysis of reference documents relating to national health policies, and an analysis of the strengths, weaknesses, opportunities, and threats to the system. RESULTS: In total, 171 health care professionals participated in the different workshops, and 770 others were questioned among 150 health care establishments from 10 health care regions of the country. At the end of this research work, organizational and technical problems were noted at the level of the health information system of Gabon, notably the absence of a judicial framework defining the roles and responsibilities of the different actors of the system, weak data management, a stovepipe information system, several non-interoperable IT applications, and weak completeness rate, at 30%. Among the 770 health care professionals surveyed, 539 (70%) were favorable to a new information system. As for the main assets, we noted the existence of computing equipment and acceptable internet coverage, 31.5% of the health care establishments are connected via cable. CONCLUSION: This research sheds light on the existing health information system and should enable the implementation of a new system.

Visualizing omics and clinical data: Which challenges for dealing with their variety?

Life sciences are currently going through a great number of transformations raised by the in-going revolution in high-throughput technologies for the acquisition of data. The integration of their high dimensionality, ranging from omics to clinical data, is becoming one of the most challenging stages. It involves inter-disciplinary developments with the aim to move towards an enhanced understanding of human physiology for caring purposes. Biologists, bioinformaticians, physicians and other experts related to the healthcare domain have to accompany each step of the analysis process in order to investigate and expertise these various data. In this perspective, methods related to information visualization are gaining increasing attention within life sciences. The softwares based on these methods are now well recognized to facilitate expert users' success in carrying out their data analysis tasks. This article aims at reviewing the current methods and techniques dedicated to information visualisation and their current use in software development related to omics or/and clinical data.

Reuse of termino-ontological resources and text corpora for building a multilingual domain ontology: an application to Alzheimer's disease.

Ontologies are useful tools for sharing and exchanging knowledge. However ontology construction is complex and often time consuming. In this paper, we present a method for building a bilingual domain ontology from textual and termino-ontological resources intended for semantic annotation and information retrieval of textual documents. This method combines two approaches: ontology learning from texts and the reuse of existing terminological resources. It consists of four steps: (i) term extraction from domain specific corpora (in French and English) using textual analysis tools, (ii) clustering of terms into concepts organized according to the UMLS Metathesaurus, (iii) ontology enrichment through the alignment of French and English terms using parallel corpora and the integration of new concepts, (iv) refinement and validation of results by domain experts. These validated results are formalized into a domain ontology dedicated to Alzheimer's disease and related syndromes which is available online (http://lesim.isped.u-bordeaux2.fr/SemBiP/ressources/ontoAD.owl). The latter currently includes 5765 concepts linked by 7499 taxonomic relationships and 10,889 non-taxonomic relationships. Among these results, 439 concepts absent from the UMLS were created and 608 new synonymous French terms were added. The proposed method is sufficiently flexible to be applied to other domains.

Repurposing non-pharmacological interventions for Alzheimer's disease through link prediction on biomedical literature.

Non-pharmaceutical interventions (NPI) have great potential to improve cognitive function but limited investigation to discover NPI repurposing for Alzheimer's Disease (AD). This is the first study to develop an innovative framework to extract and represent NPI information from biomedical literature in a knowledge graph (KG), and train link prediction models to repurpose novel NPIs for AD prevention. We constructed a comprehensive KG, called ADInt, by extracting NPI information from biomedical literature. We used the previously-created SuppKG and NPI lexicon to identify NPI entities. Four KG embedding models (i.e., TransE, RotatE, DistMult and ComplEX) and two novel graph convolutional network models (i.e., R-GCN and CompGCN) were trained and compared to learn the representation of ADInt. Models were evaluated and compared on two test sets (time slice and clinical trial ground truth) and the best performing model was used to predict novel NPIs for AD. Discovery patterns were applied to generate mechanistic pathways for high scoring candidates. The ADInt has 162,212 nodes and 1,017,284 edges. R-GCN performed best in time slice (MR = 5.2054, Hits@10 = 0.8496) and clinical trial ground truth (MR = 3.4996, Hits@10 = 0.9192) test sets. After evaluation by domain experts, 10 novel dietary supplements and 10 complementary and integrative health were proposed from the score table calculated by R-GCN. Among proposed novel NPIs, we found plausible mechanistic pathways for photodynamic therapy and Choerospondias axillaris to prevent AD, and validated psychotherapy and manual therapy techniques using real-world data analysis. The proposed framework shows potential for discovering new NPIs for AD prevention and understanding their mechanistic pathways.

Automatic filtering and substantiation of drug safety signals.

Drug safety issues pose serious health threats to the population and constitute a major cause of mortality worldwide. Due to the prominent implications to both public health and the pharmaceutical industry, it is of great importance to unravel the molecular mechanisms by which an adverse drug reaction can be potentially elicited. These mechanisms can be investigated by placing the pharmaco-epidemiologically detected adverse drug reaction in an information-rich context and by exploiting all currently available biomedical knowledge to substantiate it. We present a computational framework for the biological annotation of potential adverse drug reactions. First, the proposed framework investigates previous evidences on the drug-event association in the context of biomedical literature (signal filtering). Then, it seeks to provide a biological explanation (signal substantiation) by exploring mechanistic connections that might explain why a drug produces a specific adverse reaction. The mechanistic connections include the activity of the drug, related compounds and drug metabolites on protein targets, the association of protein targets to clinical events, and the annotation of proteins (both protein targets and proteins associated with clinical events) to biological pathways. Hence, the workflows for signal filtering and substantiation integrate modules for literature and database mining, in silico drug-target profiling, and analyses based on gene-disease networks and biological pathways. Application examples of these workflows carried out on selected cases of drug safety signals are discussed. The methodology and workflows presented offer a novel approach to explore the molecular mechanisms underlying adverse drug reactions.

The EU-ADR Web Platform: delivering advanced pharmacovigilance tools.

PURPOSE: Pharmacovigilance methods have advanced greatly during the last decades, making post-market drug assessment an essential drug evaluation component. These methods mainly rely on the use of spontaneous reporting systems and health information databases to collect expertise from huge amounts of real-world reports. The EU-ADR Web Platform was built to further facilitate accessing, monitoring and exploring these data, enabling an in-depth analysis of adverse drug reactions risks. METHODS: The EU-ADR Web Platform exploits the wealth of data collected within a large-scale European initiative, the EU-ADR project. Millions of electronic health records, provided by national health agencies, are mined for specific drug events, which are correlated with literature, protein and pathway data, resulting in a rich drug-event dataset. Next, advanced distributed computing methods are tailored to coordinate the execution of data-mining and statistical analysis tasks. This permits obtaining a ranked drug-event list, removing spurious entries and highlighting relationships with high risk potential. RESULTS: The EU-ADR Web Platform is an open workspace for the integrated analysis of pharmacovigilance datasets. Using this software, researchers can access a variety of tools provided by distinct partners in a single centralized environment. Besides performing standalone drug-event assessments, they can also control the pipeline for an improved batch analysis of custom datasets. Drug-event pairs can be substantiated and statistically analysed within the platform's innovative working environment. CONCLUSIONS: A pioneering workspace that helps in explaining the biological path of adverse drug reactions was developed within the EU-ADR project consortium. This tool, targeted at the pharmacovigilance community, is available online at https://bioinformatics.ua.pt/euadr/.

Broad Trends in Public Health and Epidemiology Informatics.

OBJECTIVES: The objective of this study is to highlight innovative research and contemporary trends in the area of Public Health and Epidemiology Informatics (PHEI). METHODS: Following a similar approach to last year's edition, a meticulous search was conducted on PubMed (with keywords including topics related to Public Health, Epidemiological Surveillance and Medical Informatics), examining a total of 2,022 scientific publications on Public Health and Epidemiology Informatics (PHEI). The resulting references were thoroughly examined by the three section editors. Subsequently, 10 papers were chosen as potential candidates for the best paper award. These selected papers were then subjected to peer-review by six external reviewers, in addition to the section editors and two chief editors of the IMIA yearbook of medical informatics. Each paper underwent a total of five reviews. RESULTS: Out of the 539 references retrieved from PubMed, only two were deemed worthy of the best paper award, although four papers had the potential to qualify in total. The first best paper by pertains to a study about the need for a new annotation framework due to inadequacies in existing methods and resources. The second paper elucidates the use of Weibo data to monitor the health of Chinese urbanites. The correlation between air pollution and health sensing was measured via generalized additive models. CONCLUSIONS: One of the primary findings of this edition is the dearth of studies identified for the PHEI section, which represents a significant decline compared to the previous edition. This is particularly surprising given that the post-COVID period should have led to an increased use of information and communication technology for public health issues.

The OREGANO knowledge graph for computational drug repurposing.

Drug repositioning is a faster and more affordable solution than traditional drug discovery approaches. From this perspective, computational drug repositioning using knowledge graphs is a very promising direction. Knowledge graphs constructed from drug data and information can be used to generate hypotheses (molecule/drug - target links) through link prediction using machine learning algorithms. However, it remains rare to have a holistically constructed knowledge graph using the broadest possible features and drug characteristics, which is freely available to the community. The OREGANO knowledge graph aims at filling this gap. The purpose of this paper is to present the OREGANO knowledge graph, which includes natural compounds related data. The graph was developed from scratch by retrieving data directly from the knowledge sources to be integrated. We therefore designed the expected graph model and proposed a method for merging nodes between the different knowledge sources, and finally, the data were cleaned. The knowledge graph, as well as the source codes for the ETL process, are openly available on the GitHub of the OREGANO project ( https://gitub.u-bordeaux.fr/erias/oregano ).

Repurposing Non-pharmacological Interventions for Alzheimer's Diseases through Link Prediction on Biomedical Literature.

Recently, computational drug repurposing has emerged as a promising method for identifying new pharmaceutical interventions (PI) for Alzheimer's Disease (AD). Non-pharmaceutical interventions (NPI), such as Vitamin E and Music therapy, have great potential to improve cognitive function and slow the progression of AD, but have largely been unexplored. This study predicts novel NPIs for AD through link prediction on our developed biomedical knowledge graph. We constructed a comprehensive knowledge graph containing AD concepts and various potential interventions, called ADInt, by integrating a dietary supplement domain knowledge graph, SuppKG, with semantic relations from SemMedDB database. Four knowledge graph embedding models (TransE, RotatE, DistMult and ComplEX) and two graph convolutional network models (R-GCN and CompGCN) were compared to learn the representation of ADInt. R-GCN outperformed other models by evaluating on the time slice test set and the clinical trial test set and was used to generate the score tables of the link prediction task. Discovery patterns were applied to generate mechanism pathways for high scoring triples. Our ADInt had 162,213 nodes and 1,017,319 edges. The graph convolutional network model, R-GCN, performed best in both the Time Slicing test set (MR = 7.099, MRR = 0.5007, Hits@1 = 0.4112, Hits@3 = 0.5058, Hits@10 = 0.6804) and the Clinical Trials test set (MR = 1.731, MRR = 0.8582, Hits@1 = 0.7906, Hits@3 = 0.9033, Hits@10 = 0.9848). Among high scoring triples in the link prediction results, we found the plausible mechanism pathways of (Photodynamic therapy, PREVENTS, Alzheimer's Disease) and (Choerospondias axillaris, PREVENTS, Alzheimer's Disease) by discovery patterns and discussed them further. In conclusion, we presented a novel methodology to extend an existing knowledge graph and discover NPIs (dietary supplements (DS) and complementary and integrative health (CIH)) for AD. We used discovery patterns to find mechanisms for predicted triples to solve the poor interpretability of artificial neural networks. Our method can potentially be applied to other clinical problems, such as discovering drug adverse reactions and drug-drug interactions.

Towards a bilingual Alzheimer's disease terminology acquisition using a parallel corpus.

We present in this paper a method for acquiring a bilingual terminology concerning the Alzheimer's disease using a parallel corpus. NLP techniques are used for parsing English and French texts in order to extract candidate terms. These terms are then matched automatically using an approach that combines two alignment techniques: one based on the calculation of an association score between two terms, and another technique based on the calculation of morphological similarity. This method provided good results on an Alzheimer's disease related corpus with a precision of 73%.

RAVEL: retrieval and visualization in ELectronic health records.

Because of the ever-increasing amount of information in patients' EHRs, healthcare professionals may face difficulties for making diagnoses and/or therapeutic decisions. Moreover, patients may misunderstand their health status. These medical practitioners need effective tools to locate in real time relevant elements within the patients' EHR and visualize them according to synthetic and intuitive presentation models. The RAVEL project aims at achieving this goal by performing a high profile industrial research and development program on the EHR considering the following areas: (i) semantic indexing, (ii) information retrieval, and (iii) data visualization. The RAVEL project is expected to implement a generic, loosely coupled to data sources prototype so that it can be transposed into different university hospitals information systems.

Novelty in Public Health and Epidemiology Informatics.

OBJECTIVES: To highlight novelty studies and current trends in Public Health and Epidemiology Informatics (PHEI). METHODS: Similar to last year's edition, a PubMed search of 2021 scientific publications on PHEI has been conducted. The resulting references were reviewed by the two section editors. Then, 11 candidate best papers were selected from the initial 782 references. These papers were then peer-reviewed by selected external reviewers. They included at least two senior researchers, to allow the Editorial Committee of the 2022 IMIA Yearbook edition to make an informed decision for selecting the best papers of the PHEI section. RESULTS: Among the 782 references retrieved from PubMed, two were selected as the best papers. The first best paper reports a study which performed a comprehensive comparison of traditional statistical approaches (e.g., Cox Proportional Hazards models) vs. machine learning techniques in a large, real-world dataset for predicting breast cancer survival, with a focus on explainability. The second paper describes the engineering of deep learning models to establish associations between ocular features and major hepatobiliary diseases and to advance automated screening and identification of hepatobiliary diseases from ocular images. CONCLUSION: Overall, from this year edition, we observed that the number of studies related to PHEI has decreased. The findings of the two studies selected as best papers on the topic suggest that a significant effort is still being made by the community to compare traditional learning methods with deep learning methods. Using multimodality datasets (images, texts) could improve approaches for tackling public health issues.

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.

REDIRECT: Mapping Drug Prescriptions and Evidence from Biomedical Literature.

To enhance their practice, healthcare professionals need to cross-link various usage recommendations provided by heterogeneous vocabularies that must be retrieved and integrated conjointly. This is the aim of the Knowledge Warehouse / K-Ware platform. It enables establishing relevant bridges between different knowledge sources (structured vocabularies, thesaurus, ontologies) expressed in the semantic web standard languages (i.e. SKOS, OWL, RDF). This poster presents the strategy applied in K-Ware to hide the different aspects of linking literals with medical entities encoded in these knowledge sources to fetch some publications abstracts from Pubmed.

Using serious mobile games to improve health literacy in rural Sub-Saharan Africa: A literature review.

The African gaming industry is beginning to flourish as a result of a rise in the availability of inexpensive phones and the number of mobile phone subscribers. It has enabled the development and implementation of mobile serious games to promote healthy behavior change in rural communities. This paper examines the use of mobile serious games in healthcare education, with a particular focus on those designed to increase health literacy in rural Africa. Identifying and addressing the design challenges and issues faced by people living in rural African communities through the use of persuasive mobile games can promote behavior change among these underserved communities. We used PubMed, Scopus, Google Scholar and manual search to identify relevant studies published from 2011 to July 2021. The literature review highlights how the identified challenges affect the implementation of persuasive strategies, suggests design solutions for overcoming them, and discusses how persuasive games can be tailored to suit the target rural African populations. Some of the identified challenges are technical in nature (e.g., access to electricity and internet connectivity), while others are not (e.g., language diversity and low literacy). As the number of serious games for healthcare education and awareness continues to increase, it is essential for the successful implementation of inclusive mobile health technologies in rural Africa to identify and address the specific challenges faced by underserved populations such as rural African communities.

From Real-world Individuals' Data to National Health Indicators: Multiphase Pilot Study in Gabon.

BACKGROUND: Achieving health goals requires informed decision-making supported by transparent, reliable, and relevant health information. This helps decision makers, such as health managers, to better understand the functioning of their health system and improve their ability to respond quickly to health demands. To achieve this, the health system needs to be supported by a digitized decision-making information system. In Sub-Saharan African countries, inadequate digital infrastructure, including limited internet connectivity and insufficient access to appropriate computer software, makes it difficult to collect, process, and analyze data for health statistics. The processing of data is done manually in this case; however, this situation affects the quality of the health statistics produced and compromises the quality of health intervention choices in these countries. OBJECTIVE: This study aimed to describe the conceptual approach of a data production and dissemination platform model proposed and implemented in Gabon. More precisely, it aimed to present the approach applied for the multidimensional analysis of the data production and dissemination process in the existing information system and present the results of an evaluation of the proposed model implemented in a real context. METHODS: The research was carried out in 3 phases. First, a platform was designed and developed based on the examination of the various data production and indicator generation procedures. Then, the platform was implemented in chosen health facilities in Gabon. Finally, a platform evaluation was carried out with actual end users. RESULTS: A total of 14 users with 12 years of average experience in health data management were interviewed. The results show that the use of the proposed model significantly improved the completeness, timeliness, and accuracy of data compared with the traditional system (93% vs 12%, P<.001; 96% vs 18%, P<.001; and 100% vs 18%, P<.001; respectively). CONCLUSIONS: The proposed model contributes significantly to the improvement of health data quality in Gabon.

Interactive Voice Response Service to Improve High School Students Covid-19 Literacy in Burkina Faso: A Usability Study.

Mobile technology is widely used in healthcare. However, designers and developers in many cases have focused on developing solutions that are often tailored to highly literate people. While the advent of the pandemic has called for people to seek and use Covid-19 related information to adapt their behaviors, it is relatively difficult for low literate to get easily access to health information through digital technologies. In this study, we present a Mobile based Interactive Voice Response service designed particularly for low-literate people which provides validated Covid-19 related health information in local African languages. We conducted a field study, among high school students, through a usability study to assess users' perception. The service received an excellent numerical usability score of 78.75.