Automatic detection of cases of febrile illness may have potential for early detection of outbreaks of infectious disease either by identification of anomalous numbers of febrile illness or in concert with other information in diagnosing specific syndromes, such as febrile respiratory syndrome. At most institutions, febrile information is contained only in free-text clinical records. We compared the sensitivity and specificity of three fever detection algorithms for detecting fever from free-text. Keyword CC and CoCo classified patients based on triage chief complaints; Keyword HP classified patients based on dictated emergency department reports. Keyword HP was the most sensitive (sensitivity 0.98, specificity 0.89), and Keyword CC was the most specific (sensitivity 0.61, specificity 1.0). Because chief complaints are available sooner than emergency department reports, we suggest a combined application that classifies patients based on their chief complaint followed by classification based on their emergency department report, once the report becomes available.

We consider the task of Medical Concept Normalization (MCN) which aims to map informal medical phrases such as "loosing weight" to formal medical concepts, such as "Weight loss". Deep learning models have shown high performance across various MCN datasets containing small number of target concepts along with adequate number of training examples per concept. However, scaling these models to millions of medical concepts entails the creation of much larger datasets which is cost and effort intensive. Recent works have shown that training MCN models using automatically labeled examples extracted from medical knowledge bases partially alleviates this problem. We extend this idea by computationally creating a distant dataset from patient discussion forums. We extract informal medical phrases and medical concepts from these forums using a synthetically trained classifier and an off-the-shelf medical entity linker respectively. We use pretrained sentence encoding models to find the k-nearest phrases corresponding to each medical concept. These mappings are used in combination with the examples obtained from medical knowledge bases to train an MCN model. Our approach outperforms the previous state-of-the-art by 15.9% and 17.1% classification accuracy across two datasets while avoiding manual labeling.

Post COVID-19 condition (PCC) is known to affect a large proportion of COVID-19 survivors. Robust study design and methods are needed to understand post-COVID-19 diagnosis patterns in all survivors, not just those clinically diagnosed with PCC.

The massive amount of biomedical information published online requires the development of automatic knowledge discovery technologies to effectively make use of this available content. To foster and support this, the research community creates linguistic resources, such as annotated corpora, and designs shared evaluation campaigns and academic competitive challenges. This work describes an ecosystem that facilitates research and development in knowledge discovery in the biomedical domain, specifically in Spanish language. To this end, several resources are developed and shared with the research community, including a novel semantic annotation model, an annotated corpus of 1045 sentences, and computational resources to build and evaluate automatic knowledge discovery techniques. Furthermore, a research task is defined with objective evaluation criteria, and an online evaluation environment is setup and maintained, enabling researchers interested in this task to obtain immediate feedback and compare their results with the state-of-the-art. As a case study, we analyze the results of a competitive challenge based on these resources and provide guidelines for future research. The constructed ecosystem provides an effective learning and evaluation environment to encourage research in knowledge discovery in Spanish biomedical documents.

Understanding the molecular mechanism of COVID-19 pathogenesis helps in the rapid therapeutic target identification. Usually, viral protein targets host proteins in an organized fashion. The expression of any viral gene depends mostly on the host translational machinery. Recent studies report the great significance of codon usage biases in establishing host-viral protein-protein interactions (PPI). Exploring the codon usage patterns between a pair of co-evolved host and viral proteins may present novel insight into the host-viral protein interactomes during disease pathogenesis. Leveraging the similarity in codon usage patterns, we propose a computational scheme to recreate the host-viral protein-protein interaction network. We use host proteins from seventeen (17) essential signaling pathways for our current work towards understanding the possible targeting mechanism of SARS-CoV-2 proteins. We infer both negatively and positively interacting edges in the network. Further, extensive analysis is performed to understand the host PPI network topologically and the attacking behavior of the viral proteins. Our study reveals that viral proteins mostly utilize codons, rare in the targeted host proteins (negatively correlated interaction). Among them, non-structural proteins, NSP3 and structural protein, Spike (S), are the most influential proteins in interacting with multiple host proteins. While ranking the most affected pathways, MAPK pathways observe to be the worst affected during the SARS-CoV-2 infection. Several proteins participating in multiple pathways are highly central in host PPI and mostly targeted by multiple viral proteins. We observe many potential targets (host proteins) from the affected pathways associated with the various drug molecules, including Arsenic trioxide, Dexamethasone, Hydroxychloroquine, Ritonavir, and Interferon beta, which are either under clinical trial or in use during COVID-19.

With the onset of the Coronavirus Disease 2019 (COVID-19) pandemic, there has been a surge in the number of publicly available biomedical information sources, which makes it an increasingly challenging research goal to retrieve a relevant text to a topic of interest. In this paper, we propose a Contextual Query Expansion framework based on the clinical Domain knowledge (CQED) for formalizing an effective search over PubMed to retrieve relevant COVID-19 scholarly articles to a given information need.

Literature-based discovery (LBD) is characterized by uncovering hidden associations in non-interacting scientific literature. Prior approaches to LBD include use of: (1) domain expertise and structured background knowledge to manually filter and explore the literature, (2) distributional statistics and graph-theoretic measures to rank interesting connections, and (3) heuristics to help eliminate spurious connections. However, manual approaches to LBD are not scalable and purely distributional approaches may not be sufficient to obtain insights into the meaning of poorly understood associations. While several graph-based approaches have the potential to elucidate associations, their effectiveness has not been fully demonstrated. A considerable degree of a priori knowledge, heuristics, and manual filtering is still required.

High-dimensional biomedical data are frequently clustered to identify subgroup structures pointing at distinct disease subtypes. It is crucial that the used cluster algorithm works correctly. However, by imposing a predefined shape on the clusters, classical algorithms occasionally suggest a cluster structure in homogenously distributed data or assign data points to incorrect clusters. We analyzed whether this can be avoided by using emergent self-organizing feature maps (ESOM).

The rapid evolution of the COVID-19 pandemic has underscored the need to quickly disseminate the latest clinical knowledge during a public-health emergency. One surprisingly effective platform for healthcare professionals (HCPs) to share knowledge and experiences from the front lines has been social media (for example, the "#medtwitter" community on Twitter). However, identifying clinically-relevant content in social media without manual labeling is a challenge because of the sheer volume of irrelevant data. We present an unsupervised, iterative approach to mine clinically relevant information from social media data, which begins by heuristically filtering for HCP-authored texts and incorporates topic modeling and concept extraction with MetaMap. This approach identifies granular topics and tweets with high clinical relevance from a set of about 52 million COVID-19-related tweets from January to mid-June 2020. We also show that because the technique does not require manual labeling, it can be used to identify emerging topics on a week-to-week basis. Our method can aid in future public-health emergencies by facilitating knowledge transfer among healthcare workers in a rapidly-changing information environment, and by providing an efficient and unsupervised way of highlighting potential areas for clinical research.

The large-scale collection of observational data and digital technologies could help curb the COVID-19 pandemic. However, the coexistence of multiple Common Data Models (CDMs) and the lack of data extract, transform, and load (ETL) tool between different CDMs causes potential interoperability issue between different data systems. The objective of this study is to design, develop, and evaluate an ETL tool that transforms the PCORnet CDM format data into the OMOP CDM.

Develop a novel methodology to create a comprehensive knowledge graph (SuppKG) to represent a domain with limited coverage in the Unified Medical Language System (UMLS), specifically dietary supplement (DS) information for discovering drug-supplement interactions (DSI), by leveraging biomedical natural language processing (NLP) technologies and a DS domain terminology.

Real-time surveillance in the field of health informatics has emerged as a growing domain of interest among worldwide researchers. Evolution in this field has helped in the introduction of various initiatives related to public health informatics. Surveillance systems in the area of health informatics utilizing social media information have been developed for early prediction of disease outbreaks and to monitor diseases. In the past few years, the availability of social media data, particularly Twitter data, enabled real-time syndromic surveillance that provides immediate analysis and instant feedback to those who are charged with follow-ups and investigation of potential outbreaks. In this paper, we review the recent work, trends, and machine learning(ML) text classification approaches used by surveillance systems seeking social media data in the healthcare domain. We also highlight the limitations and challenges followed by possible future directions that can be taken further in this domain.

To discover candidate drugs to repurpose for COVID-19 using literature-derived knowledge and knowledge graph completion methods.

Narrative clinical guidelines often contain assumptions, knowledge gaps, and ambiguities that make translation into an electronic computable format difficult. This can lead to divergence in electronic implementations, reducing the usefulness of collected data outside of that implementation setting. This work set out to evolve guidelines-based data dictionaries by mapping to HL7 Fast Health Interoperability Resources (FHIR) and semantic terminology, thus progressing toward machine-readable guidelines that define the minimum data set required to support family planning and sexually transmitted infections.

Multi-morbidity, the health state of having two or more concurrent chronic conditions, is becoming more common as populations age, but is poorly understood. Identifying and understanding commonly occurring sets of diseases is important to inform clinical decisions to improve patient services and outcomes. Network analysis has been previously used to investigate multi-morbidity, but a classic application only allows for information on binary sets of diseases to contribute to the graph. We propose the use of hypergraphs, which allows for the incorporation of data on people with any number of conditions, and also allows us to obtain a quantitative understanding of the centrality, a measure of how well connected items in the network are to each other, of both single diseases and sets of conditions. Using this framework we illustrate its application with the set of conditions described in the Charlson morbidity index using data extracted from routinely collected population-scale, patient level electronic health records (EHR) for a cohort of adults in Wales, UK. Stroke and diabetes were found to be the most central single conditions. Sets of diseases featuring diabetes; diabetes with Chronic Pulmonary Disease, Renal Disease, Congestive Heart Failure and Cancer were the most central pairs of diseases. We investigated the differences between results obtained from the hypergraph and a classic binary graph and found that the centrality of diseases such as paraplegia, which are connected strongly to a single other disease is exaggerated in binary graphs compared to hypergraphs. The measure of centrality is derived from the weighting metrics calculated for disease sets and further investigation is needed to better understand the effect of the metric used in identifying the clinical significance and ranked centrality of grouped diseases. These initial results indicate that hypergraphs can be used as a valuable tool for analysing previously poorly understood relationships and information available in EHR data.

Biomedical natural language processing tools are increasingly being applied for broad-coverage information extraction-extracting medical information of all types in a scientific document or a clinical note. In such broad-coverage settings, linking mentions of medical concepts to standardized vocabularies requires choosing the best candidate concepts from large inventories covering dozens of types. This study presents a novel semantic type prediction module for biomedical NLP pipelines and two automatically-constructed, large-scale datasets with broad coverage of semantic types.

The COVID-19 crisis led a group of scientific and informatics experts to accelerate development of an infrastructure for electronic data exchange for the identification, processing, and reporting of scientific findings. The Fast Healthcare Interoperability Resources (FHIR®) standard which is overcoming the interoperability problems in health information exchange was extended to evidence-based medicine (EBM) knowledge with the EBMonFHIR project. A 13-step Code System Development Protocol was created in September 2020 to support global development of terminologies for exchange of scientific evidence. For Step 1, we assembled expert working groups with 55 people from 26 countries by October 2020. For Step 2, we identified 23 commonly used tools and systems for which the first version of code systems will be developed. For Step 3, a total of 368 non-redundant concepts were drafted to become display terms for four code systems (Statistic Type, Statistic Model, Study Design, Risk of Bias). Steps 4 through 13 will guide ongoing development and maintenance of these terminologies for scientific exchange. When completed, the code systems will facilitate identifying, processing, and reporting research results and the reliability of those results. More efficient and detailed scientific communication will reduce cost and burden and improve health outcomes, quality of life, and patient, caregiver, and healthcare professional satisfaction. We hope the achievements reached thus far will outlive COVID-19 and provide an infrastructure to make science computable for future generations. Anyone may join the effort at https://www.gps.health/covid19_knowledge_accelerator.html.

The importance of incorporating Natural Language Processing (NLP) methods in clinical informatics research has been increasingly recognized over the past years, and has led to transformative advances. Typically, clinical NLP systems are developed and evaluated on word, sentence, or document level annotations that model specific attributes and features, such as document content (e.g., patient status, or report type), document section types (e.g., current medications, past medical history, or discharge summary), named entities and concepts (e.g., diagnoses, symptoms, or treatments) or semantic attributes (e.g., negation, severity, or temporality). From a clinical perspective, on the other hand, research studies are typically modelled and evaluated on a patient- or population-level, such as predicting how a patient group might respond to specific treatments or patient monitoring over time. While some NLP tasks consider predictions at the individual or group user level, these tasks still constitute a minority. Owing to the discrepancy between scientific objectives of each field, and because of differences in methodological evaluation priorities, there is no clear alignment between these evaluation approaches. Here we provide a broad summary and outline of the challenging issues involved in defining appropriate intrinsic and extrinsic evaluation methods for NLP research that is to be used for clinical outcomes research, and vice versa. A particular focus is placed on mental health research, an area still relatively understudied by the clinical NLP research community, but where NLP methods are of notable relevance. Recent advances in clinical NLP method development have been significant, but we propose more emphasis needs to be placed on rigorous evaluation for the field to advance further. To enable this, we provide actionable suggestions, including a minimal protocol that could be used when reporting clinical NLP method development and its evaluation.

Crimean-Congo hemorrhagic fever (CCHF) is considered one of the major public health concerns with case fatality rates of up to 80%. Currently, there is no effective approved vaccine for CCHF. In this study, we used a computer-aided vaccine design approach to develop the first multi-epitope recombinant vaccine for CCHF. For this purpose, linear B-cell and T-cell binding epitopes from two structural glycoproteins of CCHF virus including Gc and Gn were predicted. The epitopes were further studied regarding their antigenicity, allergenicity, hydrophobicity, stability, toxicity and population coverage. A total number of seven epitopes including five T-cell and two B-cell epitopes were screened for the final vaccine construct. Final vaccine construct composed of 382 amino acid residues which were organized in four domains including linear B-cell, T-cell epitopes and cholera toxin B-subunit (CTxB) along with heat labile enterotoxin IIc B subunit (LT-IIc) as adjuvants. All the segments were joined using appropriate linkers. The physicochemical properties as well as the presence of IFN-γ inducing epitopes in the proposed vaccine, was also checked to determining the vaccine stability, solubility and its ability to induce cell-mediated immune responses. The 3D structure of proposed vaccine was subjected to the prediction of computational B-cell epitopes and molecular docking studies with MHC-I and II molecules. Furthermore, molecular dynamics stimulations were performed to study the vaccine-MHCs complexes stability during stimulation time. The results suggest that our proposed vaccine was stable, well soluble in water and potentially antigenic. Results also demonstrated that the vaccine can induce both humoral and cell-mediated immune responses and could serve as a promising anti-CCHF vaccine candidate.

Clinical trials are essential for generating reliable medical evidence, but often suffer from expensive and delayed patient recruitment because the unstructured eligibility criteria description prevents automatic query generation for eligibility screening. In response to the COVID-19 pandemic, many trials have been created but their information is not computable. We included 700 COVID-19 trials available at the point of study and developed a semi-automatic approach to generate an annotated corpus for COVID-19 clinical trial eligibility criteria called COVIC. A hierarchical annotation schema based on the OMOP Common Data Model was developed to accommodate four levels of annotation granularity: i.e., study cohort, eligibility criteria, named entity and standard concept. In COVIC, 39 trials with more than one study cohorts were identified and labelled with an identifier for each cohort. 1,943 criteria for non-clinical characteristics such as "informed consent", "exclusivity of participation" were annotated. 9767 criteria were represented by 18,161 entities in 8 domains, 7,743 attributes of 7 attribute types and 16,443 relationships of 11 relationship types. 17,171 entities were mapped to standard medical concepts and 1,009 attributes were normalized into computable representations. COVIC can serve as a corpus indexed by semantic tags for COVID-19 trial search and analytics, and a benchmark for machine learning based criteria extraction.