Redefining fundamental concepts of transcription initiation in bacteria.

Despite enormous progress in understanding the fundamentals of bacterial gene regulation, our knowledge remains limited when compared with the number of bacterial genomes and regulatory systems to be discovered. Derived from a small number of initial studies, classic definitions for concepts of gene regulation have evolved as the number of characterized promoters has increased. Together with discoveries made using new technologies, this knowledge has led to revised generalizations and principles. In this Expert Recommendation, we suggest precise, updated definitions that support a logical, consistent conceptual framework of bacterial gene regulation, focusing on transcription initiation. The resulting concepts can be formalized by ontologies for computational modelling, laying the foundation for improved bioinformatics tools, knowledge-based resources and scientific communication. Thus, this work will help researchers construct better predictive models, with different formalisms, that will be useful in engineering, synthetic biology, microbiology and genetics.

Toward robust clinical genome interpretation: Developing a consistent terminology to characterize Mendelian disease-gene relationships-allelic requirement, inheritance modes, and disease mechanisms.

PURPOSE: The terminology used for gene-disease curation and variant annotation to describe inheritance, allelic requirement, and both sequence and functional consequences of a variant is currently not standardized. There is considerable discrepancy in the literature and across clinical variant reporting in the derivation and application of terms. Here, we standardize the terminology for the characterization of disease-gene relationships to facilitate harmonized global curation and to support variant classification within the ACMG/AMP framework. METHODS: Terminology for inheritance, allelic requirement, and both structural and functional consequences of a variant used by Gene Curation Coalition members and partner organizations was collated and reviewed. Harmonized terminology with definitions and use examples was created, reviewed, and validated. RESULTS: We present a standardized terminology to describe gene-disease relationships, and to support variant annotation. We demonstrate application of the terminology for classification of variation in the ACMG SF 2.0 genes recommended for reporting of secondary findings. Consensus terms were agreed and formalized in both Sequence Ontology (SO) and Human Phenotype Ontology (HPO) ontologies. Gene Curation Coalition member groups intend to use or map to these terms in their respective resources. CONCLUSION: The terminology standardization presented here will improve harmonization, facilitate the pooling of curation datasets across international curation efforts and, in turn, improve consistency in variant classification and genetic test interpretation.

Whole-genome sequencing analysis of suicide deaths integrating brain-regulatory eQTLs data to identify risk loci and genes.

Recent large-scale genome-wide association studies (GWAS) have started to identify potential genetic risk loci associated with risk of suicide; however, a large portion of suicide-associated genetic factors affecting gene expression remain elusive. Dysregulated gene expression, not assessed by GWAS, may play a significant role in increasing the risk of suicide death. We performed the first comprehensive genomic association analysis prioritizing brain expression quantitative trait loci (eQTLs) within regulatory regions in suicide deaths from the Utah Suicide Genetic Risk Study (USGRS). 440,324 brain-regulatory eQTLs were obtained by integrating brain eQTLs, histone modification ChIP-seq, ATAC-seq, DNase-seq, and Hi-C results from publicly available data. Subsequent genomic analyses were conducted in whole-genome sequencing (WGS) data from 986 suicide deaths of non-Finnish European (NFE) ancestry and 415 ancestrally matched controls. Additional independent USGRS suicide deaths with genotyping array data (n = 4657) and controls from the Genome Aggregation Database were explored for WGS result replication. One significant eQTL locus, rs926308 (p = 3.24e-06), was identified. The rs926308-T is associated with lower expression of RFPL3S, a gene important for neocortex development and implicated in arousal. Gene-based analyses performed using Sherlock Bayesian statistical integrative analysis also detected 20 genes with expression changes that may contribute to suicide risk. From analyzing publicly available transcriptomic data, ten of these genes have previous evidence of differential expression in suicide death or in psychiatric disorders that may be associated with suicide, including schizophrenia and autism (ZNF501, ZNF502, CNN3, IGF1R, KLHL36, NBL1, PDCD6IP, SNX19, BCAP29, and ARSA). Electronic health records (EHR) data was further merged to evaluate if there were clinically relevant subsets of suicide deaths associated with genetic variants. In summary, our study identified one risk locus and ten genes associated with suicide risk via gene expression, providing new insight into possible genetic and molecular mechanisms leading to suicide.

Settling the score: variant prioritization and Mendelian disease.

When investigating Mendelian disease using exome or genome sequencing, distinguishing disease-causing genetic variants from the multitude of candidate variants is a complex, multidimensional task. Many prioritization tools and online interpretation resources exist, and professional organizations have offered clinical guidelines for review and return of prioritization results. In this Review, we describe the strengths and weaknesses of widely used computational approaches, explain their roles in the diagnostic and discovery process and discuss how they can inform (and misinform) expert reviewers. We place variant prioritization in the wider context of gene prioritization, burden testing and genotype-phenotype association, and we discuss opportunities and challenges introduced by whole-genome sequencing.

A method for structuring complex clinical knowledge and its representational formalisms to support composite knowledge interoperability in healthcare.

INTRODUCTION: The use and interoperability of clinical knowledge starts with the quality of the formalism utilized to express medical expertise. However, a crucial challenge is that existing formalisms are often suboptimal, lacking the fidelity to represent complex knowledge thoroughly and concisely. Often this leads to difficulties when seeking to unambiguously capture, share, and implement the knowledge for care improvement in clinical information systems used by providers and patients. OBJECTIVES: To provide a systematic method to address some of the complexities of knowledge composition and interoperability related to standards-based representational formalisms of medical knowledge. METHODS: Several cross-industry (Healthcare, Linguistics, System Engineering, Standards Development, and Knowledge Engineering) frameworks were synthesized into a proposed reference knowledge framework. The framework utilizes IEEE 42010, the MetaObject Facility, the Semantic Triangle, an Ontology Framework, and the Domain and Comprehensibility Appropriateness criteria. The steps taken were: 1) identify foundational cross-industry frameworks, 2) select architecture description method, 3) define life cycle viewpoints, 4) define representation and knowledge viewpoints, 5) define relationships between neighboring viewpoints, and 6) establish characteristic definitions of the relationships between components. System engineering principles applied included separation of concerns, cohesion, and loose coupling. RESULTS: A "Multilayer Metamodel for Representation and Knowledge" (M*R/K) reference framework was defined. It provides a standard vocabulary for organizing and articulating medical knowledge curation perspectives, concepts, and relationships across the artifacts created during the life cycle of language creation, authoring medical knowledge, and knowledge implementation in clinical information systems such as electronic health records (EHR). CONCLUSION: M*R/K provides a systematic means to address some of the complexities of knowledge composition and interoperability related to medical knowledge representations used in diverse standards. The framework may be used to guide the development, assessment, and coordinated use of knowledge representation formalisms. M*R/K could promote the alignment and aggregated use of distinct domain-specific languages in composite knowledge artifacts such as clinical practice guidelines (CPGs).

Pain points in parents' interactions with newborn screening systems: a qualitative study.

BACKGROUND & OBJECTIVES: This study aims to explore and elucidate parents' experience of newborn screening [NBS], with the overarching goal of identifying desiderata for the development of informatics-based educational and health management resources. METHODS: We conducted four focus groups and four one-on-one qualitative interviews with a total of 35 participants between March and September 2020. Participants were grouped into three types: parents who had received true positive newborn screening results; parents who had received false positive results; and soon-to-be parents who had no direct experience of the screening process. Interview data were subjected to analysis using an inductive, constant comparison approach. RESULTS: Results are divided into five sections: (1) experiences related to the process of receiving NBS results and prior knowledge of the NBS program; (2) approaches to the management of a child's medical data; (3) sources of additional informational and emotional support; (4) barriers faced by parents navigating the health system; and (5) recommendations and suggestions for new parents experiencing the NBS process. CONCLUSION: Our analysis revealed a wide range of experiences of, and attitudes towards the newborn screening program and the wider newborn screening system. While parents' view of the screening process was - on the whole - positive, some participants reported experiencing substantial frustration, particularly related to how results are initially communicated and difficulties in accessing reliable, timely information. This frustration with current information management and education resources indicates a role for informatics-based approaches in addressing parents' information needs.

Unification of miRNA and isomiR research: the mirGFF3 format and the mirtop API.

MOTIVATION: MicroRNAs (miRNAs) are small RNA molecules (∼22 nucleotide long) involved in post-transcriptional gene regulation. Advances in high-throughput sequencing technologies led to the discovery of isomiRs, which are miRNA sequence variants. While many miRNA-seq analysis tools exist, the diversity of output formats hinders accurate comparisons between tools and precludes data sharing and the development of common downstream analysis methods. RESULTS: To overcome this situation, we present here a community-based project, miRNA Transcriptomic Open Project (miRTOP) working towards the optimization of miRNA analyses. The aim of miRTOP is to promote the development of downstream isomiR analysis tools that are compatible with existing detection and quantification tools. Based on the existing GFF3 format, we first created a new standard format, mirGFF3, for the output of miRNA/isomiR detection and quantification results from small RNA-seq data. Additionally, we developed a command line Python tool, mirtop, to create and manage the mirGFF3 format. Currently, mirtop can convert into mirGFF3 the outputs of commonly used pipelines, such as seqbuster, isomiR-SEA, sRNAbench, Prost! as well as BAM files. Some tools have also incorporated the mirGFF3 format directly into their code, such as, miRge2.0, IsoMIRmap and OptimiR. Its open architecture enables any tool or pipeline to output or convert results into mirGFF3. Collectively, this isomiR categorization system, along with the accompanying mirGFF3 and mirtop API, provide a comprehensive solution for the standardization of miRNA and isomiR annotation, enabling data sharing, reporting, comparative analyses and benchmarking, while promoting the development of common miRNA methods focusing on downstream steps of miRNA detection, annotation and quantification. AVAILABILITY AND IMPLEMENTATION: https://github.com/miRTop/mirGFF3/ and https://github.com/miRTop/mirtop. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Methods and feasibility study for exome sequencing as a universal second-tier test in newborn screening.

PURPOSE: Newborn screening disorders increasingly require genetic variant analysis as part of second-tier or confirmatory testing. Sanger sequencing and gene-specific next-generation sequencing (NGS)-based tests, the current methods of choice, are costly and lack scalability when expanding to new conditions. We describe a scalable, exome sequencing-based NGS pipeline with a priori analysis restriction that can be universally applied to any NBS disorder. METHODS: De-identified abnormal newborn screening specimens representing severe combined immune deficiency (SCID), cystic fibrosis (CF), VLCAD deficiency, metachromatic leukodystrophy (MLD), and in silico sequence read data sets were used to validate the pipeline. To support interpretation and clinical decision-making within the bioinformatics pipeline, variants from multiple databases were curated and validated. RESULTS: CFTR variant panel analysis correctly identified all variants. Concordance compared with diagnostic testing results for targeted gene analysis was between 78.6% and 100%. Validation of the bioinformatics pipeline with in silico data sets revealed a 100% detection rate. Varying degrees of overlap were observed between ClinVar and other databases ranging from 3% to 65%. Data normalization revealed that 11% of variants across the databases required manual curation. CONCLUSION: This pipeline allows for restriction of analysis to variants within a single gene or multiple genes, and can be readily expanded to full exome analysis if clinically indicated and parental consent is granted.

The VAAST Variant Prioritizer (VVP): ultrafast, easy to use whole genome variant prioritization tool.

BACKGROUND: Prioritization of sequence variants for diagnosis and discovery of Mendelian diseases is challenging, especially in large collections of whole genome sequences (WGS). Fast, scalable solutions are needed for discovery research, for clinical applications, and for curation of massive public variant repositories such as dbSNP and gnomAD. In response, we have developed VVP, the VAAST Variant Prioritizer. VVP is ultrafast, scales to even the largest variant repositories and genome collections, and its outputs are designed to simplify clinical interpretation of variants of uncertain significance. RESULTS: We show that scoring the entire contents of dbSNP (> 155 million variants) requires only 95 min using a machine with 4 cpus and 16 GB of RAM, and that a 60X WGS can be processed in less than 5 min. We also demonstrate that VVP can score variants anywhere in the genome, regardless of type, effect, or location. It does so by integrating sequence conservation, the type of sequence change, allele frequencies, variant burden, and zygosity. Finally, we also show that VVP scores are consistently accurate, and easily interpreted, traits not shared by many commonly used tools such as SIFT and CADD. CONCLUSIONS: VVP provides rapid and scalable means to prioritize any sequence variant, anywhere in the genome, and its scores are designed to facilitate variant interpretation using ACMG and NHS guidelines. These traits make it well suited for operation on very large collections of WGS sequences.

ClinVar Miner: Demonstrating utility of a Web-based tool for viewing and filtering ClinVar data.

ClinVar Miner is a Web-based suite that utilizes the data held in the National Center for Biotechnology Information's ClinVar archive. The goal is to render the data more accessible to processes pertaining to conflict resolution of variant interpretation as well as tracking details of data submission and data management for detailed variant curation. Here, we establish the use of these tools to address three separate use cases and to perform analyses across submissions. We demonstrate that the ClinVar Miner tools are an effective means to browse and consolidate data for variant submitters, curation groups, and general oversight. These tools are also relevant to the variant interpretation community in general.

Unique aspects of sequence variant interpretation for inborn errors of metabolism (IEM): The ClinGen IEM Working Group and the Phenylalanine Hydroxylase Gene.

The ClinGen Inborn Errors of Metabolism Working Group was tasked with creating a comprehensive, standardized knowledge base of genes and variants for metabolic diseases. Phenylalanine hydroxylase (PAH) deficiency was chosen to pilot development of the Working Group's standards and guidelines. A PAH variant curation expert panel (VCEP) was created to facilitate this process. Following ACMG-AMP variant interpretation guidelines, we present the development of these standards in the context of PAH variant curation and interpretation. Existing ACMG-AMP rules were adjusted based on disease (6) or strength (5) or both (2). Disease adjustments include allele frequency thresholds, functional assay thresholds, and phenotype-specific guidelines. Our validation of PAH-specific variant interpretation guidelines is presented using 85 variants. The PAH VCEP interpretations were concordant with existing interpretations in ClinVar for 69 variants (81%). Development of biocurator tools and standards are also described. Using the PAH-specific ACMG-AMP guidelines, 714 PAH variants have been curated and will be submitted to ClinVar. We also discuss strategies and challenges in applying ACMG-AMP guidelines to autosomal recessive metabolic disease, and the curation of variants in these genes.

Viral Pathogen Detection by Metagenomics and Pan-Viral Group Polymerase Chain Reaction in Children With Pneumonia Lacking Identifiable Etiology.

Background: Community-acquired pneumonia (CAP) is a leading cause of pediatric hospitalization. Pathogen identification fails in approximately 20% of children but is critical for optimal treatment and prevention of hospital-acquired infections. We used two broad-spectrum detection strategies to identify pathogens in test-negative children with CAP and asymptomatic controls. Methods: Nasopharyngeal/oropharyngeal (NP/OP) swabs from 70 children <5 years with CAP of unknown etiology and 90 asymptomatic controls were tested by next-generation sequencing (RNA-seq) and pan viral group (PVG) PCR for 19 viral families. Association of viruses with CAP was assessed by adjusted odds ratios (aOR) and 95% confidence intervals controlling for season and age group. Results: RNA-seq/PVG PCR detected previously missed, putative pathogens in 34% of patients. Putative viral pathogens included human parainfluenza virus 4 (aOR 9.3, P = .12), human bocavirus (aOR 9.1, P < .01), Coxsackieviruses (aOR 5.1, P = .09), rhinovirus A (aOR 3.5, P = .34), and rhinovirus C (aOR 2.9, P = .57). RNA-seq was more sensitive for RNA viruses whereas PVG PCR detected more DNA viruses. Conclusions: RNA-seq and PVG PCR identified additional viruses, some known to be pathogenic, in NP/OP specimens from one-third of children hospitalized with CAP without a previously identified etiology. Both broad-range methods could be useful tools in future epidemiologic and diagnostic studies.

Human Bocavirus Capsid Messenger RNA Detection in Children With Pneumonia.

Background: The role of human bocavirus (HBoV) in respiratory illness is uncertain. HBoV genomic DNA is frequently detected in both ill and healthy children. We hypothesized that spliced viral capsid messenger RNA (mRNA) produced during active replication might be a better marker for acute infection. Methods: As part of the Etiology of Pneumonia in the Community (EPIC) study, children aged <18 years who were hospitalized with community-acquired pneumonia (CAP) and children asymptomatic at the time of elective outpatient surgery (controls) were enrolled. Nasopharyngeal/oropharyngeal specimens were tested for HBoV mRNA and genomic DNA by quantitative polymerase chain reaction. Results: HBoV DNA was detected in 10.4% of 1295 patients with CAP and 7.5% of 721 controls (odds ratio [OR], 1.4 [95% confidence interval {CI}, 1.0-2.0]); HBoV mRNA was detected in 2.1% and 0.4%, respectively (OR, 5.1 [95% CI, 1.6-26]). When adjusted for age, enrollment month, and detection of other respiratory viruses, HBoV mRNA detection (adjusted OR, 7.6 [95% CI, 1.5-38.4]) but not DNA (adjusted OR, 1.2 [95% CI, .6-2.4]) was associated with CAP. Among children with no other pathogens detected, HBoV mRNA (OR, 9.6 [95% CI, 1.9-82]) was strongly associated with CAP. Conclusions: Detection of HBoV mRNA but not DNA was associated with CAP, supporting a pathogenic role for HBoV in CAP. HBoV mRNA could be a useful target for diagnostic testing.

Phevor combines multiple biomedical ontologies for accurate identification of disease-causing alleles in single individuals and small nuclear families.

Phevor integrates phenotype, gene function, and disease information with personal genomic data for improved power to identify disease-causing alleles. Phevor works by combining knowledge resident in multiple biomedical ontologies with the outputs of variant-prioritization tools. It does so by using an algorithm that propagates information across and between ontologies. This process enables Phevor to accurately reprioritize potentially damaging alleles identified by variant-prioritization tools in light of gene function, disease, and phenotype knowledge. Phevor is especially useful for single-exome and family-trio-based diagnostic analyses, the most commonly occurring clinical scenarios and ones for which existing personal genome diagnostic tools are most inaccurate and underpowered. Here, we present a series of benchmark analyses illustrating Phevor's performance characteristics. Also presented are three recent Utah Genome Project case studies in which Phevor was used to identify disease-causing alleles. Collectively, these results show that Phevor improves diagnostic accuracy not only for individuals presenting with established disease phenotypes but also for those with previously undescribed and atypical disease presentations. Importantly, Phevor is not limited to known diseases or known disease-causing alleles. As we demonstrate, Phevor can also use latent information in ontologies to discover genes and disease-causing alleles not previously associated with disease.

Unbiased Detection of Respiratory Viruses by Use of RNA Sequencing-Based Metagenomics: a Systematic Comparison to a Commercial PCR Panel.

Current infectious disease molecular tests are largely pathogen specific, requiring test selection based on the patient's symptoms. For many syndromes caused by a large number of viral, bacterial, or fungal pathogens, such as respiratory tract infections, this necessitates large panels of tests and has limited yield. In contrast, next-generation sequencing-based metagenomics can be used for unbiased detection of any expected or unexpected pathogen. However, barriers for its diagnostic implementation include incomplete understanding of analytical performance and complexity of sequence data analysis. We compared detection of known respiratory virus-positive (n= 42) and unselected (n= 67) pediatric nasopharyngeal swabs using an RNA sequencing (RNA-seq)-based metagenomics approach and Taxonomer, an ultrarapid, interactive, web-based metagenomics data analysis tool, with an FDA-cleared respiratory virus panel (RVP; GenMark eSensor). Untargeted metagenomics detected 86% of known respiratory virus infections, and additional PCR testing confirmed RVP results for only 2 (33%) of the discordant samples. In unselected samples, untargeted metagenomics had excellent agreement with the RVP (93%). In addition, untargeted metagenomics detected an additional 12 viruses that were either not targeted by the RVP or missed due to highly divergent genome sequences. Normalized viral read counts for untargeted metagenomics correlated with viral burden determined by quantitative PCR and showed high intrarun and interrun reproducibility. Partial or full-length viral genome sequences were generated in 86% of RNA-seq-positive samples, allowing assessment of antiviral resistance, strain-level typing, and phylogenetic relatedness. Overall, untargeted metagenomics had high agreement with a sensitive RVP, detected viruses not targeted by the RVP, and yielded epidemiologically and clinically valuable sequence information.

The genomic CDS sandbox: An assessment among domain experts.

Genomics is a promising tool that is becoming more widely available to improve the care and treatment of individuals. While there is much assertion, genomics will most certainly require the use of clinical decision support (CDS) to be fully realized in the routine clinical setting. The National Human Genome Research Institute (NHGRI) of the National Institutes of Health recently convened an in-person, multi-day meeting on this topic. It was widely recognized that there is a need to promote the innovation and development of resources for genomic CDS such as a CDS sandbox. The purpose of this study was to evaluate a proposed approach for such a genomic CDS sandbox among domain experts and potential users. Survey results indicate a significant interest and desire for a genomic CDS sandbox environment among domain experts. These results will be used to guide the development of a genomic CDS sandbox.

Technical desiderata for the integration of genomic data with clinical decision support.

The ease with which whole genome sequence (WGS) information can be obtained is rapidly approaching the point where it can become useful for routine clinical care. However, significant barriers will inhibit widespread adoption unless clinicians are able to effectively integrate this information into patient care and decision-making. Electronic health records (EHR) and clinical decision support (CDS) systems may play a critical role in this integration. A previously published technical desiderata focused primarily on the integration of genomic data into the EHR. This manuscript extends the previous desiderata by specifically addressing needs related to the integration of genomic information with CDS. The objective of this study is to develop and validate a guiding set of technical desiderata for supporting the clinical use of WGS through CDS. A panel of domain experts in genomics and CDS developed a proposed set of seven additional requirements. These desiderata were reviewed by 63 experts in genomics and CDS through an online survey and refined based on the experts' comments. These additional desiderata provide important guiding principles for the technical development of CDS capabilities for the clinical use of WGS information.

An artificial intelligence approach for investigating multifactorial pain-related features of endometriosis.

Endometriosis is a debilitating, chronic disease that is estimated to affect 11% of reproductive-age women. Diagnosis of endometriosis is difficult with diagnostic delays of up to 12 years reported. These delays can negatively impact health and quality of life. Vague, nonspecific symptoms, like pain, with multiple differential diagnoses contribute to the difficulty of diagnosis. By investigating previously imprecise symptoms of pain, we sought to clarify distinct pain symptoms indicative of endometriosis, using an artificial intelligence-based approach. We used data from 473 women undergoing laparoscopy or laparotomy for a variety of surgical indications. Multiple anatomical pain locations were clustered based on the associations across samples to increase the power in the probability calculations. A Bayesian network was developed using pain-related features, subfertility, and diagnoses. Univariable and multivariable analyses were performed by querying the network for the relative risk of a postoperative diagnosis, given the presence of different symptoms. Performance and sensitivity analyses demonstrated the advantages of Bayesian network analysis over traditional statistical techniques. Clustering grouped the 155 anatomical sites of pain into 15 pain locations. After pruning, the final Bayesian network included 18 nodes. The presence of any pain-related feature increased the relative risk of endometriosis (p-value < 0.001). The constellation of chronic pelvic pain, subfertility, and dyspareunia resulted in the greatest increase in the relative risk of endometriosis. The performance and sensitivity analyses demonstrated that the Bayesian network could identify and analyze more significant associations with endometriosis than traditional statistical techniques. Pelvic pain, frequently associated with endometriosis, is a common and vague symptom. Our Bayesian network for the study of pain-related features of endometriosis revealed specific pain locations and pain types that potentially forecast the diagnosis of endometriosis.

The Business Process Management for Healthcare (BPM+ Health) Consortium: motivation, methodology, and deliverables for enabling clinical knowledge interoperability (CKI).

OBJECTIVES: To enhance the Business Process Management (BPM)+ Healthcare language portfolio by incorporating knowledge types not previously covered and to improve the overall effectiveness and expressiveness of the suite to improve Clinical Knowledge Interoperability. METHODS: We used the BPM+ Health and Object Management Group (OMG) standards development methodology to develop new languages, following a gap analysis between existing BPM+ Health languages and clinical practice guideline knowledge types. Proposal requests were developed based on these requirements, and submission teams were formed to respond to them. The resulting proposals were submitted to OMG for ratification. RESULTS: The BPM+ Health family of languages, which initially consisted of the Business Process Model and Notation, Decision Model and Notation, and Case Model and Notation, was expanded by adding 5 new language standards through the OMG. These include Pedigree and Provenance Model and Notation for expressing epistemic knowledge, Knowledge Package Model and Notation for supporting packaging knowledge, Shared Data Model and Notation for expressing ontic knowledge, Party Model and Notation for representing entities and organizations, and Specification Common Elements, a language providing a standard abstract and reusable library that underpins the 4 new languages. DISCUSSION AND CONCLUSION: In this effort, we adopted a strategy of separation of concerns to promote a portfolio of domain-agnostic, independent, but integrated domain-specific languages for authoring medical knowledge. This strategy is a practical and effective approach to expressing complex medical knowledge. These new domain-specific languages offer various knowledge-type options for clinical knowledge authors to choose from without potentially adding unnecessary overhead or complexity.

Developing an LSTM Model to Identify Surgical Site Infections using Electronic Healthcare Records.

Recently, hospitals and healthcare providers have made efforts to reduce surgical site infections as they are a major cause of surgical complications, a prominent reason for hospital readmission, and associated with significantly increased healthcare costs. Traditional surveillance methods for SSI rely on manual chart review, which can be laborious and costly. To assist the chart review process, we developed a long short-term memory (LSTM) model using structured electronic health record data to identify SSI. The top LSTM model resulted in an average precision (AP) of 0.570 [95% CI 0.567, 0.573] and area under the receiver operating characteristic curve (AUROC) of 0.905 [95% CI 0.904, 0.906] compared to the top traditional machine learning model, a random forest, which achieved 0.552 [95% CI 0.549, 0.555] AP and 0.899 [95% CI 0.898, 0.900] AUROC. Our LSTM model represents a step toward automated surveillance of SSIs, a critical component of quality improvement mechanisms.