Citations

Abstract Objective Social determinants of health (SDoH) play critical roles in health outcomes and well-being. Understanding the interplay of SDoH and health outcomes is critical to reducing healthcare inequalities and transforming a “sick care” system into a “health-promoting” system. To address the SDOH terminology gap and better embed relevant elements in advanced biomedical informatics, we propose an SDoH ontology (SDoHO), which represents fundamental SDoH factors and their relationships in a standardized and measurable way. Material and Methods Drawing on the content of existing ontologies relevant to certain aspects of SDoH, we used a top-down approach to formally model classes, relationships, and constraints based on multiple SDoH-related resources. Expert review and coverage evaluation, using a bottom-up approach employing clinical notes data and a national survey, were performed. Results We constructed the SDoHO with 708 classes, 106 object properties, and 20 data properties, with 1,561 logical axioms and 976 declaration axioms in the current version. Three experts achieved 0.967 agreement in the semantic evaluation of the ontology. A comparison between the coverage of the ontology and SDOH concepts in 2 sets of clinical notes and a national survey instrument also showed satisfactory results. Discussion SDoHO could potentially play an essential role in providing a foundation for a comprehensive understanding of the associations between SDoH and health outcomes and paving the way for health equity across populations. Conclusion SDoHO has well-designed hierarchies, practical objective properties, and versatile functionalities, and the comprehensive semantic and coverage evaluation achieved promising performance compared to the existing ontologies relevant to SDoH.

Drug-resistant Gram-positive bacterial infections are still a substantial burden on the public health system, with two bacteria ( Staphylococcus aureus and Streptococcus pneumoniae ) accounting for over 1.5 million drug-resistant infections in the United States alone in 2017. In 2019, 250,000 deaths were attributed to these pathogens globally. We have developed a preclinical glycopeptide antibiotic, MCC5145, that has excellent potency (MIC 90 ≤ 0.06 μg/ml) against hundreds of isolates of methicillin-resistant S. aureus (MRSA) and other Gram-positive bacteria, with a greater than 1000-fold margin over mammalian cell cytotoxicity values. The antibiotic has therapeutic in vivo efficacy when dosed subcutaneously in multiple murine models of established bacterial infections, including thigh infection with MRSA and blood septicemia with S. pneumoniae , as well as when dosed orally in an antibiotic-induced Clostridioides difficile infection model. MCC5145 exhibited reduced nephrotoxicity at microbiologically active doses in mice compared to vancomycin. MCC5145 also showed improved activity against biofilms compared to vancomycin, both in vitro and in vivo, and a low propensity to select for drug resistance. Characterization of drug action using a transposon library bioinformatic platform showed a mechanistic distinction from other glycopeptide antibiotics.

AbstractIntroductionSlower mobility is associated with mild cognitive impairment (MCI) and dementia. We examined the interaction of endurance with gait speed on prevalent MCI and dementia.MethodsCross‐sectional multinomial regression in the ARIC cohort (n = 2844 participants; 71 to 94 years; 44% men; 18% Black persons) with cognitive status (normal/MCI/dementia), 4 m gait speed, and endurance (2 minute walk [2MW]).ResultsFaster gait speed (up to but not above 1 m/s) and better 2MW were separately associated with lower dementia risk. Good performance in both (2MW = 200 m, gait speed = 1.2 m/s) was associated with 99% lower dementia (Relative Prevalence Ratio [RPR] = 0.01 [95% CI: 0.0 to 0.06]) and 73% lower MCI, RPR = 0.27 (0.15 to 0.48) compared to poor performance in both (2MW = 100 m, gait speed = 0.8 m/s). Models incorporating a gait speed‐by‐2MW interaction term outperformed gait speed‐only models (P < .001).DiscussionGait speed relationships with dementia diminish at faster gait speeds. Combining endurance with gait speed may yield more sensitive markers of MCI and dementia than gait speed alone.

Abstract Objective : Developing clinical natural language processing systems often requires access to many clinical documents, which are not widely available to the public due to privacy and security concerns. To address this challenge, we propose to develop methods to generate synthetic clinical notes and evaluate their utility in real clinical natural language processing tasks. Materials and Methods : We implemented 4 state-of-the-art text generation models, namely CharRNN, SegGAN, GPT-2, and CTRL, to generate clinical text for the History and Present Illness section. We then manually annotated clinical entities for randomly selected 500 History and Present Illness notes generated from the best-performing algorithm. To compare the utility of natural and synthetic corpora, we trained named entity recognition (NER) models from all 3 corpora and evaluated their performance on 2 independent natural corpora. Results : Our evaluation shows GPT-2 achieved the best BLEU (bilingual evaluation understudy) score (with a BLEU-2 of 0.92). NER models trained on synthetic corpus generated by GPT-2 showed slightly better performance on 2 independent corpora: strict F1 scores of 0.709 and 0.748, respectively, when compared with the NER models trained on natural corpus (F1 scores of 0.706 and 0.737, respectively), indicating the good utility of synthetic corpora in clinical NER model development. In addition, we also demonstrated that an augmented method that combines both natural and synthetic corpora achieved better performance than that uses the natural corpus only. Conclusions : Recent advances in text generation have made it possible to generate synthetic clinical notes that could be useful for training NER models for information extraction from natural clinical notes, thus lowering the privacy concern and increasing data availability. Further investigation is needed to apply this technology to practice.

AbstractLarge observational data networks that leverage routine clinical practice data in electronic health records (EHRs) are critical resources for research on coronavirus disease 2019 (COVID-19). Data normalization is a key challenge for the secondary use of EHRs for COVID-19 research across institutions. In this study, we addressed the challenge of automating the normalization of COVID-19 diagnostic tests, which are critical data elements, but for which controlled terminology terms were published after clinical implementation. We developed a simple but effective rule-based tool called COVID-19 TestNorm to automatically normalize local COVID-19 testing names to standard LOINC (Logical Observation Identifiers Names and Codes) codes. COVID-19 TestNorm was developed and evaluated using 568 test names collected from 8 healthcare systems. Our results show that it could achieve an accuracy of 97.4% on an independent test set. COVID-19 TestNorm is available as an open-source package for developers and as an online Web application for end users (https://clamp.uth.edu/covid/loinc.php). We believe that it will be a useful tool to support secondary use of EHRs for research on COVID-19.