Two complementary AI approaches for predicting UMLS semantic group assignment: heuristic reasoning and deep learning.

OBJECTIVE: Use heuristic, deep learning (DL), and hybrid AI methods to predict semantic group (SG) assignments for new UMLS Metathesaurus atoms, with target accuracy ≥95%. MATERIALS AND METHODS: We used train-test datasets from successive 2020AA-2022AB UMLS Metathesaurus releases. Our heuristic "waterfall" approach employed a sequence of 7 different SG prediction methods. Atoms not qualifying for a method were passed on to the next method. The DL approach generated BioWordVec and SapBERT embeddings for atom names, BioWordVec embeddings for source vocabulary names, and BioWordVec embeddings for atom names of the second-to-top nodes of an atom's source hierarchy. We fed a concatenation of the 4 embeddings into a fully connected multilayer neural network with an output layer of 15 nodes (one for each SG). For both approaches, we developed methods to estimate the probability that their predicted SG for an atom would be correct. Based on these estimations, we developed 2 hybrid SG prediction methods combining the strengths of heuristic and DL methods. RESULTS: The heuristic waterfall approach accurately predicted 94.3% of SGs for 1 563 692 new unseen atoms. The DL accuracy on the same dataset was also 94.3%. The hybrid approaches achieved an average accuracy of 96.5%. CONCLUSION: Our study demonstrated that AI methods can predict SG assignments for new UMLS atoms with sufficient accuracy to be potentially useful as an intermediate step in the time-consuming task of assigning new atoms to UMLS concepts. We showed that for SG prediction, combining heuristic methods and DL methods can produce better results than either alone.

Do electronic health record systems "dumb down" clinicians?

A panel sponsored by the American College of Medical Informatics (ACMI) at the 2021 AMIA Symposium addressed the provocative question: "Are Electronic Health Records dumbing down clinicians?" After reviewing electronic health record (EHR) development and evolution, the panel discussed how EHR use can impair care delivery. Both suboptimal functionality during EHR use and longer-term effects outside of EHR use can reduce clinicians' efficiencies, reasoning abilities, and knowledge. Panel members explored potential solutions to problems discussed. Progress will require significant engagement from clinician-users, educators, health systems, commercial vendors, regulators, and policy makers. Future EHR systems must become more user-focused and scalable and enable providers to work smarter to deliver improved care.

Don Lindberg's influence on future generations: The U.S. National Library of Medicine's biomedical informatics research training programs.

Through his visionary leadership as Director of the U.S. National Library of Medicine (NLM), Donald A. B. Lindberg M.D. influenced future generations of informatics professionals and the field of biomedical informatics itself. This chapter describes Dr. Lindberg's role in sponsoring and shaping the NLM's Institutional T15 training programs.

Donald A.B. Lindberg and the U.S. National Library of Medicine transformed biomedical and health informatics.

This overview summary of the Informatics Section of the book Transforming biomedical informatics and health information access: Don Lindberg and the U.S. National Library of Medicine illustrates how the NLM revolutionized the field of biomedical and health informatics during Lindberg's term as NLM Director. Authors present a before-and-after perspective of what changed, how it changed, and the impact of those changes.

Donald A.B. Lindberg and the U.S. National Library of Medicine Transformed Biomedical and Health Informatics.

This overview summary of the Informatics Section of the book Transforming biomedical informatics and health information access: Don Lindberg and the U.S. National Library of Medicine illustrates how the NLM revolutionized the field of biomedical and health informatics during Lindberg's term as NLM Director. Authors present a before-and-after perspective of what changed, how it changed, and the impact of those changes.

Don Lindberg's Influence on Future Generations: The U.S. National Library of Medicine's Biomedical Informatics Research Training Programs.

Through his visionary leadership as Director of the U.S. National Library of Medicine (NLM), Donald A.B. Lindberg M.D. influenced future generations of informatics professionals and the field of biomedical informatics itself. This chapter describes Dr. Lindberg's role in sponsoring and shaping the NLM's Institutional T15 training programs.

The roles of the US National Library of Medicine and Donald A.B. Lindberg in revolutionizing biomedical and health informatics.

Over a 31-year span as Director of the US National Library of Medicine (NLM), Donald A.B. Lindberg, MD, and his extraordinary NLM colleagues fundamentally changed the field of biomedical and health informatics-with a resulting impact on biomedicine that is much broader than its influence on any single subfield. This article provides substance to bolster that claim. The review is based in part on the informatics section of a new book, "Transforming biomedical informatics and health information access: Don Lindberg and the US National Library of Medicine" (IOS Press, forthcoming 2021). After providing insights into selected aspects of the book's informatics-related contents, the authors discuss the broader context in which Dr. Lindberg and the NLM accomplished their transformative work.

Deacetylation of Miro1 by HDAC6 blocks mitochondrial transport and mediates axon growth inhibition.

Inhibition of histone deacetylase 6 (HDAC6) was shown to support axon growth on the nonpermissive substrates myelin-associated glycoprotein (MAG) and chondroitin sulfate proteoglycans (CSPGs). Though HDAC6 deacetylates α-tubulin, we find that another HDAC6 substrate contributes to this axon growth failure. HDAC6 is known to impact transport of mitochondria, and we show that mitochondria accumulate in distal axons after HDAC6 inhibition. Miro and Milton proteins link mitochondria to motor proteins for axon transport. Exposing neurons to MAG and CSPGs decreases acetylation of Miro1 on Lysine 105 (K105) and decreases axonal mitochondrial transport. HDAC6 inhibition increases acetylated Miro1 in axons, and acetyl-mimetic Miro1 K105Q prevents CSPG-dependent decreases in mitochondrial transport and axon growth. MAG- and CSPG-dependent deacetylation of Miro1 requires RhoA/ROCK activation and downstream intracellular Ca2+ increase, and Miro1 K105Q prevents the decrease in axonal mitochondria seen with activated RhoA and elevated Ca2+ These data point to HDAC6-dependent deacetylation of Miro1 as a mediator of axon growth inhibition through decreased mitochondrial transport.

A method for analyzing inpatient care variability through physicians' orders.

OBJECTIVE: Administrators assess care variability through chart review or cost variability to inform care standardization efforts. Chart review is costly and cost variability is imprecise. This study explores the potential of physician orders as an alternative measure of care variability. MATERIALS & METHODS: The authors constructed an order variability metric from adult Vanderbilt University Hospital patients treated between 2013 and 2016. The study compared how well a cost variability model predicts variability in the length of stay compared to an order variability model. Both models adjusted for covariates such as severity of illness, comorbidities, and hospital transfers. RESULTS: The order variability model significantly minimized the Akaike information criterion (superior outcome) compared to the cost variability model. This result also held when excluding patients who received intensive care. CONCLUSION: Order variability can potentially typify care variability better than cost variability. Order variability is a scalable metric, calculable during the course of care.

Axonal G3BP1 stress granule protein limits axonal mRNA translation and nerve regeneration.

Critical functions of intra-axonally synthesized proteins are thought to depend on regulated recruitment of mRNA from storage depots in axons. Here we show that axotomy of mammalian neurons induces translation of stored axonal mRNAs via regulation of the stress granule protein G3BP1, to support regeneration of peripheral nerves. G3BP1 aggregates within peripheral nerve axons in stress granule-like structures that decrease during regeneration, with a commensurate increase in phosphorylated G3BP1. Colocalization of G3BP1 with axonal mRNAs is also correlated with the growth state of the neuron. Disrupting G3BP functions by overexpressing a dominant-negative protein activates intra-axonal mRNA translation, increases axon growth in cultured neurons, disassembles axonal stress granule-like structures, and accelerates rat nerve regeneration in vivo.

hnRNPs Interacting with mRNA Localization Motifs Define Axonal RNA Regulons.

mRNA translation in axons enables neurons to introduce new proteins at sites distant from their cell body. mRNA-protein interactions drive this post-transcriptional regulation, yet knowledge of RNA binding proteins (RBP) in axons is limited. Here we used proteomics to identify RBPs interacting with the axonal localizing motifs of Nrn1, Hmgb1, Actb, and Gap43 mRNAs, revealing many novel RBPs in axons. Interestingly, no RBP is shared between all four RNA motifs, suggesting graded and overlapping specificities of RBP-mRNA pairings. A systematic assessment of axonal mRNAs interacting with hnRNP H1, hnRNP F, and hnRNP K, proteins that bound with high specificity to Nrn1 and Hmgb1, revealed that axonal mRNAs segregate into axon growth-associated RNA regulons based on hnRNP interactions. Axotomy increases axonal transport of hnRNPs H1, F, and K, depletion of these hnRNPs decreases axon growth and reduces axonal mRNA levels and axonal protein synthesis. Thus, subcellular hnRNP-interacting RNA regulons support neuronal growth and regeneration.

Discovering hidden knowledge through auditing clinical diagnostic knowledge bases.

OBJECTIVE: Evaluate potential for data mining auditing techniques to identify hidden concepts in diagnostic knowledge bases (KB). Improving completeness enhances KB applications such as differential diagnosis and patient case simulation. MATERIALS AND METHODS: Authors used unsupervised (Pearson's correlation - PC, Kendall's correlation - KC, and a heuristic algorithm - HA) methods to identify existing and discover new finding-finding interrelationships ("properties") in the INTERNIST-1/QMR KB. Authors estimated KB maintenance efficiency gains (effort reduction) of the approaches. RESULTS: The methods discovered new properties at 95% CI rates of [0.1%, 5.4%] (PC), [2.8%, 12.5%] (KC), and [5.6%, 18.8%] (HA). Estimated manual effort reduction for HA-assisted determination of new properties was approximately 50-fold. CONCLUSION: Data mining can provide an efficient supplement to ensuring the completeness of finding-finding interdependencies in diagnostic knowledge bases. Authors' findings should be applicable to other diagnostic systems that record finding frequencies within diseases (e.g., DXplain, ISABEL).

Evaluation of a Novel System to Enhance Clinicians' Recognition of Preadmission Adverse Drug Reactions.

BACKGROUND: Often unrecognized by providers, adverse drug reactions (ADRs) diminish patients' quality of life, cause preventable admissions and emergency department visits, and increase health care costs. OBJECTIVE: This article evaluates whether an automated system, the Adverse Drug Effect Recognizer (ADER), could assist clinicians in detecting and addressing inpatients' ongoing preadmission ADRs. METHODS: ADER uses natural language processing to extract patients' medications, findings, and past diagnoses from admission notes. It compares excerpted information to a database of known medication adverse effects and promptly warns clinicians about potential ongoing ADRs and potential confounders via alerts placed in patients' electronic health records (EHRs). A 3-month intervention trial evaluated ADER's impact on antihypertensive medication ordering behaviors. At the time of patient admission, ADER warned providers on the Internal Medicine wards of Vanderbilt University Hospital about potential ongoing preadmission antihypertensive medication ADRs. A retrospective control group, comprised similar physicians from a period prior to the intervention, received no alerts. The evaluation compared ordering behaviors for each group to determine if preadmission medications changed during hospitalization or at discharge. The study also analyzed intervention group participants' survey responses and user comments. RESULTS: ADER identified potential preadmission ADRs for 30% of both groups. Compared with controls, intervention providers more often withheld or discontinued suspected ADR-causing medications during the inpatient stay (p < 0.001). Intervention providers who responded to alert-related surveys held or discontinued suspected ADR-causing medications more often at discharge (p < 0.001). CONCLUSION: Results indicate that ADER helped physicians recognize ADRs and reduced ordering of suspected ADR-causing medications. In hospitals using EHRs, ADER-like systems could improve clinicians' recognition and elimination of ongoing ADRs.

Accessing Reliable Health Information on the Web: A Review of the HON Approach.

Accessing online health content of high quality and reliability presents challenges. Laypersons cannot easily differentiate trustworthy content from misinformed or manipulated content. This article describes complementary approaches for members of the general public and health professionals to find trustworthy content with as little bias as possible. These include the Khresmoi health search engine (K4E), the Health On the Net Code of Conduct (HONcode) and health trust indicator Web browser extensions.

A long journey to short abbreviations: developing an open-source framework for clinical abbreviation recognition and disambiguation (CARD).

OBJECTIVE: The goal of this study was to develop a practical framework for recognizing and disambiguating clinical abbreviations, thereby improving current clinical natural language processing (NLP) systems' capability to handle abbreviations in clinical narratives. METHODS: We developed an open-source framework for clinical abbreviation recognition and disambiguation (CARD) that leverages our previously developed methods, including: (1) machine learning based approaches to recognize abbreviations from a clinical corpus, (2) clustering-based semiautomated methods to generate possible senses of abbreviations, and (3) profile-based word sense disambiguation methods for clinical abbreviations. We applied CARD to clinical corpora from Vanderbilt University Medical Center (VUMC) and generated 2 comprehensive sense inventories for abbreviations in discharge summaries and clinic visit notes. Furthermore, we developed a wrapper that integrates CARD with MetaMap, a widely used general clinical NLP system. RESULTS AND CONCLUSION: CARD detected 27 317 and 107 303 distinct abbreviations from discharge summaries and clinic visit notes, respectively. Two sense inventories were constructed for the 1000 most frequent abbreviations in these 2 corpora. Using the sense inventories created from discharge summaries, CARD achieved an F1 score of 0.755 for identifying and disambiguating all abbreviations in a corpus from the VUMC discharge summaries, which is superior to MetaMap and Apache's clinical Text Analysis Knowledge Extraction System (cTAKES). Using additional external corpora, we also demonstrated that the MetaMap-CARD wrapper improved MetaMap's performance in recognizing disorder entities in clinical notes. The CARD framework, 2 sense inventories, and the wrapper for MetaMap are publicly available at https://sbmi.uth.edu/ccb/resources/abbreviation.htm . We believe the CARD framework can be a valuable resource for improving abbreviation identification in clinical NLP systems.

Health On the Net's 20 Years of Transparent and Reliable Health Information.

The Health On the Net Foundation (HON) was born in 1996, during the beginning of the World Wide Web, from a collective decision by health specialists, led by the late Jean-Raoul Scherrer, who anticipated the need for online trustworthy health information. Because the Internet is a free space that everyone shares, a search for quality information is like a shot in the dark: neither will reliably hit their target. Thus, HON was created to promote deployment of useful and reliable online health information, and to enable its appropriate and efficient use. Two decades on, HON is the oldest and most valued quality marker for online health information. The organization has maintained its reputation through dynamic measures, innovative endeavors and dedication to upholding key values and goals. This paper provides an overview of the HON Foundation, and its activities, challenges, and achievements over the years.