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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Easily configured real-time CPOE Pick Off Tool supporting focused clinical research and quality improvement.

Real-time alerting systems typically warn providers about abnormal laboratory results or medication interactions. For more complex tasks, institutions create site-wide 'data warehouses' to support quality audits and longitudinal research. Sophisticated systems like i2b2 or Stanford's STRIDE utilize data warehouses to identify cohorts for research and quality monitoring.

Analyzing differences between chinese and english clinical text: a cross-institution comparison of discharge summaries in two languages.

Worldwide adoption of Electronic Medical Records (EMRs) databases in health care have generated an unprecedented amount of clinical data available electronically. There has been an increasing trend in US and western institutions towards collaborating with China on medical research using EMR data. However, few studies have investigated characteristics of EMR data in China and their differences with the data in US hospitals.

Implementing an interface terminology for structured clinical documentation.

Clinically oriented interface terminologies support interactions between humans and computer programs that accept structured entry of healthcare information. This manuscript describes efforts over the past decade to introduce an interface terminology called CHISL (Categorical Health Information Structured Lexicon) into clinical practice as part of a computer-based documentation application at Vanderbilt University Medical Center. Vanderbilt supports a spectrum of electronic documentation modalities, ranging from transcribed dictation, to a partial template of free-form notes, to strict, structured data capture.

A comparative study of current Clinical Natural Language Processing systems on handling abbreviations in discharge summaries.

Clinical Natural Language Processing (NLP) systems extract clinical information from narrative clinical texts in many settings. Previous research mentions the challenges of handling abbreviations in clinical texts, but provides little insight into how well current NLP systems correctly recognize and interpret abbreviations. In this paper, we compared performance of three existing clinical NLP systems in handling abbreviations: MetaMap, MedLEE, and cTAKES.

Comparative analysis of pharmacovigilance methods in the detection of adverse drug reactions using electronic medical records.

Objective: Medication  safety requires that each drug be monitored throughout its market life as early detection of adverse drug reactions (ADRs) can lead to alerts that prevent patient harm. Recently, electronic medical records (EMRs) have emerged as a valuable resource for pharmacovigilance. This study examines the use of retrospective medication orders and inpatient laboratory results documented in the EMR to identify ADRs.

Detecting abbreviations in discharge summaries using machine learning methods.

Recognition and identification of abbreviations is an important, challenging task in clinical natural language processing (NLP). A comprehensive lexical resource comprised of all common, useful clinical abbreviations would have great applicability. The authors present a corpus-based method to create a lexical resource of clinical abbreviations using machine-learning (ML) methods, and tested its ability to automatically detect abbreviations from hospital discharge summaries.

Analyses of longitudinal, hospital clinical laboratory data with application to blood glucose concentrations.

Electronic medical record (EMR) systems afford researchers with opportunities to investigate a broad range of scientific questions. In contrast to purposeful study designs, however, EMR data acquisition procedures typically do not align with any specific hypothesis. Subsequent investigations therefore require detailed characterization of clinical procedures and protocols that underlie EMR data, as well as careful consideration of model choice.