Lightweight Language Models are Prone to Reasoning Errors for Complex Computational Phenotyping Tasks.

Objective: Although computational phenotyping is a central informatics activity with resulting cohorts supporting a wide variety of applications, it is time-intensive because of manual data review. We previously assessed the ability of LLMs to perform computational phenotyping tasks using computable phenotypes for ARF respiratory support therapies. They successfully performed concept classification and classification of single-therapy phenotypes, but underperformed on multiple-therapy phenotypes.

SHREC: A Framework for Advancing Next-Generation Computational Phenotyping with Large Language Models.

Computational phenotyping is a central informatics activity with resulting cohorts supporting a wide variety of applications. However, it is time-intensive because of manual data review and limited automation. Since LLMs have demonstrated promising capabilities for text classification, comprehension, and generation, we posit they will perform well at repetitive manual review tasks traditionally performed by human experts.

Developing and sustaining inclusive language in biomedical informatics communications: an AMIA Board of Directors endorsed paper on the Inclusive Language and Context Style Guidelines.

Objectives: In 2023, AMIA's Inclusive Language and Context Style Guidelines (the "Guidelines") were approved by the Board of Directors and made a publicly available resource. This work began in 2021 through AMIA's DEI Task Force and subsequent DEI Committee; many members provided input, feedback, and time to create the Guidelines. In this paper, the authors provide a transparent account of the origin, development, contents, and dissemination of the Guidelines and share plans for their future development and use.

Design and Development of an Integrated Virtual Reality (VR)-Based Training System for Difficult Airway Management.

For over 40 years, airway management simulation has been a cornerstone of medical training, aiming to reduce procedural risks for critically ill patients. However, existing simulation technologies often lack the versatility and realism needed to replicate the cognitive and physical challenges of complex airway management scenarios. We developed a novel Virtual Reality (VR)-based simulation system designed to enhance immersive airway management training and research.

Noninvasive vs invasive respiratory support for patients with acute hypoxemic respiratory failure.

Background: Noninvasive respiratory support modalities are common alternatives to mechanical ventilation in acute hypoxemic respiratory failure. However, studies historically compare noninvasive respiratory support to conventional oxygen rather than mechanical ventilation. In this study, we compared outcomes in patients with acute hypoxemic respiratory failure treated initially with noninvasive respiratory support to patients treated initially with invasive mechanical ventilation.

Discovery of generalizable TBI phenotypes using multivariate time-series clustering.

Traumatic Brain Injury (TBI) presents a broad spectrum of clinical presentations and outcomes due to its inherent heterogeneity, leading to diverse recovery trajectories and varied therapeutic responses. While many studies have delved into TBI phenotyping for distinct patient populations, identifying TBI phenotypes that consistently generalize across various settings and populations remains a critical research gap. Our research addresses this by employing multivariate time-series clustering to unveil TBI's dynamic intricates.

Mobilizing health equity through Computable Biomedical Knowledge (CBK): a call to action to the library, information sciences, and health informatics communities.

The twin pandemics of COVID-19 and structural racism brought into focus health disparities and disproportionate impacts of disease on communities of color. Health equity has subsequently emerged as a priority. Recognizing that the future of health care will be informed by advanced information technologies including artificial intelligence (AI), machine learning, and algorithmic applications, the authors argue that to advance towards states of improved health equity, health information professionals need to engage in and encourage the conduct of research at the intersections of health equity, health disparities, and computational biomedical knowledge (CBK) applications.

Making Science Computable Using Evidence-Based Medicine on Fast Healthcare Interoperability Resources: Standards Development Project.

Background: Evidence-based medicine (EBM) has the potential to improve health outcomes, but EBM has not been widely integrated into the systems used for research or clinical decision-making. There has not been a scalable and reusable computer-readable standard for distributing research results and synthesized evidence among creators, implementers, and the ultimate users of that evidence. Evidence that is more rapidly updated, synthesized, disseminated, and implemented would improve both the delivery of EBM and evidence-based health care policy.

Acute Respiratory Failure From Early Pandemic COVID-19: Noninvasive Respiratory Support vs Mechanical Ventilation.

Background: The optimal strategy for initial respiratory support in patients with respiratory failure associated with COVID-19 is unclear, and the initial strategy may affect outcomes.

Research Question: Which initial respiratory support strategy is associated with improved outcomes in patients with COVID-19 with acute respiratory failure?

Study Design And Methods: All patients with COVID-19 requiring respiratory support and admitted to a large health care network were eligible for inclusion. We compared patients treated initially with noninvasive respiratory support (NIRS; noninvasive positive pressure ventilation by facemask or high-flow nasal oxygen) with patients treated initially with invasive mechanical ventilation (IMV).

A Least Absolute Shrinkage and Selection Operator-Derived Predictive Model for Postoperative Respiratory Failure in a Heterogeneous Adult Elective Surgery Patient Population.

Background: Postoperative respiratory failure (PRF) is associated with increased hospital charges and worse patient outcomes. Reliable prediction models can help to guide postoperative planning to optimize care, to guide resource allocation, and to foster shared decision-making with patients.

Research Question: Can a predictive model be developed to accurately identify patients at high risk of PRF?

Study Design And Methods: In this single-site proof-of-concept study, we used structured query language to extract, transform, and load electronic health record data from 23,999 consecutive adult patients admitted for elective surgery (2014-2021).

Discovery of Generalizable TBI Phenotypes Using Multivariate Time-Series Clustering.

Traumatic Brain Injury (TBI) presents a broad spectrum of clinical presentations and outcomes due to its inherent heterogeneity, leading to diverse recovery trajectories and varied therapeutic responses. While many studies have delved into TBI phenotyping for distinct patient populations, identifying TBI phenotypes that consistently generalize across various settings and populations remains a critical research gap. Our research addresses this by employing multivariate time-series clustering to unveil TBI's dynamic intricates.

Impact of the COVID-19 Pandemic on Emergency Department Encounters in a Major Metropolitan Area.

Background: The end of 2019 marked the emergence of the COVID-19 pandemic. Public avoidance of health care facilities, including the emergency department (ED), has been noted during prior pandemics.

Objective: This study described pandemic-related changes in adult and pediatric ED presentations, acuity, and hospitalization rates during the pandemic in a major metropolitan area.

Noninvasive vs Invasive Respiratory Support for Patients with Acute Hypoxemic Respiratory Failure.

Rationale: Noninvasive respiratory support modalities are common alternatives to mechanical ventilation for patients with early acute hypoxemic respiratory failure. These modalities include noninvasive positive pressure ventilation, using either continuous or bilevel positive airway pressure, and nasal high flow using a high flow nasal cannula system. However, outcomes data historically compare noninvasive respiratory support to conventional oxygen rather than to mechanical ventilation.

Computable Phenotypes for Post-acute sequelae of SARS-CoV-2: A National COVID Cohort Collaborative Analysis.

Post-acute sequelae of SARS-CoV-2 (PASC) is an increasingly recognized yet incompletely understood public health concern. Several studies have examined various ways to phenotype PASC to better characterize this heterogeneous condition. However, many gaps in PASC phenotyping research exist, including a lack of the following: 1) standardized definitions for PASC based on symptomatology; 2) generalizable and reproducible phenotyping heuristics and meta-heuristics; and 3) phenotypes based on both COVID-19 severity and symptom duration.

Identifying TBI Physiological States by Clustering Multivariate Clinical Time-Series Data.

Determining clinically relevant physiological states from multivariate time-series data with missing values is essential for providing appropriate treatment for acute conditions such as Traumatic Brain Injury (TBI), respiratory failure, and heart failure. Utilizing non-temporal clustering or data imputation and aggregation techniques may lead to loss of valuable information and biased analyses. In our study, we apply the SLAC-Time algorithm, an innovative self-supervision-based approach that maintains data integrity by avoiding imputation or aggregation, offering a more useful representation of acute patient states.

Advances in Clinical Decision Support Systems: Contributions from the 2022 Literature.

Objective: To summarize significant research contributions published in 2022 in the field of clinical decision support (CDS) systems and select the best papers for the Decision Support section of the International Medical Informatics Association (IMIA) Yearbook 2023.

Methods: A renewed search query for identifying CDS scholarship was developed using Medical Subject Headings (MeSH) terms and related keywords. The query was executed in PubMed in January 2023.

Barriers to Adoption of Tailored Drug-Drug Interaction Clinical Decision Support.

Objective: Despite the benefits of the tailored drug-drug interaction (DDI) alerts and the broad dissemination strategy, the uptake of our tailored DDI alert algorithms that are enhanced with patient-specific and context-specific factors has been limited. The goal of the study was to examine barriers and health care system dynamics related to implementing tailored DDI alerts and identify the factors that would drive optimization and improvement of DDI alerts.

Methods: We employed a qualitative research approach, conducting interviews with a participant interview guide framed based on Proctor's taxonomy of implementation outcomes and informed by the Theoretical Domains Framework.

Comparison of Force During the Endotracheal Intubation of Commercial Simulation Manikins.

Background  Medical simulation allows clinicians to safely practice the procedural skill of endotracheal intubation. Applied force to oropharyngeal structures increases the risk of patient harm, and video laryngoscopy (VL) requires less force to obtain a glottic view. It is unknown how much force is required to obtain a glottic view using commercially available simulation manikins and if variability exists.

Assessment of Prediction Tasks and Time Window Selection in Temporal Modeling of Electronic Health Record Data: a Systematic Review.

Unlabelled: Temporal electronic health record (EHR) data are often preferred for clinical prediction tasks because they offer more complete representations of a patient's pathophysiology than static data. A challenge when working with temporal EHR data is problem formulation, which includes defining the time windows of interest and the prediction task. Our objective was to conduct a systematic review that assessed the definition and reporting of concepts relevant to temporal clinical prediction tasks.

Managed critical care: impact of remote decision-making on patient outcomes.

Objectives: Tele-intensive care unit (tele-ICU) use has become increasingly common as an extension of bedside care for critically ill patients. The objective of this work was to illustrate the degree of tele-ICU involvement in critical care processes and evaluate the impact of tele-ICU decision-making authority.

Study Design: Previous studies examining tele-ICU impact on patient outcomes do not sufficiently account for the extent of decision-making authority between remote and bedside providers.

WindowSHAP: An efficient framework for explaining time-series classifiers based on Shapley values.

Unpacking and comprehending how black-box machine learning algorithms (such as deep learning models) make decisions has been a persistent challenge for researchers and end-users. Explaining time-series predictive models is useful for clinical applications with high stakes to understand the behavior of prediction models, e.g.

A self-supervised learning-based approach to clustering multivariate time-series data with missing values (SLAC-Time): An application to TBI phenotyping.

Self-supervised learning approaches provide a promising direction for clustering multivariate time-series data. However, real-world time-series data often include missing values, and the existing approaches require imputing missing values before clustering, which may cause extensive computations and noise and result in invalid interpretations. To address these challenges, we present a Self-supervised Learning-based Approach to Clustering multivariate Time-series data with missing values (SLAC-Time).

An Empirical Comparison of Explainable Artificial Intelligence Methods for Clinical Data: A Case Study on Traumatic Brain Injury.

A longstanding challenge surrounding deep learning algorithms is unpacking and understanding how they make their decisions. Explainable Artificial Intelligence (XAI) offers methods to provide explanations of internal functions of algorithms and reasons behind their decisions in ways that are interpretable and understandable to human users. .