A5063499248- Machine Learning in Healthcare
- Topic Modeling
- Sepsis Diagnosis and Treatment
- Biomedical Text Mining and Ontologies
- Alcohol Consumption and Health Effects
- Respiratory Support and Mechanisms
- Cardiac Arrest and Resuscitation
- Opioid Use Disorder Treatment
- Artificial Intelligence in Healthcare and Education
- Emergency and Acute Care Studies
- Substance Abuse Treatment and Outcomes
- Trauma and Emergency Care Studies
- Electronic Health Records Systems
- Intensive Care Unit Cognitive Disorders
- Alcoholism and Thiamine Deficiency
- Clinical Reasoning and Diagnostic Skills
- COVID-19 diagnosis using AI
- Natural Language Processing Techniques
- Medical Coding and Health Information
- Neuroscience of respiration and sleep
- Bacterial Identification and Susceptibility Testing
- Health Systems, Economic Evaluations, Quality of Life
- Burn Injury Management and Outcomes
- Asthma and respiratory diseases
- Obstructive Sleep Apnea Research
University of Wisconsin–Madison
2020-2025
University of Wisconsin Health
2024-2025
Babol University of Medical Sciences
2024
Max Weber Stiftung - Deutsche Geisteswissenschaftliche Institute im Ausland
2024
UW Health University Hospital
2024
Loyola University Chicago
2015-2022
National Institute on Drug Abuse
2022
DePaul University
2022
Urban Institute
2022
Xylem (United States)
2022
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Sepsis is a heterogeneous syndrome, and identifying clinically relevant subphenotypes essential.
Abstract Large Language Models have expanded the potential for clinical Natural Generation (NLG), presenting new opportunities to manage vast amounts of medical text. However, their use in such high-stakes environments necessitate robust evaluation workflows. In this review, we investigated current landscape metrics NLG healthcare and proposed a future direction address resource constraints expert human while balancing alignment with judgments.
Risk scores used in early warning systems exist for general inpatients and patients with suspected infection outside the intensive care unit (ICU), but their relative performance is incompletely characterized.To compare of tools to determine points-based risk among all hospitalized patients, including those without infection, identifying at death and/or ICU transfer.In a cohort design, retrospective analysis prospectively collected data was conducted 21 California 7 Illinois hospitals...
Rationale: A molecular test to distinguish between sepsis and systemic inflammation of noninfectious etiology could potentially have clinical utility.Objectives: This study evaluated the diagnostic performance a host response assay (SeptiCyte LAB) designed in critically ill adults.Methods: The employed prospective, observational, noninterventional design recruited heterogeneous cohort adult critical care patients from seven sites United States (n = 249). An additional group 198 patients,...
<h3>Importance</h3> Acute kidney injury (AKI) is associated with increased morbidity and mortality in hospitalized patients. Current methods to identify patients at high risk of AKI are limited, few prediction models have been externally validated. <h3>Objective</h3> To internally validate a machine learning score detect <h3>Design, Setting, Participants</h3> This diagnostic study included 495 971 adult hospital admissions the University Chicago (UC) from 2008 2016 (n = 48 463), Loyola...
Objective: This study aims to evaluate the trends in cancer (CA) admissions and surgeries after Affordable Care Act (ACA) Medicaid expansion. Methods: is a retrospective using HCUP-SID analyzing inpatient CA (pancreas, esophagus, lung, bladder, breast, colorectal, prostate, gastric) pre- (2010–2013) post- (2014) Surgery was defined as observed resection rate per 100 admissions. Nonexpansion (FL) expansion states (IA, MD, NY) were compared. A generalized linear model with Poisson distribution...
Abstract Objectives To assess fairness and bias of a previously validated machine learning opioid misuse classifier. Materials & Methods Two experiments were conducted with the classifier’s original (n = 1000) external validation 53 974) datasets from 2 health systems. Bias was assessed via testing for differences in type II error rates across racial/ethnic subgroups (Black, Hispanic/Latinx, White, Other) using bootstrapped 95% confidence intervals. A local surrogate model estimated to...
(1) Background: SeptiCyte RAPID is a molecular test for discriminating sepsis from non-infectious systemic inflammation, and estimating probabilities. The objective of this study was the clinical validation RAPID, based on testing retrospectively banked prospectively collected patient samples. (2) Methods: cartridge-based accepts PAXgene blood RNA sample provides sample-to-answer processing in ~1 h. output (SeptiScore, range 0–15) falls into four interpretation bands, with higher scores...
Large Language Models (LLMs) are rapidly being adopted in healthcare, necessitating standardized reporting guidelines. We present TRIPOD-LLM, an extension of the TRIPOD+AI statement, addressing unique challenges LLMs biomedical applications. TRIPOD-LLM provides a comprehensive checklist 19 main items and 50 subitems, covering key aspects from title to discussion. The guidelines introduce modular format accommodating various LLM research designs tasks, with 14 32 subitems applicable across...
BACKGROUND: Early detection of clinical deterioration using machine-learning early warning scores may improve outcomes. However, most implemented were developed logistic regression, only underwent retrospective validation, and not tested in important subgroups. OBJECTIVE: The objective our multicenter prospective observational study was to develop prospectively validate a gradient-boosted machine model (eCARTv5) for identifying on the wards. DERIVATION COHORT: All adult patients admitted...
As large language models (LLMs) are integrated into electronic health record (EHR) workflows, validated instruments essential to evaluate their performance before implementation and as documentation practices evolve. Existing for provider quality often unsuitable the complexities of LLM-generated text lack validation on real-world data. The Provider Documentation Summarization Quality Instrument (PDSQI-9) was developed clinical summaries. This study aimed validate PDSQI-9 across key aspects...
Alcohol misuse is present in over a quarter of trauma patients. Information the clinical notes electronic health record patients may be used for phenotyping tasks with natural language processing (NLP) and supervised machine learning. The objective this study to train validate an NLP classifier identifying alcohol misuse.An observational cohort 1422 adult admitted center between April 2013 November 2016. Linguistic was performed using Text Analysis Knowledge Extraction System. primary...
One of the key challenges in successful deployment and meaningful adoption AI healthcare is health system-level governance applications. Such critical not only for patient safety accountability by a system, but to foster clinician trust improve facilitate outcomes. In this case study, we describe development such structure at University Wisconsin Health (UWH) that provides oversight applications from assessment validity user acceptability through safe with continuous monitoring...
Background The clinical narrative in electronic health records (EHRs) carries valuable information for predictive analytics; however, its free-text form is difficult to mine and analyze decision support (CDS). Large-scale natural language processing (NLP) pipelines have focused on data warehouse applications retrospective research efforts. There remains a paucity of evidence implementing NLP at the bedside care delivery. Objective We aimed detail hospital-wide, operational pipeline implement...
<sec> <title>BACKGROUND</title> Electronic Health Records (EHRs) and routine documentation practices are crucial for providing comprehensive health records, diagnoses, treatments patients' daily care. However, the complexity verbosity of EHR narratives can overload healthcare providers risk diagnostic inaccuracies. </sec> <title>OBJECTIVE</title> This study aims to enhance proficiency Large Language Models (LLMs) with a medical Knowledge Graph in automated diagnosis generation by minimizing...
Approaches are needed to better delineate the continuum of opioid misuse that occurs in hospitalized patients. A prognostic enrichment strategy with latent class analysis (LCA) may facilitate treatment strategies subtypes misuse. We aim identify patients and examine distinctions between by examining patient characteristics, topic models from clinical notes, outcomes.This was an observational study inpatient hospitalizations at a tertiary care center 2007 2017. Patients were identified using...
Although alcohol misuse is associated with deleterious outcomes in critically ill patients, its detection by either self-report or examination of biomarkers difficult to obtain consistently. Phosphatidylethanol (PEth) a direct biomarker that can characterize consumption patterns; however, diagnostic accuracy identifying patients unknown.