About

S. Loyd Christensen, MD, is an Assistant Professor (Clinical) in the Department of Internal Medicine at the University of Utah. He specializes in General Internal Medicine and is actively involved in clinical practice across multiple locations including the University of Utah Hospital, Huntsman Mental Health Institute, Craig H. Neilsen Rehabilitation Hospital, and other healthcare facilities in Salt Lake City. Dr. Christensen is fluent in Spanish, English, and Portuguese, which facilitates his engagement with diverse patient populations. His educational background includes a B.M. from Brigham Young University, an M.S. in Medical Science from the University of North Texas Health Science Center, and an M.D. from Texas Tech University Health Sciences Center El Paso Paul L. Foster School of Medicine. He completed his residency in Internal Medicine at USF Health / Brandon Regional Hospital. His professional focus is on general internal medicine, and he is board-certified by the American Board of Internal Medicine. His work encompasses both clinical practice and academic responsibilities within the University of Utah's Department of Internal Medicine.

Research topics

• Computer Science
• Artificial Intelligence
• Medicine
• Political Science
• Emergency medicine
• Data science
• Gerontology
• Psychology
• Internal medicine
• Pathology

Selected publications

• Cognitive and neurobehavioral phenotypes of post 9/11 veterans with epilepsy and mild traumatic brain injury

  Frontiers in Neurology · 2026-01-26

  articleOpen access

  Introduction: Traumatic brain injury (TBI) and epilepsy are significant health concerns among the veteran population, but the links between mild TBI and cognitive and behavioral changes in epilepsy have been little explored. This study leveraged natural language processing of medical records and chart review to assess the prevalence and patterns of cognitive and behavioral symptoms in post-9/11 veterans with epilepsy, with and without history of mild TBI. The study objective was to identify distinct neurobehavioral phenotypes, and then explore their socio-demographic factors, comorbidities, and phenotypes. Methods: We conducted a detailed chart review using NLP to extract cognitive dysfunction indicators that were categorized into seven Research Domain Criteria domains. Employing Uniform Manifold Approximation and Projection for clustering and dimensionality reduction. Results: By clustering individuals on behavioral and cognitive concepts in medical notes, this study extends beyond traditional diagnostic classifications, revealing a cognitive and behavioral phenotype of veterans. Veterans with post traumatic epilepsy often demonstrate significant cognitive risk profiles associated with RDoC domains, particularly in domains related to cognitive function and arousal/regulatory systems. Both veterans with TBI before Epilepsy post traumatic epilepsy and those with epilepsy preceding TBI displayed greater cognitive and behavioral burden compared to veteran with TBI only. Notably, epilepsy preceding TBI were found more often clustering in high behavioral risk profiles. This group with epilepsy preceding TBI was associated with, including dysfunction in the RDoC domains related to negative valence systems (44.4%), arousal/regulatory systems (37.0%), and interpersonal trauma. Discussion: These findings highlight the complex interplay between TBI and Epilepsy in shaping long term cognitive/behavioral challenges and point to the need for targeted clinical management, personalized treatment approaches, and refined therapeutic strategies to maximize the quality of life for affected veterans.

  PublisherOA PDFDOI

• Automatic Extraction of Skin and Soft Tissue Infection Status from Clinical Notes

  Studies in health technology and informatics · 2024-01-25 · 1 citations

  articleOpen access

  The reliable identification of skin and soft tissue infections (SSTIs) from electronic health records is important for a number of applications, including quality improvement, clinical guideline construction, and epidemiological analysis. However, in the United States, types of SSTIs (e.g. is the infection purulent or non-purulent?) are not captured reliably in structured clinical data. With this work, we trained and evaluated a rule-based clinical natural language processing system using 6,576 manually annotated clinical notes derived from the United States Veterans Health Administration (VA) with the goal of automatically extracting and classifying SSTI subtypes from clinical notes. The trained system achieved mention- and document-level performance metrics of the range 0.39 to 0.80 for mention level classification and 0.49 to 0.98 for document level classification.

  PublisherOA PDFDOI

• Augmenting the Hospital Score with social risk factors to improve prediction for 30-day readmission following acute myocardial infarction

  Medical Research Archives · 2024-01-01

  articleOpen access

  Background: Hospital Score is a well-known and validated tool for predicting readmission risk among diverse patient populations. Integrating social risk factors using natural language processing with the Hospital Score may improve its ability to predict 30-day readmissions following an acute myocardial infarction. Methods: A retrospective cohort included patients hospitalized at Vanderbilt University Medical Center between January 1, 2007, and December 31, 2016, with a primary index diagnosis of acute myocardial infarction, who were discharged alive. To supplement ascertainment of 30-day readmissions, data were linked to Center for Medicare & Medicaid Services (CMS) administrative data. Clinical notes from the cohort were extracted, and a natural language processing model was deployed, counting mentions of eight social risk factors. A logistic regression prediction model was run using the Hospital Score composite, its component variables, and the natural language processing-derived social risk factors. ROC comparison analysis was performed. Results: The cohort included 6,165 unique patients, where 4,137 (67.1%) were male, 1,020 (16.5%) were Black or other people of color, the average age was 67 years (SD: 13), and the 30-day hospital readmission rate was 15.1% (N=934). The final test-set AUROCs were between 0.635 and 0.669. The model containing the Hospital Score component variables and the natural language processing-derived social risk factors obtained the highest AUROC. Discussion: Social risk factors extracted using natural language processing improved model performance when added to the Hospital Score composite. Clinicians and health systems should consider incorporating social risk factors when using the Hospital Score composite to evaluate risk for readmission among patients hospitalized for acute myocardial infarction.

  PublisherOA PDFDOI

• Identifying clinical phenotypes of frontotemporal dementia in post-9/11 era veterans using natural language processing

  Frontiers in Neurology · 2024-02-15 · 7 citations

  articleOpen access

  Introduction: Frontotemporal dementia (FTD) encompasses a clinically and pathologically diverse group of neurodegenerative disorders, yet little work has quantified the unique phenotypic clinical presentations of FTD among post-9/11 era veterans. To identify phenotypes of FTD using natural language processing (NLP) aided medical chart reviews of post-9/11 era U.S. military Veterans diagnosed with FTD in Veterans Health Administration care. Methods: A medical record chart review of clinician/provider notes was conducted using a Natural Language Processing (NLP) tool, which extracted features related to cognitive dysfunction. NLP features were further organized into seven Research Domain Criteria Initiative (RDoC) domains, which were clustered to identify distinct phenotypes. Results: Veterans with FTD were more likely to have notes that reflected the RDoC domains, with cognitive and positive valence domains showing the greatest difference across groups. Clustering of domains identified three symptom phenotypes agnostic to time of an individual having FTD, categorized as Low (16.4%), Moderate (69.2%), and High (14.5%) distress. Comparison across distress groups showed significant differences in physical and psychological characteristics, particularly prior history of head injury, insomnia, cardiac issues, anxiety, and alcohol misuse. The clustering result within the FTD group demonstrated a phenotype variant that exhibited a combination of language and behavioral symptoms. This phenotype presented with manifestations indicative of both language-related impairments and behavioral changes, showcasing the coexistence of features from both domains within the same individual. Discussion: This study suggests FTD also presents across a continuum of severity and symptom distress, both within and across variants. The intensity of distress evident in clinical notes tends to cluster with more co-occurring conditions. This examination of phenotypic heterogeneity in clinical notes indicates that sensitivity to FTD diagnosis may be correlated to overall symptom distress, and future work incorporating NLP and phenotyping may help promote strategies for early detection of FTD.

  PublisherOA PDFDOI

• Examining the association of social risk with heart failure readmission in the Veterans Health Administration

  BMC Health Services Research · 2021-08-26 · 11 citations

  articleOpen access

  BACKGROUND: Previous research has found that social risk factors are associated with an increased risk of 30-day readmission. We aimed to assess the association of 5 social risk factors (living alone, lack of social support, marginal housing, substance abuse, and low income) with 30-day Heart Failure (HF) hospital readmissions within the Veterans Health Affairs (VA) and the impact of their inclusion on hospital readmission model performance. METHODS: We performed a retrospective cohort study using chart review and VA and Centers for Medicare and Medicaid Services (CMS) administrative data from a random sample of 1,500 elderly (≥ 65 years) Veterans hospitalized for HF in 2012. Using logistic regression, we examined whether any of the social risk factors were associated with 30-day readmission after adjusting for age alone and clinical variables used by CMS in its 30-day risk stratified readmission model. The impact of these five social risk factors on readmission model performance was assessed by comparing c-statistics, likelihood ratio tests, and the Hosmer-Lemeshow goodness-of-fit statistic. RESULTS: The prevalence varied among the 5 risk factors; low income (47 % vs. 47 %), lives alone (18 % vs. 19 %), substance abuse (14 % vs. 16 %), lacks social support (2 % vs. <1 %), and marginal housing (< 1 % vs. 3 %) among readmitted and non-readmitted patients, respectively. Controlling for clinical factors contained in CMS readmission models, a lack of social support was found to be associated with an increased risk of 30-day readmission (OR 4.8, 95 %CI 1.35-17.88), while marginal housing was noted to decrease readmission risk (OR 0.21, 95 %CI 0.03-0.87). Living alone (OR: 0.9, 95 %CI 0.64-1.26), substance abuse (OR 0.91, 95 %CI 0.67-1.22), and having low income (OR 1.01, 95 %CI 0.77-1.31) had no association with HF readmissions. Adding the five social risk factors to a CMS-based model (age and comorbid conditions; c-statistic 0.62) did not improve model performance (c-statistic: 0.62). CONCLUSIONS: While a lack of social support was associated with 30-day readmission in the VA, its prevalence was low. Moreover, the inclusion of some social risk factors did not improve readmission model performance. In an integrated healthcare system like the VA, social risk factors may have a limited effect on 30-day readmission outcomes.

  PublisherOA PDFDOI

• Adaptation of an NLP system to a new healthcare environment to identify social determinants of health

  Journal of Biomedical Informatics · 2021 · 43 citations

  • Computer Science
  • Artificial Intelligence
  • Computer Science
  PublisherOA PDFDOI

• Examining the Interfacility Variation of Social Determinants of Health in the Veterans Health Administration

  Federal practitioner · 2021 · 11 citations

  • Medicine
  • Gerontology
  • Emergency medicine

  INTRODUCTION: Recently, numerous studies have linked social determinants of health (SDoH) with clinical outcomes. While this association is well known, the interfacility variability of these risk favors within the Veterans Health Administration (VHA) is not known. Such information could be useful to the VHA for resource and funding allocation. The aim of this study is to explore the interfacility variability of 5 SDoH within the VHA. METHODS: In a cohort of patients (aged ≥ 65 years) hospitalized at VHA acute care facilities with either acute myocardial infarction (AMI), heart failure (HF), or pneumonia in 2012, we assessed (1) the proportion of patients with any of the following five documented SDoH: lives alone, marginal housing, alcohol use disorder, substance use disorder, and use of substance use services, using administrative diagnosis codes and clinic stop codes; and (2) the documented facility-level variability of these SDoH. To examine whether variability was due to regional coding differences, we assessed the variation of living alone using a validated natural language processing (NLP) algorithm. RESULTS: codes. Interfacility variability was noted with both administrative and NLP extraction methods. CONCLUSIONS: The presence of SDoH in administrative data among patients hospitalized for common medical issues is low and variable across VHA facilities. Significant facility-level variation of 5 SDoH was present regardless of extraction method.

  DOI

• Moonstone: a novel natural language processing system for inferring social risk from clinical narratives

  Journal of Biomedical Semantics · 2019-04-11 · 75 citations

  articleOpen access

  BACKGROUND: Social risk factors are important dimensions of health and are linked to access to care, quality of life, health outcomes and life expectancy. However, in the Electronic Health Record, data related to many social risk factors are primarily recorded in free-text clinical notes, rather than as more readily computable structured data, and hence cannot currently be easily incorporated into automated assessments of health. In this paper, we present Moonstone, a new, highly configurable rule-based clinical natural language processing system designed to automatically extract information that requires inferencing from clinical notes. Our initial use case for the tool is focused on the automatic extraction of social risk factor information - in this case, housing situation, living alone, and social support - from clinical notes. Nursing notes, social work notes, emergency room physician notes, primary care notes, hospital admission notes, and discharge summaries, all derived from the Veterans Health Administration, were used for algorithm development and evaluation. RESULTS: An evaluation of Moonstone demonstrated that the system is highly accurate in extracting and classifying the three variables of interest (housing situation, living alone, and social support). The system achieved positive predictive value (i.e. precision) scores ranging from 0.66 (homeless/marginally housed) to 0.98 (lives at home/not homeless), accuracy scores ranging from 0.63 (lives in facility) to 0.95 (lives alone), and sensitivity (i.e. recall) scores ranging from 0.75 (lives in facility) to 0.97 (lives alone). CONCLUSIONS: The Moonstone system is - to the best of our knowledge - the first freely available, open source natural language processing system designed to extract social risk factors from clinical text with good (lives in facility) to excellent (lives alone) performance. Although developed with the social risk factor identification task in mind, Moonstone provides a powerful tool to address a range of clinical natural language processing tasks, especially those tasks that require nuanced linguistic processing in conjunction with inference capabilities.

  PublisherOA PDFDOI

• Annotating Social Determinants of Health and Functional Status Information Using Publicly Accessible Corpora.

  AMIA · 2018-01-01 · 1 citations

  article
  Publisher

• Automatic Extraction of Social Determinants of Health from Veterans Affairs Clinical Documents using Natural Language Processing.

  CRI eBooks · 2017-01-01

  book-chapter
  Publisher

Frequent coauthors

• Wendy W. Chapman

  30 shared

• Marzieh Vali

  Northern California Institute for Research and Education

  14 shared

• Samir Abdelrahman

  University of Utah Health Care

  13 shared

• Salomeh Keyhani

  University of California, San Francisco

  13 shared

• Peter J. Haug

  Intermountain Healthcare

  12 shared

• Louise C. Walter

  University of California, San Francisco

  11 shared

• Charlie M. Wray

  University of California, San Francisco

  10 shared

• Brett R. South

  Pfizer (United States)

  9 shared

Education

• M.D.

  Texas Tech

  2020

• Other

  USF Health/Brandon Regional Hospital

  2023