Dr. Amy Watson-Grace | ISSUES | The Ohio State University

Enhancing Brain Injury Surveillance Using Large Language Models and Topic Modeling

Sun, 19 Oct 2025 00:00:00 +0000

Our presentation, Enhancing Brain Injury Surveillance Using Large Language Models and Topic Modeling, was delivered by Dr. Shockley McCarthy at the AI for BI Conference at The Ohio State University on October 17, 2025.

This work introduces an AI-driven framework for improving the detection and contextual understanding of brain injury cases using unstructured medical and administrative text. By applying large language models (LLMs) and structured topic modeling, we extract interpretable patterns from injury narratives and ICD-coded data to identify mechanisms of injury, intent, and underreported cases of intimate partner violence (IPV)–related brain injury.

Integrating methods from public health, law, and computational linguistics, our approach aims to enhance the precision of injury surveillance systems and inform equitable prevention and policy strategies addressing violence-related traumatic brain injury (TBI).

Download the presentation here .

Dr. Amy Watson-Grace

Tue, 03 Nov 2026 14:45:00 +0000

Dr. Amy Watson-Grace is a PhD candidate in the ISSUES Lab.

Their work focuses on disparities in access to clinical and educational services for marginalized populations across the lifespan.. They are interested in mixed methods, advanced statistical methods, marginalized populations, inequities in clinical and educational experiences, and special education policy.

They bring skills in R, hierarchical modeling, codebook/template thematic analysis, REDCap, scientific writing, and administration/interpretation of behavioral and other clinical measures and contribute to projects such as Craniofacial injuries in victims of intimate partner violence treated in United States Emergency Departments, 2018 – 2022; Minor infractions, major consequences: A multilevel analysis of school discipline disparities affecting Native American students attending New Mexico public schools; and A descriptive analysis of suicide trends by race/ethnicity, sex, and age categories in Santa Clara, California from 2018-2023.

Certifications and Licensure

AOTA advanced pediatric board certification, 2020
USC Chan Ayres Sensory Integration certification, 2020
Ohio Department of Education, 2006 - present
NBCOT certification, 1998 - present
Ohio OT, PT, AT Board, 1998 - present

Awards and honours

Ohio State University Presidential Fellowship, 2026
Best Oral Presentation, Ohio State University Hayes Forum, 2026
Ray Travel Award, 2026
Best Oral Presentation, Ohio State University HRS Research Day, 2024, 2025
HRS Travel Award, Ohio State University, 2025
Rector Fellowship, Ohio State University, 2024
College of Medicine, Office of Diversity & Inclusion Travel Award, Ohio State University, 2024
College of Medicine Graduate Fellowship, Ohio State University, 2022

Disability-related depression inequities are driven by accessibility deficits: Evidence from crowdsourced sidewalk data and machine learning

Tue, 03 Nov 2026 14:45:00 +0000

Session: Mobility, Transit, and Built Environments for Disabled People
Program: Disability
Date: Monday, November 2, 2026 | 2:45 PM – 3:00 PM

Identifying place-specific mortality mechanisms by integrating cause-of-death narratives and neighborhood typologies using machine learning

Mon, 02 Nov 2026 09:15:00 +0000

Session: Epidemiology Section: Conceptualization and Measurement
Program: Epidemiology
Date: Monday, November 2, 2026 | 9:15 AM – 9:30 AM

Detecting Hidden Mechanisms of Abuse-Related Injury in Children: Structured Topic Modeling of Clinical Death Reviews and Legal Opinions

Mon, 24 Aug 2026 00:00:00 +0000

Presented at the ISPCAN World Congress on Child Abuse and Neglect, Melbourne, Australia, August 2426, 2026.

Full title: Detecting Hidden Mechanisms of Abuse-Related Injury in Children: Structured Topic Modeling of Clinical Death Reviews and Legal Opinions Triangulating Multidisciplinary Child Fatality Reviews with Factual and Procedural Narratives from Case Law

Abstract

Existing child maltreatment surveillance systems miss or misclassify between 8090% of maltreatment deaths, and CPS records undercount fatalities by 5576%. Unstructured narrative text clinical death reviews, court opinions, and social work records accounts for 8090% of all organizational data produced, yet it is rarely leveraged for injury surveillance.

This presentation introduces a two-study AI + Structured Topic Modeling (STM) pipeline that integrates two distinct narrative corpora to recover latent, abuse-related injury mechanisms that structured administrative codes routinely obscure.

Study Design

	Study 1: CFRT Death Reviews	Study 2: CourtListener Case Law
Source	Colorado Child Fatality Review Team reports	Appellate opinions retrieved from CourtListener
Documents	223 child-level narrative records	751 opinions → 335 confirmed cases from Ohio
Text	Two free-text fields per report (clinical + administrative)	Facts + procedural history
LLM Extraction	GPT-4o-mini: caregiver behavior, injury mechanism, setting, diagnosis	GPT-4o-mini: 9 structured fields including perpetrator, victim type, legal outcome
STM Models	Risk & Contributing Factors; Circumstances	Injury Mechanism; Long-Term Consequences
Covariate	Incident year (temporal prevalence)	Judicial ruling (affirmed / reversed)

Research Questions

Can large language models transform unstructured child fatality narratives into standardized semantic features caregiver behavior, injury mechanism, setting, diagnosis?
What latent themes emerge, and what typology of risk and incident circumstances do they suggest?
Do the recovered themes reveal recurring patterns of head and neck trauma consistent with abuse-related injury?
How does the prevalence of these themes shift across incident years (20202025)?

Key Findings

Both corpora converge on craniofacial injury as the dominant mechanism routinely undercounted by ICD codes.
Strangulation emerged as a critical lethality predictor; soft-tissue damage can surface 23 days post-injury, requiring clinician and court training on delayed sequelae.
Records document long-term neurological harm (cerebral palsy, pituitary damage, blindness) in children often years before clinical follow-up.
Children represent a structurally distinct injury profile in both datasets, warranting specialized clinical and legal protocols.
“Accidental” injuries (falls, environmental contact) are frequently embedded in an abuse context screening for mechanism, not just diagnosis, is essential.
Appellate opinions and death reviews function as injury-surveillance sources beyond structured administrative fields.

Methodology

The pipeline applies:

PDF parsing (pdftools) and regex isolation of narrative fields
GPT-4o-mini for LLM-based structured field extraction
Structured Topic Modeling (STM) with incident year or judicial ruling as a prevalence covariate
BART zero-shot classification as an independent supervised-style validation check
FREX word scoring (frequency � exclusivity) for topic labeling

Lab Members

This presentation was developed by the ISSUES Lab team at The Ohio State University:

Dr. Gia E. Barboza-Salerno (Founding Director), Dr. Henriikka Weir, Malcolm McCarthy, Dr. Karla Shockley McCarthy, Dr. Amy Watson-Grace, Dr. Keith Warren, Esenia Cassidy, and Olivia McLucas with collaborators Scottye Cash and Suzanne Perkins.

📄 Presentation

Download the presentation slides here .

Racial and Ethnic Disparities in Non-Fatal Self-Inflicted Firearm Injuries Among Adults in the United States, 2000-2021

Wed, 17 Jun 2026 00:00:00 +0000

Plain-Language Summary

This study analyzes nonfatal, self-inflicted firearm injuries in the United States over a 22-year period (2000-2021) using the National Electronic Injury Surveillance System (NEISS-FISS). Because structured database codes often fail to capture the context or intent behind a self-inflicted discharge, the research team used text mining and a machine learning (LASSO) classification model to analyze the unstructured clinical narratives written by emergency department staff.

The results show that while 58% of visits were likely unintentional discharges (accidents), 10% were probable suicide attempts. The narrative text mining revealed distinct, racially disparate patterns of injury: Non-Hispanic Black patients suffered the highest proportion of lower-extremity injuries (51%), while Hispanic patients had the highest rates of craniofacial injuries (26%) and a significant association with alcohol involvement (increasing from 12% in coded data to 20% in narrative data). This highlights the value of using natural language processing to analyze emergency department notes for better injury prevention and public health surveillance.

New Publication: Racial and Ethnic Disparities in Non-Fatal Self-Inflicted Firearm Injuries

Mon, 15 Jun 2026 00:00:00 +0000

Abstract

Nonfatal self-inflicted firearm injuries represent an important and understudied public health concern in the United States. We analyzed nationally representative data from the National Electronic Injury Surveillance System–Firearm Injury Surveillance Study (NEISS-FISS), 2000–2021, to characterize nonfatal self-inflicted firearm injuries among U.S. adults aged 18–64 years. Emergency department (ED) narratives were combined using text mining and an elastic net (LASSO) classification model to augment structured surveillance data. Among 7,362 unweighted cases (representing approximately 260,602 ED visits, 58% were classified as probable unintentional discharges, whereas approximately 10% were probable suicide attempts. Among cases with complete race and ethnicity data, Non-Hispanic White (NHW; 69.2%), Non-Hispanic Black (NHB; 23.2%), and Hispanic (7.6%) individuals comprised the primary analytic groups. Narrative analysis substantially increased detection of contextual factors, particularly alcohol involvement among Hispanic patients (12.0% to 20.1%). NHB patients had the highest proportion of lower-extremity injuries (51%), while Hispanic patients had the highest prevalence of craniofacial injuries (26%). The LASSO model effectively distinguished terms associated with probable suicide attempts from unintentional firearm discharges based on narrative language. Findings highlight limitations of structured intent coding within NEISS-FISS and demonstrate the value of narrative for improving characterization of firearm injury circumstances, revealing distinct racial and ethnic group patterns, to inform more tailored prevention strategies.

Plain-Language Summary

Congratulations to Dr. Amy Watson-Grace for receiving the prestigious Presidential Fellowship

Fri, 15 May 2026 00:00:00 +0000

Congratulations to Dr. Amy Watson-Grace for receiving the prestigious Presidential Fellowship! This fellowship recognizes her outstanding academic achievement and promising research directions.

Emotional and Physical Symptoms Following Intimate Partner Violence Victimization in the United States: Implications for Law and Public Health Policy

Mon, 01 Dec 2025 00:00:00 +0000

Intimate Partner Violence & Brain Injury in U.S. Adults: Findings from the Medical Expenditure Panel Survey, 2021

Wed, 27 Aug 2025 00:00:00 +0000

Overview

National survey data show that adults who screen positive for intimate partner violence (IPV) experience more injuries and face greater medical costs than those who do not. Nine million screened IPV-positive using the HITS screening tool, and survivors were significantly more likely to utilize emergency rooms and hospitals for treating their injuries. Head and brain injuries were much more frequent among IPV survivors, and the average cost of treating these injuries was nearly twice as high compared to adults who did not screen positive for IPV.

Key Messages

Approximately 9 million adults screened positive for IPV in 2021.
IPV-positive adults had 3–4× higher ER and inpatient injury rates than IPV-negative adults.
Adults who screened positive for IPV experienced more than twice the rate of brain-injury healthcare events compared to those who screened negative.
Average per-capita spending on brain-injury care was about twice as high among IPV-positive adults.
Inpatient TBI events are less common than ER events but far more costly; counts and spending are higher among IPV-positive adults.
Poverty, social stressors, depression, and chronic conditions are significantly associated with IPV screen positivity.

Policy Relevance & Recommendations

Standardized protocols: Implement routine protocols so that IPV-positive patients with head injury are screened for brain injury, and patients with head injury are also screened for IPV.
Integrated referral pathways: Establish clear referral systems across emergency, primary, and specialty care so that when screening protocols are triggered, patients are automatically connected to appropriate follow-up services.
Follow-up for delayed symptoms: Incorporate temporal follow-up in care plans, recognizing that brain injury symptoms may appear or worsen over time.
Accessibility and appropriateness: Ensure screening and follow-up services are accessible, widely available, and tailored to the needs of IPV survivors.
Holistic screening: Extend screening beyond identifying head injury to include social and health needs that may magnify injury effects and increase vulnerability to future IPV.
Care coordination: Utilize medical social workers and interdisciplinary teams to integrate medical, psychosocial, and safety needs into a comprehensive care response.

Methodology

This brief analyzes nationally representative data from the Medical Expenditure Panel Survey—Household Component (MEPS-HC) 2021. Adults (age ≥18) were classified as IPV screen-positive if they scored ≥10 on the Hurt, Insult, Threaten, Scream (HITS) scale, a validated four-item screening tool for intimate partner violence.

Injury outcomes were identified using ICD-10-CM codes and Clinical Classifications Software Refined (CCSR) groupings from the MEPS Conditions file, with condition–event linkages used to assign emergency department (ER), inpatient, outpatient, office-based, home health, and prescription care to specific injuries. Brain injuries were defined as traumatic brain injury (ICD-10 S06) and other head injuries (S00–S09).

We estimated:

Injury and brain-injury events per 100 adults,
Per-capita spending (in 2021 USD), and
Weighted counts of injury-linked encounters.

Analyses applied MEPS survey weights (PERWT21F) and accounted for the complex design (VARSTR, VARPSU). We used survey-weighted generalized linear models (svyglm) to generate adjusted comparisons standardized over age, sex, and race/ethnicity. Risk ratios were estimated for sociodemographic factors, health-related social needs, and chronic health conditions associated with IPV screen positivity.

Suggested Citation

Barboza-Salerno, G. E., Shockley McCarthy, K., Watson-Grace, A., Harrington, T., McCarthy, M. A., Peremen, N., & McLucas, O. (2025). Intimate Partner Violence & Brain Injury in U.S. Adults: Findings from the Medical Expenditure Panel Survey, 2021. Policy brief. The Ohio State University, ISSUE Lab (Investigating Spatial Structures in Urban Environments).

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Download the full brief for more information

The Land Cover Paradox

Wed, 01 Jan 2025 00:00:00 +0000

Plain-Language Summary

This study explores the associations between developed open space, tree canopy, blue space, and land cover diversity and suicide risk across Chicago neighborhoods. Using Bayesian spatial Poisson models at the Census Block Group level, the research reveals a land cover paradox: while developed open spaces and blue spaces are associated with lower suicide rates overall, the protective effects are context-dependent and are not uniformly experienced across urban neighborhoods. The findings emphasize that suicide prevention efforts should incorporate neighborhood-level environmental factors and structural disadvantages rather than focusing solely on individual risk profiles.