Engaging Stakeholders With Professional or Lived Experience to Improve Firearm Violence Lexicon Development

Across various domains, such as health and social care, law, news, and social media, there are increasing quantities of unstructured texts being produced. These potential data sources often contain rich information that could be used for domain-specific and research purposes. However, the unstructured nature of free-text data poses a significant challenge for its utilisation due to the necessity of substantial manual intervention from domain-experts to label embedded information. Annotation tools can assist with this process by providing functionality that enables the accurate capture and transformation of unstructured texts into structured annotations, which can be used individually, or as part of larger Natural Language Processing (NLP) pipelines. We present Markup (https://www.getmarkup.com/) an open-source, web-based annotation tool that is undergoing continued development for use across all domains. Markup incorporates NLP and Active Learning (AL) technologies to enable rapid and accurate annotation using custom user configurations, predictive annotation suggestions, and automated mapping suggestions to both domain-specific ontologies, such as the Unified Medical Language System (UMLS), and custom, user-defined ontologies. We demonstrate a real-world use case of how Markup has been used in a healthcare setting to annotate structured information from unstructured clinic letters, where captured annotations were used to build and test NLP applications.

We aimed to examine the feasibility of applying natural language processing (NLP) to unstructured electronic health record (EHR) documents to detect the presence of financial insecurity among patients with rheumatologic disease enrolled in an integrated care management program (iCMP). We incorporated supervised, rule-based NLP and statistical methods to identify financial insecurity among patients with rheumatic conditions enrolled in an iCMP (n = 20,395) in a multihospital EHR system. We constructed a lexicon for financial insecurity using data from available knowledge sources and then reviewed EHR notes from 538 randomly selected individuals (training cohort n = 366, validation cohort n = 172). We manually categorized records as having "definite," "possible," or "no" mention of financial insecurity. All available notes were processed using Narrative Information Linear Extraction, a rule-based version of NLP. Models were trained using the NLP features for financial insecurity using logistic, least absolute shrinkage operator (LASSO), and random forest performance characteristic and were compared with the reference standard. A total of 245,142 notes were processed from 538 individual patient records. Financial insecurity was present among 100 (27%) individuals in the training cohort and 63 (37%) in the validation cohort. The LASSO and random forest models performed identically and slightly better than logistic regression, with positive predictive values of 0.90, sensitivities of 0.29, and specificities of 0.98. The development of a context-driven lexicon used with rule-based NLP to extract data that identify financial insecurity is feasible for use and improved the capture for presence of financial insecurity with high accuracy. In the absence of a standard lexicon and construct definition for financial insecurity status, additional studies are needed to optimize the sensitivity of algorithms to categorize financial insecurity with construct validity.

To evaluate the association between self-reported gun violence exposures and mental health and behavioral health indicators in a rural population. Using cross-sectional survey responses from 630 residents of a rural county in Pennsylvania, logistic regression models estimate the likelihood of residents indicating moderate or severe levels of health outcomes as a function of gun violence exposure. We control for a series of variables related to gun ownership, behavior, history with firearms and demographic characteristics. Personal firearm victimization was associated with self-reported depressive symptoms and interrupted sleep. Secondary exposure to firearm violence, particularly exposure to friends attempting or completing a suicide, was associated with higher odds of reporting severe levels (14 days or more in the past month) of depressive symptoms, anxiety, and poor sleep. For firearm suicide involving a friend or family member, the odds of reporting severe levels of all three outcomes are 3 times greater (OR 2.984, 95% CI 1.457–6.108). For each additional firearm exposure, the odds of experiencing severe levels of mental health and sleep difficulties are 1.4 times greater (OR 1.384, 95% CI 1.115–1.720). Cumulative exposures also increase the odds of reporting binge drinking and drug use. Firearm violence exposure was associated with adverse health indicators in this rural population. Approaches to counter the effects of cumulative firearm exposure and firearm suicide exposure, including reinvigorating community spaces and strengthening social supports, may help to reduce mental health burden in rural communities.

7 Background: Natural Language Processing (NLP) presents a novel method of extracting text-embedded information from the electronic health record (EHR) to improve routine assessment of palliative quality metrics such as timely advance care planning (ACP), palliative care provision (PC), and hospice referral. Methods: We identified cancer patients (ICD-9-CM codes 140-209) who received a gastrostomy tube (ICD-9-CM 43.11, 43.19, 44.32; CPT code 49440) from Jan 1, 2012, to Mar 31, 2016 at an academic medical center. We used NLP to identify palliative indication for gastrostomy tube placement by labeling clinical notes from the EHR containing the key word “venting” near the time of the procedure. Documentation of ACP, PC, and hospice referral was identified by NLP using a validated key term library. The sensitivity and specificity of the NLP method was determined by comparing outcome identification to manual chart abstraction performed by two clinicians. All NLP code was written in the open-source programming language Python. Results: NLP was performed for 75,626 documents. Among 305 cancer patients who underwent gastrostomy, 75 (24.6%) were classified by NLP as having a palliative indication for the procedure compared to 72 patients (23.6%) classified by human coders. Manual chart abstraction took > 2,600 times longer than NLP (28 hrs vs. 38 seconds). NLP identified the correct patients with high precision (0.92) and recall (0.96). ACP was documented during the index admission for 89.3% of patients. PC was documented for 85.7% and hospice referral was documented for 64.3% of these patients with advanced cancer during the index hospitalization. NLP identified ACP, PC and hospice referral with high precision (0.88-1.0) and recall (0.92-1.0) compared to human coders. Median survival was 37 days following gastrostomy tube procedure. Conclusions: NLP can greatly speed the assessment of established palliative quality metrics with an accuracy approaching that of human coders. These methods offer opportunities for facilitate quality improvement in palliative care for patients with advanced cancer.

American Indian or Alaska Native and Black adults experience elevated rates of firearm injury and death, but both groups are severely underrepresented in research on firearm exposure and behaviors. To explore geodemographic differences in firearm behaviors and violence exposure among American Indian or Alaska Native and Black adults in the US. In this survey study, nationally representative samples of American Indian or Alaska Native and/or Black adults recruited from KnowledgePanel were surveyed cross-sectionally. Surveys were administered online between April 12 and May 4, 2023. Firearm access, storage, and carrying behaviors and lifetime firearm violence exposure were the primary outcomes. Demographic factors such as age, geographic location, and political affiliation were considered. Data were weighted to geodemographic distributions from the US Census Bureau's 2022 Current Population Survey. Of 3542 participants, 527 (14.9%) were American Indian or Alaska Native (280 [53.1%] female) and 3015 (85.1%) were Black (1646 [54.6%] female). Both groups exhibited high firearm access rates (American Indian or Alaska Native adults: 238 [45.4%; 95% CI, 39.4%-51.7%]; Black adults: 909 [30.4%; 95% CI, 28.0%-32.9%]), predominantly owning handguns for home protection. The groups demonstrated similar firearm storage patterns, and a substantial proportion endorsed always or almost always carrying firearms outside the home (American Indian or Alaska Native adults: 18.9%; Black adults: 15.2%). Self-protection was a common reason for carrying a firearm (American Indian or Alaska Native adults: 104 [84.9%; 95% CI, 74.1%-91.7%]; Black adults: 350 [88.3%; 95% CI, 82.3%-92.4%]), and a minority of participants cited lack of faith in the police (American Indian or Alaska Native adults: 19 [15.2%; 95% CI, 8.2%-26.7%]; Black adults: 61 [15.4%; 95% CI, 10.3%-21.2%]), indicating potential shifts in public safety dynamics. In this survey study of American Indian or Alaska Native and Black US adults, a substantial percentage of both groups reported living in homes with firearms, storing firearms loaded and unlocked, frequently carrying firearms outside the home, and having been exposed directly and indirectly to gun violence. These findings underscore the need for nuanced public health campaigns and policies and highlight challenges for law enforcement in contexts of racial disparities and changing legal frameworks.

To review the public health approach to preventing and treating firearm violence. Peer-reviewed, published scholarship and federal data systems. English-language, indexed research articles on the epidemiology, risk, prevention, and consequences of firearm violence. This narrative review includes findings related to the epidemiology and impact of firearm violence, focusing on short- and long-term outcomes. Evidence supporting interventions at the individual, agent, and environmental level to reduce firearm-related harm was examined. Firearm violence is a major public health challenge in the Unites States. The consequences of firearm violence reach beyond the nearly 40,000 firearm-related deaths and 90,000 firearm-related injuries each year. Firearm violence, including self-harm, assault, and unintentional injury, affects the health of individuals, families, communities, and health systems. Data sources remain inadequate, however, to fully capture these impacts. Treating firearm violence as a disease and taking a public health approach to prevention and treatment is key to reducing the harms of firearm violence. Using a public health framework not only recognizes the physical and mental consequences of firearm violence but also focuses our attention on underlying causes and on innovative, multi-level interventions to reduce the harms of firearm violence. The public health approach positions clinicians to change the conversation from political diatribe of pro-gun and anti-gun to systematically reducing injury and death. To achieve comparable success, we must design, test, and implement effective interventions at the environmental, policy, technological, and individual levels to prevent firearm violence. We must collect robust data on firearm violence and its consequences. And we must reckon with the conditions of inequality and disadvantage that feed violence through all means.

Pneumonia is caused by microbes that establish an infectious process in the lungs. The gold standard for pneumonia diagnosis is radiologist-documented pneumonia-related features in radiology notes that are captured in electronic health records in an unstructured format. The study objective was to develop a methodological approach for assessing validity of a pneumonia diagnosis based on identifying presence or absence of key radiographic features in radiology reports with subsequent rendering of diagnostic decisions into a structured format. A pneumonia-specific natural language processing (NLP) pipeline was strategically developed applying Clinical Text Analysis and Knowledge Extraction System (cTAKES) to validate pneumonia diagnoses following development of a pneumonia feature-specific lexicon. Radiographic reports of study-eligible subjects identified by International Classification of Diseases (ICD) codes were parsed through the NLP pipeline. Classification rules were developed to assign each pneumonia episode into one of three categories: "positive," "negative," or "not classified: requires manual review" based on tagged concepts that support or refute diagnostic codes. A total of 91,998 pneumonia episodes diagnosed in 65,904 patients were retrieved retrospectively. Approximately 89% (81,707/91,998) of the total pneumonia episodes were documented by 225,893 chest X-ray reports. NLP classified and validated 33% (26,800/81,707) of pneumonia episodes classified as "Pneumonia-positive," 19% as (15401/81,707) as "Pneumonia-negative," and 48% (39,209/81,707) as "episode classification pending further manual review." NLP pipeline performance metrics included accuracy (76.3%), sensitivity (88%), and specificity (75%). The pneumonia-specific NLP pipeline exhibited good performance comparable to other pneumonia-specific NLP systems developed to date.

ObjectivesTo assess the feasibility of using a natural language processing (NLP) application for extraction of free-text online activity mentions in adolescent mental health patient electronic health records (EHRs).SettingThe Clinical Records...

A SEMI-AUTOMATED APPROACH TO IDENTIFYING CHRONIC COUGH IN ELECTRONIC HEALTH RECORDS

Geriatric syndromes (GS) are complex conditions that affect older adults and often require multidisciplinary assessment. Natural language processing (NLP) has emerged as a promising tool for extracting relevant clinical information from unstructured text in electronic health records (EHRs). However, the application of NLP in detecting and monitoring GS remains an evolving area of research. This systematic review explores the role of NLP in the identification and analysis of GS, examining its applications, methodologies, and effectiveness. Furthermore, this review discusses the existing challenges, limitations, and future directions to advance NLP applications in the GS research. We conducted a systematic literature search across ten databases to identify studies that applied NLP to GS detection. Articles were screened using predefined inclusion and exclusion criteria, and relevant studies were evaluated for quality using PROBAST. Data were extracted on study characteristics, datasets, annotation processes, NLP approaches, performance metrics, population demographics, and clinical applications. A PRISMA flow diagram was used to illustrate the study selection process. A total of 65 studies were included, where the majority of the studies used traditional rule-based and machine learning approaches. Publicly available datasets were scarce, and most studies used their private dataset, leading to significant variability in data sources and formats. Annotation methodologies differed across studies, with minimal shared guidelines or standards, making direct comparisons challenging. Performance metrics varied across syndromes, with F1-score, precision, and recall as the most commonly reported. Key challenges included the lack of dataset uniformity, differences in annotation practices, and the absence of external validation. NLP has shown potential in GS analysis, particularly for the detection of syndromes and epidemiological research. However, the majority of studies only focused on one syndrome, and variability in dataset availability, annotation processes, and model performance present challenges to broader implementation. Future research should focus on improving the comprehensiveness of GS identification, dataset standardisation, enhancing model generalisability, and integrating NLP approaches into clinical workflows.

Firearm-related violence is the leading cause of death for adolescents and can have lifelong ramifications, including to one’s mental health and well-being. Through a scoping review of existing literature published since 1980, the current study describes nuanced pathways through which exposure to firearm violence (direct or indirect) can contribute to the development and exacerbation of posttraumatic symptomatology during adolescence (ages 10–19). Furthermore, this review explores moderators and mediators of this relationship, shedding light on the interplay between individual, familial, and societal factors. Findings from the reviewed studies ( N = 16) demonstrate various links between direct and indirect firearm exposure and short- and long-term posttraumatic symptomatology in adolescents and point to necessary solutions that encompass consistent firearm violence prevention and mental health support. Results can inform future research, policy development, and intervention strategies to mitigate the adverse psychological effects of firearm violence on the vulnerable adolescent population.

2043 Background: Electronic health records (EHR) are used for retrospective cancer outcomes analysis. Sites and timing of recurrence are not captured in structured EHR data. Novel computerized methods are necessary to use unstructured longitudinal EHR data for large scale studies. Methods: We previously developed a neural network-based NLP algorithm to identify no recurrence vs. metastatic recurrence cases by analyzing physician notes, pathology and radiology reports in Stanford’s breast cancer database, Oncoshare (Cohort A). To validate this algorithm for local vs. distant recurrence, we identified a distinct Oncoshare cohort (Cohort B). Cases were manually curated for longitudinal development of local or distant recurrence and metastatic sites. A two-sided t-test was used to compare mean probabilities between local and distant recurrence cases. Next, we combined cases in Cohorts A and B to train and validate a novel NLP classifier that identifies metastatic site. The combined cohort was randomly divided into training and validation sets. Sensitivity and specificity were calculated for the NLP algorithm’s ability to detect metastatic sites compared to manual curation. Results: In Cohort B: 350 metastatic cases were identified. Mean probability for local and distant recurrence was 0.43 and 0.79, respectively and differed significantly for patients with local vs. distant recurrence (p<0.01). In Cohorts A and B: 632 metastatic cases were used for determination of sites. Sensitivity and specificity were highest for detection of peritoneal metastasis followed by liver, lung, skin, bone and central nervous system (table). Conclusions: This NLP algorithm is a scalable tool that uses unstructured EHR data to capture breast cancer recurrence, distinguishing local from distant recurrence and identifying metastatic site. This method may facilitate analysis of large datasets and correlation of outcomes with metastatic site. [Table: see text]

Mixed-methods evaluation of three natural language processing modeling approaches for measuring documented goals-of-care discussions in the electronic health record

Abstract. To Electronic health records (EHRs) are a treasure trove of patient information, including clinical notes. However, extracting meaningful insights from these unstructured narratives remains a challenge. This paper explores the potential of natural language processing (NLP) and deep learning to unlock the rich clinical narrative data and revolutionize healthcare. Clinical notes capture a wealth of information beyond structured data points. They document patient history, symptoms, treatment progress, and physician observations. Analyzing this narrative unlocks a deeper understanding of a patient's health journey, aiding in NLP can identify subtle patterns in language usage that might be indicative of specific diseases. This can assist clinicians in arriving at more accurate diagnoses, particularly for complex cases. Deep learning models can analyze narratives to identify patients at higher risk of developing certain conditions. This allows for proactive interventions and preventative measures. Extracting details about a patient's lifestyle, social context, and emotional well-being from narratives can facilitate the development of personalized treatment plans that cater to individual needs. NLP techniques like named entity recognition, sentiment analysis, and topic modeling enable the extraction of key clinical entities, emotions, and themes from narratives. Deep learning, with its ability to learn complex relationships in text data, empowers NLP tasks. Deep learning models can be trained on massive datasets of clinical notes, achieving superior performance in tasks like information extraction compared to traditional NLP methods. Deep learning models can handle the vast amount of text data generated in healthcare settings, enabling large- scale analysis of clinical narratives. By learning from diverse patient populations, deep learning models can improve their generalizability and adapt to variations in language usage within clinical documentation. Integrating NLP and deep learning into clinical workflows promises a future where the patient narrative is not just documented but actively analyzed to inform better healthcare decisions. This can lead to earlier diagnoses, more effective treatments, and ultimately, improved patient outcomes. By addressing the challenges and fostering responsible development, we can unlock the true potential of clinical narratives and empower a new era of data-driven healthcare.

BackgroundThe increasing adoption of electronic health records (EHRs) in clinical practice holds the promise of improving care and advancing research by serving as a rich source of data, but most EHRs allow clinicians to enter data in a text format without much structure. Natural language processing (NLP) may reduce reliance on manual abstraction of these text data by extracting clinical features directly from unstructured clinical digital text data and converting them into structured data.ObjectiveThis study aimed to assess the performance of a commercially available NLP tool for extracting clinical features from free-text consult notes.MethodsWe conducted a pilot, retrospective, cross-sectional study of the accuracy of NLP from dictated consult notes from our tuberculosis clinic with manual chart abstraction as the reference standard. Consult notes for 130 patients were extracted and processed using NLP. We extracted 15 clinical features from these consult notes and grouped them a priori into categories of simple, moderate, and complex for analysis.ResultsFor the primary outcome of overall accuracy, NLP performed best for features classified as simple, achieving an overall accuracy of 96% (95% CI 94.3-97.6). Performance was slightly lower for features of moderate clinical and linguistic complexity at 93% (95% CI 91.1-94.4), and lowest for complex features at 91% (95% CI 87.3-93.1).ConclusionsThe findings of this study support the use of NLP for extracting clinical features from dictated consult notes in the setting of a tuberculosis clinic. Further research is needed to fully establish the validity of NLP for this and other purposes.