Natural language processing for geriatric syndromes: a systematic review of methods, applications, and challenges.

To examine the value of unstructured electronic health record (EHR) data (free-text notes) in identifying a set of geriatric syndromes. Retrospective analysis of unstructured EHR notes using a natural language processing (NLP) algorithm. Large multispecialty group. Older adults (N=18,341; average age 75.9, 58.9% female). We compared the number of geriatric syndrome cases identified using structured claims and structured and unstructured EHR data. We also calculated these rates using a population-level claims database as a reference and identified comparable epidemiological rates in peer-reviewed literature as a benchmark. Using insurance claims data resulted in a geriatric syndrome prevalence ranging from 0.03% for lack of social support to 8.3% for walking difficulty. Using structured EHR data resulted in similar prevalence rates, ranging from 0.03% for malnutrition to 7.85% for walking difficulty. Incorporating unstructured EHR notes, enabled by applying the NLP algorithm, identified considerably higher rates of geriatric syndromes: absence of fecal control (2.1%, 2.3 times as much as structured claims and EHR data combined), decubitus ulcer (1.4%, 1.7 times as much), dementia (6.7%, 1.5 times as much), falls (23.6%, 3.2 times as much), malnutrition (2.5%, 18.0 times as much), lack of social support (29.8%, 455.9 times as much), urinary retention (4.2%, 3.9 times as much), vision impairment (6.2%, 7.4 times as much), weight loss (19.2%, 2.9 as much), and walking difficulty (36.34%, 3.4 as much). The geriatric syndrome rates extracted from structured data were substantially lower than published epidemiological rates, although adding the NLP results considerably closed this gap. Claims and structured EHR data give an incomplete picture of burden related to geriatric syndromes. Geriatric syndromes are likely to be missed if unstructured data are not analyzed. Pragmatic NLP algorithms can assist with identifying individuals at high risk of experiencing geriatric syndromes and improving coordination of care for older adults.

BackgroundConventional prognostic scores usually require predefined clinical variables to predict outcome. The advancement of natural language processing has made it feasible to derive meaning from unstructured data. We aimed to test whether using unstructured text in electronic health records can improve the prediction of functional outcome after acute ischemic stroke.Methods and ResultsPatients hospitalized for acute ischemic stroke were identified from 2 hospital stroke registries (3847 and 2668 patients, respectively). Prediction models developed using the first cohort were externally validated using the second cohort, and vice versa. Free text in the history of present illness and computed tomography reports was used to build machine learning models using natural language processing to predict poor functional outcome at 90 days poststroke. Four conventional prognostic models were used as baseline models. The area under the receiver operating characteristic curves of the model using history of present illness in the internal and external validation sets were 0.820 and 0.792, respectively, which were comparable to the National Institutes of Health Stroke Scale score (0.811 and 0.807). The model using computed tomography reports achieved area under the receiver operating characteristic curves of 0.758 and 0.658. Adding information from clinical text significantly improved the predictive performance of each baseline model in terms of area under the receiver operating characteristic curves, net reclassification improvement, and integrated discrimination improvement indices (all P<0.001). Swapping the study cohorts led to similar results.ConclusionsBy using natural language processing, unstructured text in electronic health records can provide an alternative tool for stroke prognostication, and even enhance the performance of existing prognostic scores.

Background. There is growing evidence that social and behavioral determinants of health (SBDH) play a substantial effect in a wide range of health outcomes. Electronic health records (EHRs) have been widely employed to conduct observational studies in the age of artificial intelligence (AI). However, there has been limited review into how to make the most of SBDH information from EHRs using AI approaches.Methods. A systematic search was conducted in six databases to find relevant peer-reviewed publications that had recently been published. Relevance was determined by screening and evaluating the articles. Based on selected relevant studies, a methodological analysis of AI algorithms leveraging SBDH information in EHR data was provided.Results. Our synthesis was driven by an analysis of SBDH categories, the relationship between SBDH and healthcare-related statuses, natural language processing (NLP) approaches for extracting SBDH from clinical notes, and predictive models using SBDH for health outcomes.Discussion. The associations between SBDH and health outcomes are complicated and diverse; several pathways may be involved. Using NLP technology to support the extraction of SBDH and other clinical ideas simplifies the identification and extraction of essential concepts from clinical data, efficiently unlocks unstructured data, and aids in the resolution of unstructured data-related issues.Conclusion. Despite known associations between SBDH and diseases, SBDH factors are rarely investigated as interventions to improve patient outcomes. Gaining knowledge about SBDH and how SBDH data can be collected from EHRs using NLP approaches and predictive models improves the chances of influencing health policy change for patient wellness, ultimately promoting health and health equity.

Purpose To address the feasibility, reliability and internal validity of natural language processing (NLP) for automated functional assessment of hospitalised COVID-19 patients in key International Classification of Functioning, Disability and Health (ICF) categories and levels from unstructured text in electronic health records (EHR) from a large teaching hospital. Materials and methods Eight human annotators assigned four ICF categories to relevant sentences: Emotional functions, Exercise tolerance, Walking and Moving, Work and Employment and their ICF levels (Functional Ambulation Categories for Walking and Moving, metabolic equivalents for Exercise tolerance). A linguistic neural network-based model was trained on 80% of the annotated sentences; inter-annotator agreement (IAA, Cohen’s kappa), a weighted score of precision and recall (F1) and RMSE for level detection were assessed for the remaining 20%. Results In total 4112 sentences of non-COVID-19 and 1061 of COVID-19 patients were annotated. Average IAA was 0.81; F1 scores were 0.7 for Walking and Moving and Emotional functions; RMSE for Walking and Moving (5- level scale) was 1.17 for COVID-19 patients. Conclusion Using a limited amount of annotated EHR sentences, a proof-of-concept was obtained for automated functional assessment of COVID-19 patients in ICF categories and levels. This allows for instantaneous assessment of the functional consequences of new diseases like COVID-19 for large numbers of patients. Key messages Hospitalised Covid-19 survivors may persistently suffer from low physical and mental functioning and a reduction in overall quality of life requiring appropriate and personalised rehabilitation strategies. For this, assessment of functioning within multiple domains and categories of the International Classification of Function is required, which is cumbersome using structured data. We show a proof-of-concept using Natural Language Processing techniques to automatically derive the aforementioned information from free-text notes within the Electronic Health Record of a large academic teaching hospital.

ObjectiveTo develop scalable natural language processing (NLP) infrastructure for processing the free text in electronic health records (EHRs).Materials and MethodsWe extend the open-source Apache cTAKES NLP software with several standard technologies for scalability. We remove processing bottlenecks by monitoring component queue size. We process EHR free text for patients in the PrecisionLink Biobank at Boston Children’s Hospital. The extracted concepts are made searchable via a web-based portal.ResultsWe processed over 1.2 million notes for over 8000 patients, extracting 154 million concepts. Our largest tested configuration processes over 1 million notes per day.DiscussionThe unique information represented by extracted NLP concepts has great potential to provide a more complete picture of patient status.ConclusionNLP large EHR document collections can be done efficiently, in service of high throughput phenotyping.

The majority of clinical information stored in Spanish healthcare systems is found as unstructured text in electronic health records (EHRs). The automatic extraction of valuable information contained in these documents is a critical task. Valuable information for oncology clinical analysis units and Real-World Evidence studies includes the location of the neoplasm presented by a patient. This location, included in the ICD-10 coding category, can be extracted from the texts by natural language processing (NLP). This study set out to explore the classification of medical documents in Spanish for the purpose of extracting the location of the patient's primary neoplasm. A private corpus composed of 23,704 real clinical EHRs was utilised. The prediction problem was approached through a classification of 12 primary organ groupings and 29 specific locations. In order to achieve this, four NLP methodologies were developed: traditional machine learning (ML); ensemble ML; recurrent neural networks (RNN); and Transformers-based models. Our findings demonstrate that traditional ML models exhibit superior performance when compared to RNNs and Transformers. Models such as XGBoost and SVMs demonstrate remarkable efficacy, attaining an F1-score of 0.938 for the 12-class classification and an F1-score of 0.838 for the 29-class specific classification, respectively. The pre-trained RoBERTa-Base-Biomed model, which incorporates medical and clinical corpora in Spanish, demonstrates an F1-score of 0.808 for the 29-location problem. However, the Transformer model exhibits superior performance than the rest of the approaches when dealing with an external corpus, indicating a higher generalisation capacity.

Few at-risk adults are identified by specialized services prior to the development of a first episode of psychosis. A transdiagnostic risk calculator, predicting psychosis using electronic health record (EHR) data, was developed in London, UK to identify patients at risk, using structured data and 14 natural language processing (NLP)-derived symptom and substance use concepts. We report the adaptation and internal validation of this risk calculator in a Southeast England region. In a retrospective cohort study using EHR patient notes we identified individuals accessing mental healthcare in Southeast England (Nov-1992 to Jan-2023) who received a primary diagnosis of a non-psychotic or non-organic mental disorder. We developed new machine-learning NLP algorithms for diagnosis, symptom and substance use concepts by fine-tuning existing open-source transformer models. Baseline and outcome coded diagnoses were supplemented with NLP-derived diagnosis data. Cox regression was used to predict psychosis and prior weights were applied; discrimination (Harrell's C) was assessed. Nearly all NLP concepts achieved an F1-measure of accuracy above 0.8 following development. In an analysis sample of 63,922 patients with complete data, the risk calculator had acceptable but lower accuracy in Southeast England (Harrell's C 0.71) compared to the London benchmark (Harrell's C 0.85). The risk calculator performed similarly in Southeast England to other external validation studies, discriminating acceptably, suggesting that this calculator may be adapted successfully for new patient populations, services and geographic areas. Differences in accuracy may be due to different cultures of data capture, different NLP approaches, or differences in the patient cohort.

Nowadays, the dissemination of news information has become more rapid, liberal, and open to the public. People can find what they want to know more and more easily from a variety of sources, including traditional news outlets and new social media platforms. However, at a time when our lives are glutted with all kinds of news, we cannot help but doubt the veracity and legitimacy of these news sources; meanwhile, we also need to guard against the possible impact of various forms of fake news. To combat the spread of misinformation, more and more researchers have turned to natural language processing (NLP) approaches for effective fake news detection. However, in the face of increasingly serious fake news events, existing detection methods still need to be continuously improved. This study proposes a modified proof-of-concept model named NER-SA, which integrates natural language processing (NLP) and named entity recognition (NER) to conduct the in-domain and cross-domain analysis of fake news detection with the existing three datasets simultaneously. The named entities associated with any particular news event exist in a finite and available evidence pool. Therefore, entities must be mentioned and recognized in this entity bank in any authentic news articles. A piece of fake news inevitably includes only some entitlements in the entity bank. The false information is deliberately fabricated with fictitious, imaginary, and even unreasonable sentences and content. As a result, there must be differences in statements, writing logic, and style between legitimate news and fake news, meaning that it is possible to successfully detect fake news. We developed a mathematical model and used the simulated annealing algorithm to find the optimal legitimate area. Comparing the detection performance of the NER-SA model with current state-of-the-art models proposed in other studies, we found that the NER-SA model indeed has superior performance in detecting fake news. For in-domain analysis, the accuracy increased by an average of 8.94% on the LIAR dataset and 19.36% on the fake or real news dataset, while the F1-score increased by an average of 24.04% on the LIAR dataset and 19.36% on the fake or real news dataset. In cross-domain analysis, the accuracy and F1-score for the NER-SA model increased by an average of 28.51% and 24.54%, respectively, across six domains in the FakeNews AMT dataset. The findings and implications of this study are further discussed with regard to their significance for improving accuracy, understanding context, and addressing adversarial attacks. The development of stylometric detection based on NLP approaches using NER techniques can improve the effectiveness and applicability of fake news detection.

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...

e13591 Background: Relapse is a major concern for oncologists and breast cancer survivors that necessitates additional treatment and often leads to mortality. Cancer registries routinely track cancer mortality, but few monitor for relapse because of logistical challenges and prohibitive costs. In this context, Natural Language Processing (NLP) is a promising tool. Merging artificial intelligence with linguistics, NLP can rapidly analyze vast volumes of text in electronic health records. This capability of NLP is particularly valuable for Computed Tomography (CT) scans used in breast cancer care. CT scans are routinely used to characterize breast cancer progression and are described in transcribed dictations by radiologists. We aimed to apply NLP to these text reports to identify breast cancer relapses. Objective: To automate breast cancer relapse detection and classification in CT text reports using NLP. Methods: We analyzed 1,445 CT text reports from patients diagnosed with breast cancer between January 1, 2005, and December 31, 2014. These reports underwent manual review by trained human annotators. Text was annotated to identify terminology defining local, regional, and distant breast cancer relapses. Annotated reports were partitioned into a training-validation set (90% cohort) and a test set (10% cohort) for NLP model development. Results: In our dataset of 1,445 CT text reports, 72 (5.0%) were classified as local relapse, 97 (6.7%) as regional relapse, and 743 (51.4%) as distant relapse. The performance of our NLP model using the training-validation dataset can be summarized by the following metrics and 95% confidence intervals: 94% (±3.2) accuracy for detection and 96% (±2.9) accuracy for classification. The performance of our NLP model was confirmed using the test dataset, with 90% (±4.5) accuracy for detection and 91% (±6.3) accuracy for classification. For reference, all metrics are outlined (Table). Conclusions: Our model for identifying regional and distant relapses in CT reports had excellent performance, but had lower sensitivity for local relapses, posing a risk of false negatives. Automating the identification and classification of breast cancer relapses, if used retrospectively, can enhance cancer registry data about patient outcomes and, if used prospectively, holds the potential for enhancing patient care. [Table: see text]

Natural Language Processing (NLP) has rapidly evolved into a core enabler of enterprise digital transformation, empowering organizations to automate workflows, extract insights from unstructured text, and enhance data-driven decisionmaking processes. This paper presents a comprehensive survey of current NLP technologies, emphasizing their practical integration within enterprise environments. We review the development of large-scale pre-trained models such as BERT, GPT-3, and domain-specific variants, highlighting their capabilities in applications including sentiment analysis, intelligent document processing, regulatory compliance monitoring, customer service automation, and risk assessment across sectors such as healthcare, finance, and retail. Key deployment considerations such as cloud infrastructure, container orchestration, data pipelines, and governance strategies are critically analyzed to guide scalable and secure implementation. Furthermore, we synthesize existing costaware ROI evaluation approaches to assess the financial viability and business value of NLP deployments, integrating performance metrics, operational costs, and strategic benefits. Real-world industrial case studies are discussed, demonstrating best practices and challenges in deploying NLP systems at scale, including issues of model drift, explainability, data privacy compliance, and integration with enterprise IT ecosystems. Additionally, we explore emerging trends such as multilingual adaptation, voiceenabled NLP, and environmentally sustainable AI deployment strategies. This survey aims to provide researchers, practitioners, and decision-makers with a structured understanding of how NLP technologies can be effectively leveraged to deliver measurable business outcomes, enhance organizational efficiency, and drive strategic innovation in real-world enterprise settings. This paper reviews existing ROI evaluation approaches and deployment strategies for NLP in enterprises.

Electronic health records (EHRs) are a rich source of health information; however social determinants of health, including incarceration, and how they impact health and health care disparities can be hard to extract. The main objective of this study was to compare sensitivity and specificity of patient self-report with various methods of identifying incarceration exposure using the EHR. Validation study using multiple data sources and types. Participants of the Veterans Aging Cohort Study (VACS), a national observational cohort based on data from the Veterans Health Administration (VHA) EHR that includes all human immunodeficiency virus-infected patients in care (47,805) and uninfected patients (99,060) matched on region, age, race/ethnicity, and sex. Self-reported incarceration history compared with: (1) linked VHA EHR data to administrative data from a state Department of Correction (DOC), (2) linked VHA EHR data to administrative data on incarceration from Centers for Medicare and Medicaid Services (CMS), (3) VHA EHR-specific identifier codes indicative of receipt of VHA incarceration reentry services, and (4) natural language processing (NLP) in unstructured text in VHA EHR. Linking the EHR to DOC data: sensitivity 2.5%, specificity 100%; linking the EHR to CMS data: sensitivity 7.9%, specificity 99.3%; VHA EHR-specific identifier for receipt of reentry services: sensitivity 7.3%, specificity 98.9%; and NLP, sensitivity 63.5%, specificity 95.9%. NLP tools hold promise as a feasible and valid method to identify individuals with exposure to incarceration in EHR. Future work should expand this approach using a larger body of documents and refinement of the methods, which may further improve operating characteristics of this method.

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 &gt; 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.

Requirements engineering is the first phase in software development life cycle and it also plays one of the most important and critical roles. Requirement document mainly contains both functional requirements and non-functional requirements. Non-functional requirements are significant to describe the properties and constraints of the system. Early identification of Non-functional requirement has direct impact on the system architecture and initial design decision. Practically, non-functional requirements are extracted manually from the document. This makes it tedious, time-consuming task and prone to various errors. In this paper, we propose an automatic approach to identify and classify non-functional requirements using semantic and syntactic analysis with machine learning approaches from unconstrained documents. We used A dataset of public requirements documents (PURE) that consists of 79 unconstrained requirements documents in different forms. In our approach, features were extracted from the requirement sentences using four different natural language processing methods including statistical and state-ofthe-art semantic analysis presented by Google word2vec and bidirectional encoder representations from transformers models. The adopted approach can efficiently classify non-functional requirements with an accuracy between 84% and 87% using statistical vectorization method and 88% to 92% using word embedding semantic methods. Furthermore, by fusing different models trained on different features, the accuracy improves by 2.4% compared with the best individual classifier. INDEX TERMS Software requirement, Machine learning, Natural language processing.

This systematic review evaluated natural language processing (NLP) approaches for detecting cognitive impairment in electronic health record clinical notes. Following PRISMA guidelines, we analyzed 18 studies (n = 1,064,530) that employed rule-based algorithms (67%), traditional machine learning (28%), and deep learning (17%). NLP models demonstrated robust performance in identifying cognitive decline, with median sensitivity 0.88 (IQR 0.74–0.91) and specificity 0.96 (IQR 0.81–0.99). Deep learning architectures achieved superior results, with area under the receiver operating characteristic curves up to 0.997. Major implementation challenges included incomplete electronic health record data capture, inconsistent clinical documentation practices, and limited external validation. While NLP demonstrates promise, successful clinical translation requires establishing standardized approaches, improving access to annotated datasets, and developing equitable deployment frameworks.