Development of a generalizable natural language processing pipeline to extract physician-reported pain from clinical reports: Generated using publicly-available datasets and tested on institutional clinical reports for cancer patients with bone metastases

Naseri, Hossein; Kafi, Kamran; Skamene, Sonia; Tolba, M. K.; Faye, Mame Daro; Ramia, Paul; Khriguian, Julia; Kildea, J.

doi:10.1016/j.jbi.2021.103864

Cited by 16 publications

(17 citation statements)

References 37 publications

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“…This study represents the application of a clinical text deep learning model in answering a clinical question related to pain in the ED. This methodology has been in development for four years (4, 7–9) and stems from other reports of the application of natural language processing to symptom identification in electronic health records (18–20). Emergency Medicine, with a large volume of undifferentiated patients, is an ideal setting for the deployment of artificial intelligence approaches to ED management, prediction of medical conditions and symptoms, patient acuity, deposition, and pre-hospital management (21, 22).…”

Section: Discussionmentioning

confidence: 99%

Analyzing Pain Patterns in the Emergency Department: Leveraging Clinical Text Deep Learning Models for Real-World Insights

Hughes,

Wu,

Jones

et al. 2023

Preprint

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Objective: To estimate the prevalence of patients presenting in pain to an inner-city emergency department (ED), describing this population, their treatment, and the effect of the COVID-19 pandemic. Materials and Methods: We applied a clinical text deep learning model to the free text nursing assessments to identify the prevalence of pain on arrival to the ED. Using interrupted time series analysis, we examined the prevalence over three years. We describe this population pre- and post-pandemic in terms of their demographics, arrival patterns and treatment. Results: 55.16% (95%CI 54.95% - 55.36%) of all patients presenting to this ED had pain on arrival. There were significant differences in demographics, arrival and departure patterns between those patients with and without pain. The COVID-19 pandemic initially precipitated a decrease followed by a sharp, sustained rise in the prevalence of pain on arrival, altering the population arriving in pain and their treatment. Discussion The application of a clinical text deep learning model has successfully identified the prevalence of pain on arrival. The description of this population and their treatment forms the basis of intervention to improve care for patients presenting with pain. The combination of the clinical text deep learning model and interrupted time series analysis has identified the effects of the COVID-19 pandemic on pain care in the ED. Conclusion A clinical text deep learning model has led to identifying the prevalence of pain on arrival and was able to identify the effect a major pandemic had on pain care in this ED.

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Section: Discussionmentioning

confidence: 99%

Analyzing Pain Patterns in the Emergency Department: Leveraging Clinical Text Deep Learning Models for Real-World Insights

Hughes,

Wu,

Jones

et al. 2023

Preprint

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“…This finding also suggests the value of continued work to improve EHR data quality, such as through health information exchange, 50,51 standards-based data collection, 52,53 and reliable natural language processing methods for extracting symptom and treatment information from unstructured clinical notes. 54,55 Moreover, as we continue to develop Tx Tracker, we will consider ways to effectively include other pain-relevant data types, such as imaging and nonprimary care specialist notes and reports. This study's finding also reinforced that the value of Tx Tracker will only be realized if it is well integrated with clinical workflows.…”

Section: Discussionmentioning

confidence: 99%

Evaluating a Prototype Clinical Decision Support Tool for Chronic Pain Treatment in Primary Care

et al. 2022

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Objectives The Chronic Pain Treatment Tracker (Tx Tracker) is a prototype decision support tool to aid primary care clinicians when caring for patients with chronic noncancer pain. This study evaluated clinicians' perceived utility of Tx Tracker in meeting information needs and identifying treatment options, and preferences for visual design. Methods We conducted 12 semi-structured interviews with primary care clinicians from four health systems in Indiana. The interviews were conducted in two waves, with prototype and interview guide revisions after the first six interviews. The interviews included exploration of Tx Tracker using a think-aloud approach and a clinical scenario. Clinicians were presented with a patient scenario and asked to use Tx Tracker to make a treatment recommendation. Last, participants answered several evaluation questions. Detailed field notes were collected, coded, and thematically analyzed by four analysts. Results We identified several themes: the need for clinicians to be presented with a comprehensive patient history, the usefulness of Tx Tracker in patient discussions about treatment planning, potential usefulness of Tx Tracker for patients with high uncertainty or risk, potential usefulness of Tx Tracker in aggregating scattered information, variability in expectations about workflows, skepticism about underlying electronic health record data quality, interest in using Tx Tracker to annotate or update information, interest in using Tx Tracker to translate information to clinical action, desire for interface with visual cues for risks, warnings, or treatment options, and desire for interactive functionality. Conclusion Tools like Tx Tracker, by aggregating key information about past, current, and potential future treatments, may help clinicians collaborate with their patients in choosing the best pain treatments. Still, the use and usefulness of Tx Tracker likely relies on continued improvement of its functionality, accurate and complete underlying data, and tailored integration with varying workflows, care team roles, and user preferences.

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“…Automatic extraction and classifcation of physician-reported pain from clinical notes in cancer patients [44] Diferent ML and ruled-based algorithms A systematic review on NLP for LBP [46] Body posture 16 actors posed in various body postures to depict pain, and 20 observers selected the most efective images. After validation, a set of 144 images was established "Head averted," "gaze downward," and "forward body lean" are common body postures for pain [49] Respiratory features ADABoost, XGBoost, RF, SVM, and KNN Features from PPG in postoperative patients [52] Fusion architectures 65 automatic respiratory features [53] CNN: convolutional neural network; MRI: magnetic resonance imaging; LSTM: long short-term memory network; RNN: recurrent neural network; ML: machine learning; NLP: natural language processing; LBP: low back pain; RF: random forest; SVM: support vector machine; KNN: k-nearest neighbors; PPG: photoplethysmography.…”

Section: Metamap and Negex Algorithmsmentioning

confidence: 99%

Artificial Intelligence for Automatic Pain Assessment: Research Methods and Perspectives

Cascella,

Schiavo,

Cuomo

et al. 2023

Pain Research and Management

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Although proper pain evaluation is mandatory for establishing the appropriate therapy, self-reported pain level assessment has several limitations. Data-driven artificial intelligence (AI) methods can be employed for research on automatic pain assessment (APA). The goal is the development of objective, standardized, and generalizable instruments useful for pain assessment in different clinical contexts. The purpose of this article is to discuss the state of the art of research and perspectives on APA applications in both research and clinical scenarios. Principles of AI functioning will be addressed. For narrative purposes, AI-based methods are grouped into behavioral-based approaches and neurophysiology-based pain detection methods. Since pain is generally accompanied by spontaneous facial behaviors, several approaches for APA are based on image classification and feature extraction. Language features through natural language strategies, body postures, and respiratory-derived elements are other investigated behavioral-based approaches. Neurophysiology-based pain detection is obtained through electroencephalography, electromyography, electrodermal activity, and other biosignals. Recent approaches involve multimode strategies by combining behaviors with neurophysiological findings. Concerning methods, early studies were conducted by machine learning algorithms such as support vector machine, decision tree, and random forest classifiers. More recently, artificial neural networks such as convolutional and recurrent neural network algorithms are implemented, even in combination. Collaboration programs involving clinicians and computer scientists must be aimed at structuring and processing robust datasets that can be used in various settings, from acute to different chronic pain conditions. Finally, it is crucial to apply the concepts of explainability and ethics when examining AI applications for pain research and management.

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Development of a generalizable natural language processing pipeline to extract physician-reported pain from clinical reports: Generated using publicly-available datasets and tested on institutional clinical reports for cancer patients with bone metastases

Cited by 16 publications

References 37 publications

Analyzing Pain Patterns in the Emergency Department: Leveraging Clinical Text Deep Learning Models for Real-World Insights

Analyzing Pain Patterns in the Emergency Department: Leveraging Clinical Text Deep Learning Models for Real-World Insights

Evaluating a Prototype Clinical Decision Support Tool for Chronic Pain Treatment in Primary Care

Artificial Intelligence for Automatic Pain Assessment: Research Methods and Perspectives

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