Characterization of patients with idiopathic normal pressure hydrocephalus using natural language processing within an electronic healthcare record system

Funnell, Jonathan P.; Noor, Kawsar; Khan, Danyal Z.; D’Antona, Linda; Dobson, Richard; Hanrahan, John; Hepworth, Christopher; Moncur, E.; Thomas, Benjamin M.; Thorne, Lewis; Watkins, Laurence; Williams, Simon C.; Wong, Wai Keong; Toma, Ahmed K; Marcus, Hani J.

doi:10.3171/2022.9.jns221095

Cited by 5 publications

(5 citation statements)

References 30 publications

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“…The model used is derived from previous work conducted on capturing symptoms for patients with Normal Pressure Hydrocephalus. 8 We describe the model training process below.…”

Section: Cogstack Model and Training Of Medcat Platformmentioning

confidence: 99%

“…Third, our work followed an established methodology for the application of CogStack to a neurosurgical problem. 8 Further, this research aimed to counter reporting heterogeneity in the literature of NLP applications in health care by adhering to measures of quality published by Mellia et al, as a by-proxy reporting checklist. 7 This study has several limitations.…”

Section: Strengths and Limitationsmentioning

confidence: 99%

“…7 For example, our group have substantiated the efficacy of NLP in identifying concepts associated with normal pressure hydrocephalus, which may in future aid timely diagnosis and discrimination from mimics. 8 This study aims to identify text concepts associated with surgically managed VS using an NLP model (CogStack) through analysis of free-text clinical data from patient EHRs, to create a unique concept panel associated with surgically managed VS. Resultantly, we aim to highlight how NLP may be applied in a surgical health care setting using VS as an exemplar. Finally, through worked example, we aim to demonstrate how NLP may be used to predict postoperative cerebrospinal fluid (CSF) leak in VS patients.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Concept Recognition and Characterization of Patients Undergoing Resection of Vestibular Schwannoma Using Natural Language Processing

Marcus,

Grover,

Williams

et al. 2024

J Neurol Surg B Skull Base

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Background Natural language processing (NLP), a subset of artificial intelligence (AI), aims to decipher unstructured human language. This study showcases NLP's application in surgical health care, focusing on vestibular schwannoma (VS). By employing an NLP platform, we identify prevalent text concepts in VS patients' electronic health care records (EHRs), creating concept panels covering symptomatology, comorbidities, and management. Through a case study, we illustrate NLP's potential in predicting postoperative cerebrospinal fluid (CSF) leaks. Methods An NLP model analyzed EHRs of surgically managed VS patients from 2008 to 2018 in a single center. The model underwent unsupervised (trained on one million documents from EHR) and supervised (300 documents annotated in duplicate) learning phases, extracting text concepts and generating concept panels related to symptoms, comorbidities, and management. Statistical analysis correlated concept occurrences with postoperative complications, notably CSF leaks. Results Analysis included 292 patients' records, yielding 6,901 unique concepts and 360,929 occurrences. Concept panels highlighted key associations with postoperative CSF leaks, including “antibiotics,” “sepsis,” and “intensive care unit admission.” The NLP model demonstrated high accuracy (precision 0.92, recall 0.96, macro F1 0.93). Conclusion Our NLP model effectively extracted concepts from VS patients' EHRs, facilitating personalized concept panels with diverse applications. NLP shows promise in surgical settings, aiding in early diagnosis, complication prediction, and patient care. Further validation of NLP's predictive capabilities is warranted.

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“…The model used is derived from previous work conducted on capturing symptoms for patients with Normal Pressure Hydrocephalus. 8 We describe the model training process below.…”

Section: Cogstack Model and Training Of Medcat Platformmentioning

confidence: 99%

Section: Strengths and Limitationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Concept Recognition and Characterization of Patients Undergoing Resection of Vestibular Schwannoma Using Natural Language Processing

Marcus,

Grover,

Williams

et al. 2024

J Neurol Surg B Skull Base

Self Cite

View full text Add to dashboard Cite

show abstract

“…In the model by Funnell et al for predicting normal pressure hydrocephalus from primary care records, the suggested workflow involves automated flagging by an ML model followed by clinical review and advanced testing. 47 Moreover, model interpretability is a key component to inculcating humantechnology trust in an ML model.…”

Section: Does the Model Integrate Well Into Clinical Workflows?mentioning

confidence: 99%

Critically reading machine learning literature in neurosurgery: a reader’s guide and checklist for appraising prediction models

Emani

Swaminathan

Grobman

et al. 2023

Neurosurgical Focus

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OBJECTIVE Machine learning (ML) has become an increasingly popular tool for use in neurosurgical research. The number of publications and interest in the field have recently seen significant expansion in both quantity and complexity. However, this also places a commensurate burden on the general neurosurgical readership to appraise this literature and decide if these algorithms can be effectively translated into practice. To this end, the authors sought to review the burgeoning neurosurgical ML literature and to develop a checklist to help readers critically review and digest this work. METHODS The authors performed a literature search of recent ML papers in the PubMed database with the terms "neurosurgery" AND "machine learning," with additional modifiers "trauma," "cancer," "pediatric," and "spine" also used to ensure a diverse selection of relevant papers within the field. Papers were reviewed for their ML methodology, including the formulation of the clinical problem, data acquisition, data preprocessing, model development, model validation, model performance, and model deployment. RESULTS The resulting checklist consists of 14 key questions for critically appraising ML models and development techniques; these are organized according to their timing along the standard ML workflow. In addition, the authors provide an overview of the ML development process, as well as a review of key terms, models, and concepts referenced in the literature. CONCLUSIONS ML is poised to become an increasingly important part of neurosurgical research and clinical care. The authors hope that dissemination of education on ML techniques will help neurosurgeons to critically review new research better and more effectively integrate this technology into their practices.

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Early evaluation of a natural language processing tool to improve access to educational resources for surgical patients

Booker,

Penn,

Noor

et al. 2024

Eur Spine J

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Purpose Accessible patient information sources are vital in educating patients about the benefits and risks of spinal surgery, which is crucial for obtaining informed consent. We aim to assess the effectiveness of a natural language processing (NLP) pipeline in recognizing surgical procedures from clinic letters and linking this with educational resources. Methods Retrospective examination of letters from patients seeking surgery for degenerative spinal disease at a single neurosurgical center. We utilized MedCAT, a named entity recognition and linking NLP, integrated into the electronic health record (EHR), which extracts concepts and links them to systematized nomenclature of medicine-clinical terms (SNOMED-CT). Investigators reviewed clinic letters, identifying words or phrases that described or identified operations and recording the SNOMED-CT terms as ground truth. This was compared to SNOMED-CT terms identified by the model, untrained on our dataset. A pipeline linking clinic letters to patient-specific educational resources was established, and precision, recall, and F1 scores were calculated. Results Across 199 letters the model identified 582 surgical procedures, and the overall pipeline after adding rules a total of 784 procedures (precision = 0.94, recall = 0.86, F1 = 0.91). Across 187 letters with identified SNOMED-CT terms the integrated pipeline linking education resources directly to the EHR was successful in 157 (78%) patients (precision = 0.99, recall = 0.87, F1 = 0.92). Conclusions NLP accurately identifies surgical procedures in pre-operative clinic letters within an untrained subspecialty. Performance varies among letter authors and depends on the language used by clinicians. The identified procedures can be linked to patient education resources, potentially improving patients’ understanding of surgical procedures.

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Characterization of patients with idiopathic normal pressure hydrocephalus using natural language processing within an electronic healthcare record system

Cited by 5 publications

References 30 publications

Concept Recognition and Characterization of Patients Undergoing Resection of Vestibular Schwannoma Using Natural Language Processing

Concept Recognition and Characterization of Patients Undergoing Resection of Vestibular Schwannoma Using Natural Language Processing

Critically reading machine learning literature in neurosurgery: a reader’s guide and checklist for appraising prediction models

Early evaluation of a natural language processing tool to improve access to educational resources for surgical patients

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