Discovering themes in medical records of patients with psychogenic non-epileptic seizures

Lay, Joshua; Seneviratne, Udaya; Fok, Anthony; Roberts, Helene; Phan, Thanh G.

doi:10.1136/bmjno-2020-000087

Cited by 4 publications

(1 citation statement)

References 24 publications

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“…This process can be performed both in both unsupervised and supervised ways. The former approach has the obvious advantage of eliminating the tedious annotation phase ( 63 ). The latter requires the development of a pipeline based on a supervised algorithm using tools, such as cTAKES ( 64 ).…”

Section: Resultsmentioning

confidence: 99%

Natural language processing in clinical neuroscience and psychiatry: A review

et al. 2022

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Natural language processing (NLP) is rapidly becoming an important topic in the medical community. The ability to automatically analyze any type of medical document could be the key factor to fully exploit the data it contains. Cutting-edge artificial intelligence (AI) architectures, particularly machine learning and deep learning, have begun to be applied to this topic and have yielded promising results. We conducted a literature search for 1,024 papers that used NLP technology in neuroscience and psychiatry from 2010 to early 2022. After a selection process, 115 papers were evaluated. Each publication was classified into one of three categories: information extraction, classification, and data inference. Automated understanding of clinical reports in electronic health records has the potential to improve healthcare delivery. Overall, the performance of NLP applications is high, with an average F1-score and AUC above 85%. We also derived a composite measure in the form of Z-scores to better compare the performance of NLP models and their different classes as a whole. No statistical differences were found in the unbiased comparison. Strong asymmetry between English and non-English models, difficulty in obtaining high-quality annotated data, and train biases causing low generalizability are the main limitations. This review suggests that NLP could be an effective tool to help clinicians gain insights from medical reports, clinical research forms, and more, making NLP an effective tool to improve the quality of healthcare services.

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Section: Resultsmentioning

confidence: 99%

Natural language processing in clinical neuroscience and psychiatry: A review

et al. 2022

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Applying text-mining to clinical notes: The identification of patient characteristics from Electronic Health Records (EHRs)

Hoope,

Welvaars,

Geijtenbeek

et al. 2024

Preprint

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Background Clinical notes contain information on critical patient characteristics, which, if overlooked, could escalate the risk of adverse events as well as miscommunication between the healthcare professional and the patient. This study investigates the feasibility of employing text-mining to extract patient characteristics from Electronic Health Records (EHRs) and compares the effectiveness of text-mining against human intelligence for identifying four patient characteristics: language barrier, living alone, cognitive frailty and non-adherence. Methods A manual “golden” standard was created from 1,120 patient files (878 patients) that had unplanned hospital readmissions. Each patient was categorized under one of the four characteristics with supporting free text extracted from their EHRs. For simple terminology, a rule-based (RB) SQL query was used, and for complex terms, Named Entity Recognition (NER) models were used. Model performance was compared to the manual standard. The primary outcomes were recall, specificity, precision, negative predictive value (NPV) and F1-score. Results Performance of each patient characteristic was evaluated using a separate train/test dataset and an additional evaluation dataset was used for the NER models. Within the train/test set, the ‘language barrier’ RB query achieved a recall of 0.99 (specificity of 0.96). The ‘living alone’ NER model showed a recall of 0.86 (specificity of 0.94) on the train/test set and a recall of 0.81 (specificity of 1.00) on the validation set. In that same order, the ‘cognitive frailty’ model yielded a recall of 0.59 (specificity 0.76) on the train/test set and a recall of 0.73 (specificity 0.96) on the validation set. The NER model for non-adherence showed a recall of 0.75 (specificity of 0.99) on the train/test set, and a recall of 0.90 (specificity of 0.99) on the validation set. The models showed the tendency to overestimate the presence of patient characteristics such as identifying a family member’s language barrier as the patient’s. Conclusion This study successfully demonstrated the feasibility of applying text-mining to identify patient characteristics from EHRs. Also, it seems for more complex terminology, NER models outperform the rule-based option. Future work involves refining these models for broader application and terminology in clinical setting.

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Direct Clinical Applications of Natural Language Processing in Common Neurological Disorders: Scoping Review

Lefkovitz,

Walsh,

Blank

et al. 2024

JMIR Neurotech

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Background Natural language processing (NLP), a branch of artificial intelligence that analyzes unstructured language, is being increasingly used in health care. However, the extent to which NLP has been formally studied in neurological disorders remains unclear. Objective We sought to characterize studies that applied NLP to the diagnosis, prediction, or treatment of common neurological disorders. Methods This review followed the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) standards. The search was conducted using MEDLINE and Embase on May 11, 2022. Studies of NLP use in migraine, Parkinson disease, Alzheimer disease, stroke and transient ischemic attack, epilepsy, or multiple sclerosis were included. We excluded conference abstracts, review papers, as well as studies involving heterogeneous clinical populations or indirect clinical uses of NLP. Study characteristics were extracted and analyzed using descriptive statistics. We did not aggregate measurements of performance in our review due to the high variability in study outcomes, which is the main limitation of the study. Results In total, 916 studies were identified, of which 41 (4.5%) met all eligibility criteria and were included in the final review. Of the 41 included studies, the most frequently represented disorders were stroke and transient ischemic attack (n=20, 49%), followed by epilepsy (n=10, 24%), Alzheimer disease (n=6, 15%), and multiple sclerosis (n=5, 12%). We found no studies of NLP use in migraine or Parkinson disease that met our eligibility criteria. The main objective of NLP was diagnosis (n=20, 49%), followed by disease phenotyping (n=17, 41%), prognostication (n=9, 22%), and treatment (n=4, 10%). In total, 18 (44%) studies used only machine learning approaches, 6 (15%) used only rule-based methods, and 17 (41%) used both. Conclusions We found that NLP was most commonly applied for diagnosis, implying a potential role for NLP in augmenting diagnostic accuracy in settings with limited access to neurological expertise. We also found several gaps in neurological NLP research, with few to no studies addressing certain disorders, which may suggest additional areas of inquiry. Trial Registration Prospective Register of Systematic Reviews (PROSPERO) CRD42021228703; https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=228703

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Discovering themes in medical records of patients with psychogenic non-epileptic seizures

Cited by 4 publications

References 24 publications

Natural language processing in clinical neuroscience and psychiatry: A review

Natural language processing in clinical neuroscience and psychiatry: A review

Applying text-mining to clinical notes: The identification of patient characteristics from Electronic Health Records (EHRs)

Direct Clinical Applications of Natural Language Processing in Common Neurological Disorders: Scoping Review

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