ArticleNeurology. Clinical practice2020
Machine learning as a diagnostic decision aid for patients with transient loss of consciousness.
Article in Neurology. Clinical practice, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed, 49 citations in OpenAlex.
- Unmet needs in functional neurological disorder related to digital healthcare.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2026Review
- Noise in the diagnosis of epilepsy by experts.Epileptic disorders : international epilepsy journal with videotape · 2026Article
- Quantifying the impact of a computer-aided diagnostic score on the clinical diagnosis of functional seizures.Epilepsia · 2026Article
- Pediatric reflex syncope: updated insights and future directions.European journal of pediatrics · 2026Review
- Assistive Artificial Intelligence in Epilepsy and Its Impact on Epilepsy Care in Low- and Middle-Income Countries.Brain sciences · 2025Review
- Validation of a Machine-Learning Clinical Decision Aid for the Differential Diagnosis of Transient Loss of Consciousness.Neurology. Clinical practice · 2025Article
- Supervised machine learning compared to large language models for identifying functional seizures from medical records.Epilepsia · 2025Article
- Will Artificial Intelligence Be "Better" Than Humans in the Management of Syncope?JACC. Advances · 2024Review
- The present and future of seizure detection, prediction, and forecasting with machine learning, including the future impact on clinical trials.Frontiers in neurology · 2024Review
- A complex case with generalized epilepsy, probable focal seizures, and functional seizures.Epilepsy & behavior reports · 2024Article
- Can Artificial Intelligence Enhance Syncope Management?: AJACC. Advances · 2023Review
- Epilepsy diagnosis using a clinical decision tool and artificially intelligent electroencephalography.Epilepsy & behavior : E&B · 2023Article
- Trauma and syncope: looking beyond the injury.Trauma surgery & acute care open · 2023Review
- Clinical Functional Seizure Score (CFSS): a simple algorithm for clinicians to suspect functional seizures.Frontiers in neurology · 2023Article
- The hermeneutics of symptoms.Medicine, health care, and philosophy · 2022Article
- Functional neurological disorder: new subtypes and shared mechanisms.The Lancet. Neurology · 2022Review
- Recent Updates and Technological Developments in Evaluating Cardiac Syncope in the Emergency Department.Current cardiology reviews · 2022Article
- Diagnosing Neurally Mediated Syncope Using Classification Techniques.Journal of clinical medicine · 2021Article
- A Sneak-Peek into the Physician's Brain: A Retrospective Machine Learning-Driven Investigation of Decision-Making in TAVR versus SAVR for Young High-Risk Patients with Severe Symptomatic Aortic Stenosis.Journal of personalized medicine · 2021Article
- Support Vector Machine-Based Classification of Vasovagal Syncope Using Head-Up Tilt Test.Biology · 2021Article
Corrections and comments
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Authors and funding
11 authors at 1 institution in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTransient loss of consciousness (TLOC) is a common reason for presentation to primary/emergency care; over 90% are because of epilepsy, syncope, or psychogenic non-epileptic seizures (PNES). Misdiagnoses are common, and there are currently no validated decision rules to aid diagnosis and management. We seek to explore the utility of machine-learning techniques to develop a short diagnostic instrument by extracting features with optimal discriminatory values from responses to detailed questionnaires about TLOC manifestations and comorbidities (86 questions to patients, 31 to TLOC witnesses).
methodsMulti-center retrospective self- and witness-report questionnaire study in secondary care settings. Feature selection was performed by an iterative algorithm based on random forest analysis. Data were randomly divided in a 2:1 ratio into training and validation sets (163:86 for all data; 208:92 for analysis excluding witness reports).
resultsThree hundred patients with proven diagnoses (100 each: epilepsy, syncope and PNES) were recruited from epilepsy and syncope services. Two hundred forty-nine completed patient and witness questionnaires: 86 epilepsy (64 female), 84 PNES (61 female), and 79 syncope (59 female). Responses to 36 questions optimally predicted diagnoses. A classifier trained on these features classified 74/86 (86.0% [95% confidence interval 76.9%-92.6%]) of patients correctly in validation (100 [86.7%-100%] syncope, 85.7 [67.3%-96.0%] epilepsy, 75.0 [56.6%-88.5%] PNES). Excluding witness reports, 34 features provided optimal prediction (classifier accuracy of 72/92 [78.3 (68.4%-86.2%)] in validation, 83.8 [68.0%-93.8%] syncope, 81.5 [61.9%-93.7%] epilepsy, 67.9 [47.7%-84.1%] PNES).
conclusionsA tool based on patient symptoms/comorbidities and witness reports separates well between syncope and other common causes of TLOC. It can help to differentiate epilepsy and PNES. Validated decision rules may improve diagnostic processes and reduce misdiagnosis rates. CLASSIFICATION OF EVIDENCE: This study provides Class III evidence that for patients with TLOC, patient and witness questionnaires discriminate between syncope, epilepsy and PNES.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.