ArticleNeurology. Clinical practice2021
Predictive Modeling for Clinical Features Associated With Neurofibromatosis Type 1.
Article in Neurology. Clinical practice, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
13 citing papers in PubMed, 16 citations in OpenAlex.
- Harmonizing Multi-Institutional Clinical Documentation Using Natural Language Processing in Neurofibromatosis Type 1.Neurology. Clinical practice · 2026Observational
- Artificial intelligence in neurofibromatosis type I: diagnostic and therapeutic opportunities.Journal of applied genetics · 2026Review
- Integration of fairness-awareness into clinical language processing models.Communications medicine · 2026Article
- Comparison of rule- and large language model-based phenotype extraction from clinical notes for neurofibromatosis type 1.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Artificial intelligence-based tools for precision diagnosis and treatment of neurofibromatosis type 1 associated peripheral and central glial tumors.Orphanet journal of rare diseases · 2025Review
- Brain Metastases from Primary Cardiac Tumors: A Systematic Review of Diagnosis, Treatment, and Prognosis.Cancers · 2025Review
- Cognition and behavior in neurofibromatosis type 1: report and perspective from the Cognition and Behavior in NF1 (CABIN) Task Force.Genes & development · 2025Article
- Pulmonary Arterial Hypertension in Neurofibromatosis Type 1: A Case with a Novel NF1 Gene Mutation.Internal medicine (Tokyo, Japan) · 2025Article
- Evaluating dimensionality reduction of comorbidities for predictive modeling in individuals with neurofibromatosis type 1.JAMIA open · 2025Article
- Assessing the capture of sociodemographic information in electronic medical records to inform clinical decision making.PloS one · 2025Article
- Predicting neurodevelopmental disorders using machine learning models and electronic health records - status of the field.Journal of neurodevelopmental disorders · 2024Review
- Perinatal folate levels do not influence tumor latency or multiplicity in a model of NF1 associated plexiform-like neurofibromas.BMC research notes · 2023Article
- Neurofibromatosis Type 1-Associated Optic Pathway Gliomas: Current Challenges and Future Prospects.Cancer management and research · 2023Review
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
objectiveTo perform a longitudinal analysis of clinical features associated with neurofibromatosis type 1 (NF1) based on demographic and clinical characteristics and to apply a machine learning strategy to determine feasibility of developing exploratory predictive models of optic pathway glioma (OPG) and attention-deficit/hyperactivity disorder (ADHD) in a pediatric NF1 cohort.
methodsUsing NF1 as a model system, we perform retrospective data analyses using a manually curated NF1 clinical registry and electronic health record (EHR) information and develop machine learning models. Data for 798 individuals were available, with 578 comprising the pediatric cohort used for analysis.
resultsMales and females were evenly represented in the cohort. White children were more likely to develop OPG (odds ratio [OR]: 2.11, 95% confidence interval [CI]: 1.11-4.00,
conclusionsUsing readily available clinical and EHR data, we successfully recapitulated several important and clinically relevant patterns in NF1 semiology specifically based on demographic and clinical characteristics. Naive machine learning techniques can be potentially used to develop and validate predictive phenotype complexes applicable to risk stratification and disease management in NF1.
Identifiers
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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.