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Predicting seizure recurrence after an initial seizure-like episode from routine clinical notes using large language models: A retrospective cohort study
Background: The evaluation and management of first-time seizure-like events in children can be difficult because these episodes are not always directly observed and might be epileptic seizures or other conditions (seizure mimics). We aimed to evaluate whether machine learning models using real-world data could predict seizure recurrence after an initial seizure-like event. Methods: This retrospective cohort study compared models trained and evaluated on two separate datasets between Jan 1, 2010, and Jan 1, 2020: electronic medical records (EMRs) at Boston Children's Hospital and de-identified, patient-level, administrative claims data from the IBM MarketScan research database. The study population comprised patients with an initial diagnosis of either epilepsy or convulsions before the age of 21 years, based on International Classification of Diseases, Clinical Modification (ICD-CM) codes. We compared machine learning-based predictive modelling using structured data (logistic regression and XGBoost) with emerging techniques in natural language processing by use of large language models. Findings: The primary cohort comprised 14 021 patients at Boston Children's Hospital matching inclusion criteria with an initial seizure-like event and the comparison cohort comprised 15 062 patients within the IBM MarketScan research database. Seizure recurrence based on a composite expert-derived definition occurred in 57% of patients at Boston Children's Hospital and 63% of patients within IBM MarketScan. Large language models with additional domain-specific and location-specific pre-training on patients excluded from the study (F1-score 0路826 [95% CI 0路817-0路835], AUC 0路897 [95% CI 0路875-0路913]) performed best. All large language models, including the base model without additional pre-training (F1-score 0路739 [95% CI 0路738-0路741], AUROC 0路846 [95% CI 0路826-0路861]) outperformed models trained with structured data. With structured data only, XGBoost outperformed logistic regression and XGBoost models trained with the Boston Children's Hospital EMR (logistic regression: F1-score 0路650 [95% CI 0路643-0路657], AUC 0路694 [95% CI 0路685-0路705], XGBoost: F1-score 0路679 [0路676-0路683], AUC 0路725 [0路717-0路734]) performed similarly to models trained on the IBM MarketScan database (logistic regression: F1-score 0路596 [0路590-0路601], AUC 0路670 [0路664-0路675], XGBoost: F1-score 0路678 [0路668-0路687], AUC 0路710 [0路703-0路714]). Interpretation: Physician's clinical notes about an initial seizure-like event include substantial signals for prediction of seizure recurrence, and additional domain-specific and location-specific pre-training can significantly improve the performance of clinical large language models, even for specialised cohorts.</p
Data and sample sharing as an enabler for large-scale biomarker research and development: The EPND perspective
Biomarker discovery, development, and validation are reliant on large-scale analyses of high-quality samples and data. Currently, significant quantities of data and samples have been generated by European studies on Alzheimer's disease (AD) and other neurodegenerative diseases (NDD), representing a valuable resource for developing biomarkers to support early detection of disease, treatment monitoring, and patient stratification. However, discovery of, access to, and sharing of data and samples from AD and NDD research are hindered both by silos that limit collaboration, and by the array of complex requirements for secure, legal, and ethical sharing. In this Perspective article, we examine key challenges currently hampering large-scale biomarker research, and outline how the European Platform for Neurodegenerative Diseases (EPND) plans to address them. The first such challenge is a fragmented landscape filled with technical barriers that make it difficult to discover and access high-quality samples and data in one location. A second challenge is related to the complex array of legal and ethical requirements that must be navigated by researchers when sharing data and samples, to ensure compliance with data protection regulations and research ethics. Another challenge is the lack of broad-scale collaboration and opportunities to facilitate partnerships between data and sample contributors and researchers, in addition to a lack of regulatory engagement early in the research process to enable validation of potential biomarkers. A further challenge facing projects is the need to remain sustainable beyond initial funding periods, ensuring data and samples are shared and reused, thereby driving further research and innovation. In addressing these challenges, EPND will enable an environment of faster and more disruptive research on diagnostics and disease-modifying therapies for Alzheimer's disease and other neurodegenerative diseases
Data and sample sharing as an enabler for large-scale biomarker research and development: The EPND perspective
Biomarker discovery, development, and validation are reliant on large-scale analyses of high-quality samples and data. Currently, significant quantities of data and samples have been generated by European studies on Alzheimer's disease (AD) and other neurodegenerative diseases (NDD), representing a valuable resource for developing biomarkers to support early detection of disease, treatment monitoring, and patient stratification. However, discovery of, access to, and sharing of data and samples from AD and NDD research are hindered both by silos that limit collaboration, and by the array of complex requirements for secure, legal, and ethical sharing. In this Perspective article, we examine key challenges currently hampering large-scale biomarker research, and outline how the European Platform for Neurodegenerative Diseases (EPND) plans to address them. The first such challenge is a fragmented landscape filled with technical barriers that make it difficult to discover and access high-quality samples and data in one location. A second challenge is related to the complex array of legal and ethical requirements that must be navigated by researchers when sharing data and samples, to ensure compliance with data protection regulations and research ethics. Another challenge is the lack of broad-scale collaboration and opportunities to facilitate partnerships between data and sample contributors and researchers, in addition to a lack of regulatory engagement early in the research process to enable validation of potential biomarkers. A further challenge facing projects is the need to remain sustainable beyond initial funding periods, ensuring data and samples are shared and reused, thereby driving further research and innovation. In addressing these challenges, EPND will enable an environment of faster and more disruptive research on diagnostics and disease-modifying therapies for Alzheimer's disease and other neurodegenerative diseases
Additional file 1: of PDON: Parkinson鈥檚 disease ontology for representation and modeling of the Parkinson鈥檚 disease knowledge domain
Convergence of miRNA Expression Profiling, ?-颅Synuclein Interacton and GWAS in Parkinson's Disease. (DOCX 449 KB
The BIOMarkers in Atopic Dermatitis and Psoriasis (BIOMAP) Glossary: developing a lingua franca to facilitate data harmonisation and cross-cohort analyses
Dear Editor, BIOMAP (BIOMarkers in Atopic dermatitis and Psoriasis) is a large European consortium aiming to advance personalised medicine for atopic dermatitis and psoriasis by identifying biomarkers which predict therapeutic response and disease progression. BIOMAP brings together clinicians, researchers, patient organisations and pharmaceutical industry partners and encompasses data from over 60 individual studies, including randomised clinical trials, population-based cohorts and deeply-phenotyped disease registries. The curation and harmonisation of data and bio-samples from these established studies will facilitate cross-cohort clinical and molecular analyses, increasing the potential to identify small effect estimates and to better stratify disease subtypes. This letter serves to disseminate BIOMAP's pathway to data harmonisation and will inform future collaborative research endeavours