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Transforming epilepsy research: A systematic review on natural language processing applications

EPILEPSIA (2023)

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Extracting seizure frequency from epilepsy clinic notes: a machine reading approach to natural language processing

Kevin Xie et al.

Summary: The study aimed to automatically extract clinical information from unstructured text in clinical notes for epilepsy patients, with the potential to improve clinical decision-making and allow for large-scale retrospective research. Pretrained neural models achieved near-human performance in classifying patients as seizure-free and extracting seizure frequency and date of last seizure after finetuning. This novel machine reading approach opens new possibilities for clinical practice and research.

JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION (2022)

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Article Mathematical & Computational Biology

Ontology-based identification and prioritization of candidate drugs for epilepsy from literature

Bernd Mueller et al.

Summary: This study demonstrates the use of biomedical ontologies and text mining to identify drug names related to epilepsy in scientific literature. The ranking of these drug names enables a quick evaluation of statistical evidence in the literature and validates approaches in the drug discovery process.

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Article Computer Science, Artificial Intelligence

A multi-objective memetic algorithm for query-oriented text summarization: Medicine texts as a case study

Jesus M. Sanchez-Gomez et al.

Summary: The paper presents a new approach for query-oriented extractive multi-document text summarization using Multi-Objective Shuffled Frog-Leaping Algorithm (MOSFLA), achieving significant improvements in ROUGE scores. Experimental results demonstrate important enhancements compared to existing works in scientific literature.

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Article Oncology

Natural language processing with machine learning to predict outcomes after ovarian cancer surgery

Emma L. Barber et al.

Summary: The study utilized natural language processing and machine learning combined with discrete data predictors to predict postoperative complication and hospital readmission among women with ovarian cancer. The results showed that this approach improved the ability to predict these outcomes.

GYNECOLOGIC ONCOLOGY (2021)

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Review Clinical Neurology

Fostering reproducibility and generalizability in machine learning for clinical prediction modeling in spine surgery

Tej D. Azad et al.

Summary: With the increasing use of machine learning algorithms in the development of clinical prediction models, researchers are realizing the deleterious effects that can stem from the lack of reporting standards. In order to improve reproducibility and clinical performance, efforts have been made to characterize methods that can ensure technical, statistical, and conceptual reproducibility. These efforts are crucial before the models are applied to patient care.

SPINE JOURNAL (2021)

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Article Biochemical Research Methods

Text mining approaches for dealing with the rapidly expanding literature on COVID-19

Lucy Lu Wang et al.

Summary: This review discusses the resources and systems for text mining applications over the COVID-19 literature, compiling a list of 39 systems that provide search, discovery, visualization, and summarization functions. While many systems focus on search and discovery, some provide unique features such as summarizing findings over multiple documents or linking between scientific articles and clinical trials.

BRIEFINGS IN BIOINFORMATICS (2021)

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Article Clinical Neurology

Autopsy-reported cause of death in a population-based cohort of sudden unexpected death in epilepsy

Anne E. Keller et al.

Summary: Most SUDEP cases identified by neurologists were accurately classified by forensic pathologists in Ontario, Canada; however, concordance decreased with increased case complexity. Age was significantly associated with SUDEP classification, with Definite cases being younger than Definite Plus, which were younger than Possible SUDEP cases. This study highlights the importance of reviewing autopsy reports in research studies and assessing the public health impact of SUDEP.

EPILEPSIA (2021)

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Article Computer Science, Information Systems

Can reproducibility be improved in clinical natural language processing? A study of 7 clinical NLP suites

William Digan et al.

Summary: The research evaluated the impact of workflow management systems and other bioinformatics practices on the reproducibility of clinical natural language processing (NLP) frameworks. It found that frameworks based on WMS had better compliance with reproducibility features, and identified 34 recommendations endorsed by at least 2 articles. Each NLP framework adopted 15 features based on the literature.

JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION (2021)

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A fast, accurate, and generalisable heuristic-based negation detection algorithm for clinical text

Luke T. Slater et al.

Summary: The study presents a heuristic algorithm for negation detection based on dependency graphs, showing strong performance in clinical text mining without the need for complex rule development and adaptation. Comparing with other rule-based algorithms, the algorithm may overlook advanced cases in certain situations, but it remains a fast, powerful, and stable alternative method.

COMPUTERS IN BIOLOGY AND MEDICINE (2021)

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Article Behavioral Sciences

Insights into the real-life experiences of people living with epilepsy: A qualitative netnographic study

Barbara Fazekas et al.

Summary: The qualitative netnographic study of epilepsy conversations on public social media sites revealed key themes such as disease awareness, impact of seizures on mental and physical health, importance of proper sleep, disease burden over time, treatment options and side effects, and management of depression and anxiety among people living with epilepsy. The study highlighted the need for greater attention to the mental health impact of epilepsy and the importance of addressing issues related to treatment and care.

EPILEPSY & BEHAVIOR (2021)

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Article Clinical Neurology

Feasibility of using an automated analysis of formulation effort in patients' spoken seizure descriptions in the differential diagnosis of epileptic and nonepileptic seizures

Nathan Pevy et al.

Summary: This study investigated the differentiation between epileptic and PNES based on patients' descriptions of their first seizure. The results showed that there were more hesitations and repetitions in descriptions of epileptic seizures, and a Random Forest classifier was able to correctly classify 71% of the seizures.

SEIZURE-EUROPEAN JOURNAL OF EPILEPSY (2021)

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Article Clinical Neurology

Early identification of epilepsy surgery candidates: A multicenter, machine learning study

Benjamin D. Wissel et al.

Summary: The study shows that site-specific machine learning algorithms can identify candidates for epilepsy surgery early in the disease course in diverse practice settings.

ACTA NEUROLOGICA SCANDINAVICA (2021)

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Article Biology

The OpenNeuro resource for sharing of neuroscience data

Christopher J. Markiewicz et al.

Summary: The importance of sharing research data for reproducibility and maximizing the impact of public investment in scientific research is highlighted. OpenNeuro, as a BRAIN Initiative data archive, follows FAIR principles for openly sharing various brain imaging data types. The Brain Imaging Data Structure standard is emphasized for effective curation, sharing, and reuse of data.

ELIFE (2021)

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Deep learning-based natural language processing in ophthalmology: applications, challenges and future directions

Lily Wei Yun Yang et al.

Summary: Artificial intelligence (AI) is viewed as the fourth industrial revolution, with natural language processing (NLP) being a type of AI that transforms human language for computer interpretation. In healthcare, NLP holds promise for various applications but also presents potential challenges. Collaboration, acceptance, and technology advancement will shape the future of NLP in healthcare and ophthalmology, aiming for more equitable and generalizable use for societal benefit.

CURRENT OPINION IN OPHTHALMOLOGY (2021)

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Article Genetics & Heredity

Improving early diagnosis of rare diseases using Natural Language Processing in unstructured medical records: an illustration from Dravet syndrome

Tommaso Lo Barco et al.

Summary: This study found significant differences in clinical concepts between individuals with Dravet Syndrome and Febrile Seizures before the age of 2, which can help improve early diagnosis and guide towards appropriate care pathways. The use of natural language processing technology in analyzing electronic health records presents a promising approach for early detection of rare diseases such as Dravet Syndrome.

ORPHANET JOURNAL OF RARE DISEASES (2021)

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Machine Learning for Localizing Epileptogenic-Zone in the Temporal Lobe: Quantifying the Value of Multimodal Clinical-Semiology and Imaging Concordance

Ali Alim-Marvasti et al.

Summary: The study used machine learning models to localize epileptogenic zones in epilepsy surgery patients, showing that combining symptoms and MRI features can enhance performance in lobe localization. Despite good performance in localization, the models were not able to predict seizure-freedom better than benchmarks.

FRONTIERS IN DIGITAL HEALTH (2021)

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Prospective validation of a machine learning model that uses provider notes to identify candidates for resective epilepsy surgery

Benjamin D. Wissel et al.

EPILEPSIA (2020)

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Identifying epilepsy psychiatric comorbidities with machine learning

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ACTA NEUROLOGICA SCANDINAVICA (2020)

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Challenges to the Reproducibility of Machine Learning Models in Health Care

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Young Juhn et al.

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Digital conversations about suicide among teenagers and adults with epilepsy: A big-data, machine learning analysis

Tatiana Falcone et al.

EPILEPSIA (2020)

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Clinical concept extraction: A methodology review

Sunyang Fu et al.

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Glimpsing the Impact of COVID19 Lock-Down on People With Epilepsy: A Text Mining Approach

Jacopo Lanzone et al.

FRONTIERS IN NEUROLOGY (2020)

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Review Cell Biology

Named Entity Recognition and Relation Detection for Biomedical Information Extraction

Nadeesha Perera et al.

FRONTIERS IN CELL AND DEVELOPMENTAL BIOLOGY (2020)

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2018 n2c2 shared task on adverse drug events and medication extraction in electronic health records

Sam Henry et al.

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Extracting medications and associated adverse drug events using a natural language processing system combining knowledge base and deep learning

Long Chen et al.

JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION (2020)

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Article Computer Science, Theory & Methods

A Survey on Deep Learning: Algorithms, Techniques, and Applications

Samira Pouyanfar et al.

ACM COMPUTING SURVEYS (2019)

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Review Medicine, General & Internal

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Roland D. Thijs et al.

LANCET (2019)

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Article Clinical Neurology

Automated detection of sudden unexpected death in epilepsy risk factors in electronic medical records using natural language processing

Kristen Barbour et al.

EPILEPSIA (2019)

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Article Behavioral Sciences

Understanding the patient perspective of epilepsy treatment through text mining of online patient support groups

Kai He et al.

EPILEPSY & BEHAVIOR (2019)

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Article Medicine, General & Internal

Using natural language processing to extract structured epilepsy data from unstructured clinic letters: development and validation of the ExECT (extraction of epilepsy clinical text) system

Beata Fonferko-Shadrach et al.

BMJ OPEN (2019)

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Article Clinical Neurology

Investigation of bias in an epilepsy machine learning algorithm trained on physician notes

Benjamin D. Wissel et al.

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Article Health Care Sciences & Services

Augmenting Qualitative Text Analysis with Natural Language Processing: Methodological Study

Timothy C. Guetterman et al.

JOURNAL OF MEDICAL INTERNET RESEARCH (2018)

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Article Respiratory System

Automated chart review utilizing natural language processing algorithm for asthma predictive index

Harsheen Kaur et al.

BMC PULMONARY MEDICINE (2018)

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Using clinical Natural Language Processing for health outcomes research: Overview and actionable suggestions for future advances

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JOURNAL OF BIOMEDICAL INFORMATICS (2018)

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CLINICAL RESEARCH IN CARDIOLOGY (2017)

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Operational classification of seizure types by the International League Against Epilepsy: Position Paper of the ILAE Commission for Classification and Terminology

Robert S. Fisher et al.

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Social media in epilepsy: A quantitative and qualitative analysis

Ying Meng et al.

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Text mining applications in psychiatry: a systematic literature review

Adeline Abbe et al.

INTERNATIONAL JOURNAL OF METHODS IN PSYCHIATRIC RESEARCH (2016)

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Advances in the development of biomarkers for epilepsy

Asla Pitkanen et al.

LANCET NEUROLOGY (2016)

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Comorbidities of epilepsy: current concepts and future perspectives

Mark R. Keezer et al.

LANCET NEUROLOGY (2016)

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Review Radiology, Nuclear Medicine & Medical Imaging

Natural Language Processing in Radiology: A Systematic Review

Ewoud Pons et al.

RADIOLOGY (2016)

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Methodological Issues in Predicting Pediatric Epilepsy Surgery Candidates Through Natural Language Processing and Machine Learning

Kevin Bretonnel Cohen et al.

BIOMEDICAL INFORMATICS INSIGHTS (2016)

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HARVEST, a longitudinal patient record summarizer

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JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION (2015)

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Licong Cui et al.

JOURNAL OF BIOMEDICAL INFORMATICS (2014)

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Assessing the similarity of surface linguistic features related to epilepsy across pediatric hospitals

Brian Connolly et al.

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Article Behavioral Sciences

Validating a natural language processing tool to exclude psychogenic nonepileptic seizures in electronic medical record-based epilepsy research

H. Hamid et al.

EPILEPSY & BEHAVIOR (2013)

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Article Computer Science, Interdisciplinary Applications

Natural language processing: State of the art and prospects for significant progress, a workshop sponsored by the National Library of Medicine

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Article Mathematical & Computational Biology

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Article Clinical Neurology

Common data elements in epilepsy research: Development and implementation of the NINDS epilepsy CDE project

David W. Loring et al.

EPILEPSIA (2011)

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