Towards Constructing a Corpus for Studying the Effects of Treatments and Substances Reported in PubMed Abstracts

We present the construction of an annotated corpus of PubMed abstracts reporting about positive, negative or neutral effects of treatments or substances. Our ultimate goal is to annotate one sentence (rationale) for each abstract and to use this resource as a training set for text classification of effects discussed in PubMed abstracts. Currently, the corpus consists of 750 abstracts. We describe the automatic processing that supports the corpus construction, the manual annotation activities and some features of the medical language in the abstracts selected for the annotated corpus. It turns out that recognizing the terminology and the abbreviations is key for determining the rationale sentence. The corpus will be applied to improve our classifier, which currently has accuracy of 78.80% achieved with normalization of the abstract terms based on UMLS concepts from specific semantic groups and an SVM with a linear kernel. Finally, we discuss some other possible applications of this corpus.Comment: medical relation extraction, rationale extraction, effects and treatments, bioNL

An exploration of the psychosocial effects that school-age children with Child Absence Epilepsy (CAE) experience when their condition is misdiagnosed as Attention-Deficit/Hyperactivity Disorder (ADHD)

In today's society, the misdiagnosis of school-age children with the neurological condition Child Absence Epilepsy (CAE) as having Attention-Deficit/Hyperactivity Disorder (ADHD) has a low profile. This may be because of the lack of popular awareness of CAE. The increasing social salience towards the neuropsychological condition of ADHD places these children at risk of experiencing high psychosocial effects. Many symptoms of CAE are also associated with ADHD. However if the condition is misdiagnosed as ADHD, the child with CAE is often mistreated, both medically and socially until the correct diagnosis is made. There is little research available on the psychosocial effects of the misdiagnosis of epilepsy as ADHD, and none available relating to CAE. This research study uses case study methodology to focus on how children with CAE are psychosocially affected at the time of the misdiagnosis of ADHD and subsequently. It also explores the experiences of their parents. An in-depth interview method was adopted to gather the personal recollections of these effects directly from the ten participants in this study. The participants were found with the assistance of Epilepsy Australia and constituted one adolescent from five different families who had experienced the sequence of events and effects under investigation, and a parent (guardian) who cared for these children during this period. The findings of this research indicate that as a result of labelling, these children were misjudged in their communities, leaving strong psychosocial effects on each of the child participants who had previously been misdiagnosed with ADHD. These effects include low self-esteem, insecurity and fear experienced most often in the company of peers. As a result, when reaching adulthood, most of these participants chose to isolate themselves from social contact whenever possible. The findings offer a basis for further research in the area

An exploration of the psychosocial effects that school-age children with Child Absence Epilepsy (CAE) experience when their condition is misdiagnosed as Attention-Deficit/Hyperactivity Disorder (ADHD)

In today's society, the misdiagnosis of school-age children with the neurological condition Child Absence Epilepsy (CAE) as having Attention-Deficit/Hyperactivity Disorder (ADHD) has a low profile. This may be because of the lack of popular awareness of CAE. The increasing social salience towards the neuropsychological condition of ADHD places these children at risk of experiencing high psychosocial effects. Many symptoms of CAE are also associated with ADHD. However if the condition is misdiagnosed as ADHD, the child with CAE is often mistreated, both medically and socially until the correct diagnosis is made. There is little research available on the psychosocial effects of the misdiagnosis of epilepsy as ADHD, and none available relating to CAE. This research study uses case study methodology to focus on how children with CAE are psychosocially affected at the time of the misdiagnosis of ADHD and subsequently. It also explores the experiences of their parents. An in-depth interview method was adopted to gather the personal recollections of these effects directly from the ten participants in this study. The participants were found with the assistance of Epilepsy Australia and constituted one adolescent from five different families who had experienced the sequence of events and effects under investigation, and a parent (guardian) who cared for these children during this period. The findings of this research indicate that as a result of labelling, these children were misjudged in their communities, leaving strong psychosocial effects on each of the child participants who had previously been misdiagnosed with ADHD. These effects include low self-esteem, insecurity and fear experienced most often in the company of peers. As a result, when reaching adulthood, most of these participants chose to isolate themselves from social contact whenever possible. The findings offer a basis for further research in the area

Epilepsy Res

Objectives:The Epilepsy Learning Healthcare System (ELHS) was created in 2018 to address measurable improvements in outcomes for people with epilepsy. However, fragmentation of data systems has been a major barrier for reporting and participation. In this study, we aimed to test the feasibility of an open-source Data Integration (DI) method that connects real-life clinical data to national research and quality improvement (QI) systems.Methods:The ELHS case report forms were programmed as EPIC SmartPhrases at Mass General Brigham (MGB) in December 2018 and subsequently as EPIC SmartForms in June 2021 to collect actionable, standardized, structured epilepsy data in the electronic health record (EHR) for subsequent pull into the external national registry of the ELHS. Following the QI methodology in the Chronic Care Model, 39 providers, epileptologists and neurologists, incorporated the ELHS SmartPhrase into their clinical workflow, focusing on collecting diagnosis of epilepsy, seizure type according to the International League Against Epilepsy, seizure frequency, date of last seizure, medication adherence and side effects. The collected data was stored in the Enterprise Data Warehouse (EDW) without integration with external systems. We developed and validated a DI method that extracted the data from EDW using structured query language and later preprocessed using text mining. We used the ELHS data dictionary to match fields in the preprocessed notes to obtain the final structured dataset with seizure control information. For illustration, we described the data curated from the care period of 12/2018\u201312/2021.Results:The cohort comprised a total of 1806 patients with a mean age of 43 years old (SD: 17.0), where 57% were female, 80% were white, and 84% were non-Hispanic/Latino. Using our DI method, we automated the data mining, preprocessing, and exporting of the structured dataset into a local database, to be weekly accessible to clinicians and quality improvers. During the period of SmartPhrase implementation, there were 5168 clinic visits logged by providers documenting each patient\u2019s seizure type and frequency. During this period, providers documented 59% patients having focal seizures, 35% having generalized seizures and 6% patients having another type. Of the cohort, 45% patients had private insurance. The resulting structured dataset was bulk uploaded via web interface into the external national registry of the ELHS.Conclusions:Structured data can be feasibly extracted from text notes of epilepsy patients for weekly reporting to a national learning healthcare system.K08 AG053380/AG/NIA NIH HHSUnited States/R01 NS102190/NS/NINDS NIH HHSUnited States/RF1 NS120947/NS/NINDS NIH HHSUnited States/U48 DP006377/DP/NCCDPHP CDC HHSUnited States/R01 AG062282/AG/NIA NIH HHSUnited States/K08 NS118107/NS/NINDS NIH HHSUnited States/R01 AG073410/AG/NIA NIH HHSUnited States/P01 AG032952/AG/NIA NIH HHSUnited States/R01 NS107291/NS/NINDS NIH HHSUnited States

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

Despite improved ancillary investigations in epilepsy care, patients' narratives remain indispensable for diagnosing and treatment monitoring. This wealth of information is typically stored in electronic health records and accumulated in medical journals in an unstructured manner, thereby restricting complete utilization in clinical decision-making. To this end, clinical researchers increasing apply natural language processing (NLP)—a branch of artificial intelligence—as it removes ambiguity, derives context, and imbues standardized meaning from free-narrative clinical texts. This systematic review presents an overview of the current NLP applications in epilepsy and discusses the opportunities and drawbacks of NLP alongside its future implications. We searched the PubMed and Embase databases with a “natural language processing” and “epilepsy” query (March 4, 2022) and included original research articles describing the application of NLP techniques for textual analysis in epilepsy. Twenty-six studies were included. Fifty-eight percent of these studies used NLP to classify clinical records into predefined categories, improving patient identification and treatment decisions. Other applications of NLP had structured clinical information retrieval from electronic health records, scientific papers, and online posts of patients. Challenges and opportunities of NLP applications for enhancing epilepsy care and research are discussed. The field could further benefit from NLP by replicating successes in other health care domains, such as NLP-aided quality evaluation for clinical decision-making, outcome prediction, and clinical record summarization

Gender differences in core symptomatology in autism spectrum disorders across the lifespan

A preponderance of males with autism spectrum disorders (ASD) has been evident since the initial writings on the topic. This male predominance has consistently emerged in all ASD research to date in epidemiological as well as clinical populations. Despite this long recognized gender disparity in ASD, surprisingly there is a paucity of research addressing gender as it relates to core ASD symptom presentation. Gender differences may manifest with regard to symptom domains, severity, breadth, and so forth. The present research examined gender differences in ASD symptomatology in three populations: infants and toddlers at risk for developmental disability, children and adolescents, and adults with intellectual disability (ID). No significant gender differences in ASD symptoms were found in the infant/toddler or child/adolescent populations. In the adult population, in participants with ID alone, females had higher endorsements of social (i.e., participation in social games, sports, and activities; interest in other’s side of the conversation; and imitation) and communication (i.e., interest in other\u27s side of the conversation and reading body language) impairments compared to males. This study has considerable implications in both the clinical and research realms regarding identification and intervention issues for females with ASD, as well as stimulating a future research agenda in this area

Use of Conventional Machine Learning to Optimize Deep Learning Hyper-parameters for NLP Labeling Tasks

Deep learning delivers good performance in classification tasks, but is suboptimal with small and unbalanced datasets, which are common in many domains. To address this limitation, we use conventional machine learning, i.e., support vector machines (SVM) to tune deep learning hyper-parameters. We evaluated our approach using mental health electronic health records in which diagnostic criteria needed to extracted. A bidirectional Long Short-Term Memory network (BI-LSTM) could not learn the labels for the seven scarcest classes, but saw an increase in performance after training with optimal weights learned from tuning SVMs. With these customized class weights, the F1 scores for rare classes rose from 0 to values ranging from 18% to 57%. Overall, the BI-LSTM with SVM customized class weights achieved a micro-average of 47.1% for F1 across all classes, an improvement over the regular BI-LSTM’s 45.9%. The main contribution lies in avoiding null performance for rare classes

The correlation between giftedness and autism spectrum disorder: a systematic review

Σκοπός: Κατά την πάροδο των ετών, πολλοί μύθοι έχουν συσχετίσει την ύπαρξη νευροαναπτυξιακών διαταραχών με εξαιρετικές ικανότητες σε συγκεκριμένους τομείς, ανάλογα με τη διαταραχή. Από την άλλη μεριά, όπως περιγράφεται στη βιβλιογραφία, οι ιδιαίτερες ικανότητες και ταλέντα που μπορεί να κατέχουν παιδιά με νευροαναπτυξιακές διαταραχές συχνά επισκιάζονται από τις δυσκολίες τους και παραβλέπονται, τόσο από τους γονείς όσο και από τους εκπαιδευτικούς. Σκοπός της συστηματικής αυτής ανασκόπησης ήταν η συλλογή και μελέτη της υπάρχουσας βιβλιογραφίας, προκειμένου να διερευνηθεί η συσχετιση της χαρισματικότητας με κάθε είδους νευροαναπτυξιακή διαταραχή. Η παρούσα μελέτη παρουσιάζει τα αποτελέσματα της συστηματικής ανασκόπησης που φορούν στη συσχέτιση μεταξύ χαρισματικότητας και Διαταραχής Αυτιστικού Φάσματος (ΔΑΦ). Υλικά και Μέθοδος: Έγινε συστηματική ανασκόπηση των άρθρων έχουν δημοσιευτεί στα PubMed, Google Scholar, PsycInfo και Embase έως τον Δεκέμβριο του 2020, καθώς και των βιβλιογραφικών αναφορών τους. Αποτελέσματα: Συνολικά 6069 αξιολογήθηκαν και 32 από αυτά (συνολικά 9904 υποκείμενα) κρίθηκαν επιλέξιμα για τη συγκεκριμένη συστηματική ανασκόπηση. Δέκα από τις επιλέξιμες μελέτες μελετούσαν τη συσχέτιση μεταξύ χαρισματικότητας και Διαταραχής Αυτιστικού Φάσματος (ΔΑΦ). Από τις ανωτέρω μελέτες διαφαίνεται συσχέτιση μεταξύ ΔΑΦ και μουσικής αντίληψης, Συμπεράσματα:. Περισσότερη έρευνα πάνω στο συγκεκριμένο θέμα κρίνεται απαραίτητη. Απαιτείται η διενέργεια διαχρονικών μελετών, ώστε ξεπεραστούν μεθοδολογικές δυσκολίες που σχετίζονται με την ετερογένεια των ορισμών της χαρισματικότητας.Objective: Throughout the years, several myths have arisen suggesting that children diagnosed with neurodevelopmental disorders possess unusually high abilities in specific domains, depending on the disorder. On the other hand, special skills and talents in children with developmental disorders are most commonly overshadowed by their difficulties and overlooked. The purpose of this systematic review was to examine the association between giftedness and each neurodevelopmental disorder in particular. The present study focuses on the correlation between giftedness and Autism Spectrum Disorders (ASD) and presents the results of the above systematic review concerning the two identities. Materials and Methods: The related articles published in PubMed, Google Scholar, PsycInfo and Embase up to 31 December 2020, as well as their reference lists, were reviewed systematically. Results: A total of 6069 studies were scanned and 32 of them (9904 subjects) were deemed eligible for this systematic review. Ten of the eligible articles were investigating the correlation between giftedness and ASD. Studies have supported associations between Autism Spectrum Disorders and music ability. Conclusion: More research is needed to investigate the field of dual exceptionality in children with ASD. Longitudinal studies are needed, overcoming methodological challenges related to variability in the definition of giftedness