Gold Dataset Research Articles

The consequence of higher-order curvature-based constraints on [formula omitted] gravity

In this investigation, we perform an observational statistical analysis in the theory of f(R,Lm) gravity. The proposed theoretical model is based on the Ricci scalar’s non-linear contribution. We use a distinct parametrization for the deceleration parameter and constrain the model parameters by using various observational data. To determine the best-fit model for the cosmological parameters, we use different observational datasets such as the Hubble Space Telescope, the Pantheon Supernova Survey, the Gold dataset, the Gamma-Ray Burst (GRB), and the Baryon Acoustic Oscillations (BAO). Furthermore, we study the late-time cosmic evolution of the Universe in detail and examine the implications of the constraint values on cosmological parameters. Additionally, we conduct a thorough comparison with the standard cosmological model ΛCDM and other standard models obtained by Odintsov et al. (2024); Odintsov et al. (2023) to examine the validity of our proposed model in the low-redshift regimes. Finally, we find that the proposed model encapsulates an intriguing transition from early deceleration at high redshift to acceleration at low redshift, a quintessence dark energy scenario, and convergence towards the well-established ΛCDM model in late-time Universe’s evolution.

Improving the artificial bee colony algorithm with a proprietary estimation of distribution mechanism for protein–ligand docking

The protein–ligand docking problem plays an essential role in structure-based drug design. The challenge for a protein–ligand docking method is how to execute an efficient conformational search to explore a well-designed scoring function. In this study, we improved the artificial bee colony (ABC) algorithm and proposed an approach called ABC-EDM to solve the protein–ligand docking problem. ABC-EDM employs the scoring function of the classical AutoDock Vina to evaluate a solution during docking simulation. ABC-EDM adopts the search framework of the canonical ABC algorithm to execute conformational search. By further investigating the characteristics of the protein–ligand docking problem, a proprietary search mechanism inspired by estimation of distribution algorithm, i.e., estimation of distribution mechanism (EDM), is designed to enhance the performance of ABC-EDM. To verify the effectiveness of the proposed ABC-EDM, we compare it with three variants of the ABC algorithm, three evolutionary computation algorithms, and AutoDock Vina. The experimental results show that ABC-EDM can effectively solve the protein–ligand docking problem, and it can achieve a success rate 5% higher than AutoDock Vina on the GOLD dataset. This study reveals that taking advantage of problem-specific information about the protein–ligand docking problem to enhance a docking method contributes to solving this problem.

Predicting incident heart failure from population-based nationwide electronic health records: protocol for a model development and validation study

IntroductionHeart failure (HF) is increasingly common and associated with excess morbidity, mortality, and healthcare costs. Treatment of HF can alter the disease trajectory and reduce clinical events in HF. However,...

Emotion Detection using Twitter Datasets and Spacy Algorithm

People show emotions for everyday communication. Emotions are identified by facial expressions, behavior, writing, speaking, gestures and physical actions. Emotion plays a vital role in the interaction between two people. The detection of emotions through text is a challenge for researchers. Emotion detection from the text can be useful for real-world application. Automatic emotion detection in the original text aims to recognize emotions in any digital medium by using natural language processing techniques and different approaches. Enabling machines with the ability to recognize emotions in a particular kind of text such as twitter’s tweet has important applications in sentiment analysis and affective computing. We have worked on the newly published gold dataset (AIT-2018) and propose a model consisting of lexicalbased using WordNet-Affect and EmoSenticNet with supervised classifiers for detecting emotions in a tweet text.

Risk of atrial fibrillation and association with other diseases: protocol of the derivation and international external validation of a prediction model using nationwide population-based electronic health records

IntroductionAtrial fibrillation (AF) is a major public health issue and there is rationale for the early diagnosis of AF before the first complication occurs. Previous AF screening research is limited...

Text augmentation for semantic frame induction and parsing

Semantic frames are formal structures describing situations, actions or events, e.g., Commerce buy, Kidnapping, or Exchange. Each frame provides a set of frame elements or semantic roles corresponding to participants of the situation and lexical units (LUs)—words and phrases that can evoke this particular frame in texts. For example, for the frame Kidnapping, two key roles are Perpetrator and the Victim, and this frame can be evoked with lexical units abduct, kidnap, or snatcher. While formally sound, the scarce availability of semantic frame resources and their limited lexical coverage hinders the wider adoption of frame semantics across languages and domains. To tackle this problem, firstly, we propose a method that takes as input a few frame-annotated sentences and generates alternative lexical realizations of lexical units and semantic roles matching the original frame definition. Secondly, we show that the obtained synthetically generated semantic frame annotated examples help to improve the quality of frame-semantic parsing. To evaluate our proposed approach, we decompose our work into two parts. In the first part of text augmentation for LUs and roles, we experiment with various types of models such as distributional thesauri, non-contextualized word embeddings (word2vec, fastText, GloVe), and Transformer-based contextualized models, such as BERT or XLNet. We perform the intrinsic evaluation of these induced lexical substitutes using FrameNet gold annotations. Models based on Transformers show overall superior performance, however, they do not always outperform simpler models (based on static embeddings) unless information about the target word is suitably injected. However, we observe that non-contextualized models also show comparable performance on the task of LU expansion. We also show that combining substitutes of individual models can significantly improve the quality of final substitutes. Because intrinsic evaluation scores are highly dependent on the gold dataset and the frame preservation, and cannot be ensured by an automatic evaluation mechanism because of the incompleteness of gold datasets, we also carried out experiments with manual evaluation on sample datasets to further analyze the usefulness of our approach. The results show that the manual evaluation framework significantly outperforms automatic evaluation for lexical substitution. For extrinsic evaluation, the second part of this work assesses the utility of these lexical substitutes for the improvement of frame-semantic parsing. We took a small set of frame-annotated sentences and augmented them by replacing corresponding target words with their closest substitutes, obtained from best-performing models. Our extensive experiments on the original and augmented set of annotations with two semantic parsers show that our method is effective for improving the downstream parsing task by training set augmentation, as well as for quickly building FrameNet-like resources for new languages or subject domains.

Identifying prior signals of bipolar disorder using primary care electronic health records: a nested case-control study.

Bipolar disorders are serious mental illnesses, yet evidence suggests that the diagnosis and treatment of bipolar disorder can be delayed by around 6 years. To identify signals of undiagnosed bipolar disorder using routinely collected electronic health records. A nested case-control study conducted using the UK Clinical Practice Research Datalink (CPRD) GOLD dataset, an anonymised electronic primary care patient database linked with hospital records. 'Cases' were adult patients with incident bipolar disorder diagnoses between 1 January 2010 and 31 July 2017. The patients with bipolar disorder (the bipolar disorder group) were matched by age, sex, and registered general practice to 20 'controls' without recorded bipolar disorder (the control group). Annual episode incidence rates were estimated and odds ratios from conditional logistic regression models were reported for recorded health events before the index (diagnosis) date. There were 2366 patients with incident bipolar disorder diagnoses and 47 138 matched control patients (median age 40 years and 60.4% female: n = 1430/2366 with bipolar disorder and n = 28 471/47 138 without). Compared with the control group, the bipolar disorder group had a higher incidence of diagnosed depressive, psychotic, anxiety, and personality disorders and escalating self-harm up to 10 years before a bipolar disorder diagnosis. Sleep disturbance, substance misuse, and mood swings were more frequent among the bipolar disorder group than the control group. The bipolar disorder group had more frequent face-to-face consultations, and were more likely to miss multiple scheduled appointments and to be prescribed ≥3 different psychotropic medication classes in a given year. Psychiatric diagnoses, psychotropic prescriptions, and health service use patterns might be signals of unreported bipolar disorder. Recognising these signals could prompt further investigation for undiagnosed significant psychopathology, leading to timely referral, assessment, and initiation of appropriate treatments.

Completeness, agreement, and representativeness of ethnicity recording in the United Kingdom’s Clinical Practice Research Datalink (CPRD) and linked Hospital Episode Statistics (HES)

BackgroundThis descriptive study assessed the completeness, agreement, and representativeness of ethnicity recording in the United Kingdom (UK) Clinical Practice Research Datalink (CPRD) primary care databases alone and, for those patients registered with a GP in England, when linked to secondary care data from Hospital Episode Statistics (HES).MethodsEthnicity records were assessed for all patients in the May 2021 builds of the CPRD GOLD and CPRD Aurum databases for all UK patients. In analyses of the UK, English data was from combined CPRD-HES, whereas data from Northern Ireland, Scotland, and Wales drew from CPRD only. The agreement of ethnicity records per patient was assessed within each dataset (CPRD GOLD, CPRD Aurum, and HES datasets) and between datasets at the highest level ethnicity categorisation (‘Asian’, ‘black’, ‘mixed’, ‘white’, ‘other’). Representativeness was assessed by comparing the ethnic distributions at the highest-level categorisation of CPRD-HES to those from the Census 2011 across the UK’s devolved administrations. Additionally, CPRD-HES was compared to the experimental ethnic distributions for England and Wales from the Office for National Statistics in 2019 (ONS2019) and the English ethnic distribution from May 2021 from NHS Digital’s General Practice Extraction Service Data for Pandemic Planning and Research with HES data linkage (GDPPR-HES).ResultsIn CPRD-HES, 81.7% of currently registered patients in the UK had ethnicity recorded in primary care. For patients with multiple ethnicity records, mismatched ethnicity within individual primary and secondary care datasets was < 10%. Of English patients with ethnicity recorded in both CPRD and HES, 93.3% of records matched at the highest-level categorisation; however, the level of agreement was markedly lower in the ‘mixed’ and ‘other’ ethnic groups. CPRD-HES was less proportionately ‘white’ compared to the UK Census 2011 (80.3% vs. 87.2%) and experimental ONS2019 data (80.4% vs. 84.3%). CPRD-HES was aligned with the ethnic distribution from GDPPR-HES (‘white’ 80.4% vs. 80.7%); however, with a smaller proportion classified as ‘other’ (1.1% vs. 2.8%).ConclusionsCPRD-HES has suitable representation of all ethnic categories with some overrepresentation of minority ethnic groups and a smaller proportion classified as ‘other’ compared to the UK general population from other data sources. CPRD-HES data is useful for studying health risks and outcomes in typically underrepresented groups.

Association between outpatient follow-up and incidence of revision after knee and hip replacements: a population-based cohort study

BackgroundFollow-up visits 5 or 7 years after surgery were recommended for people having primary hip or knee replacement. The benefits of this practice to patients and the healthcare system, however, have not yet been specifically examined. The aim of this study was to investigate the association between long-term follow-up outpatient hospital visits and revision rates for patients who undergo primary knee or hip replacement surgery.MethodsCohorts were identified for patients undergoing knee or hip replacement surgery using medical records from primary care practices within the UK Clinical Practice Research Datalink (CPRD) GOLD dataset linked to hospital records from the English Hospital Episodes Statistics (HES) data. Two groups of patients were compared in terms of revision and mortality rates: those with at least one long-term (between five and 10 years since primary surgery) follow-up visit at the orthopaedic department (‘Follow-up’ group), and those without (‘No follow-up’ group).ResultsA total of 9856 (4349 in the Follow-up group) patients with knee replacement and 10,837 (4870 in the Follow-up group) with hip replacement were included in the analysis. For knee replacement, the incidence of revision was 3.6% for those followed-up and 0.6% for those not followed-up. An adjusted regression model confirmed the difference in the hazard ratio (HR) for revision was statistically significant (HR: 5.65 [95% CI 3.62 to 8.81]). Mortality at 4 years was lower for the Follow-up (17%) compared to the No follow-up group (21%), but this difference was not statistically significant (HR: 0.95 [0.84 to 1.07]). For hip replacement, the incidence of revision rates were 3.2 and 1.4% for the follow-up and not follow-up groups, respectively, the difference being statistically significant (HR: 2.34 [1.71 to 3.20]). Mortality was lower for the Follow-up (15%) compared to the No follow-up group (21%), but the difference was not statistically significant (HR: 0.91 [0.81 to 1.02]).ConclusionPatients attending follow-up orthopaedic consultations show a higher risk of revision surgery compared to those who are not followed-up. A cause for this difference could not be identified in this study but a likely explanation is that surgeons play an effective role as ultimate arbitrators when identifying patients to be included in long-term follow-up lists.

DeepMPF: deep learning framework for predicting drug–target interactions based on multi-modal representation with meta-path semantic analysis

BackgroundDrug-target interaction (DTI) prediction has become a crucial prerequisite in drug design and drug discovery. However, the traditional biological experiment is time-consuming and expensive, as there are abundant complex interactions present in the large size of genomic and chemical spaces. For alleviating this phenomenon, plenty of computational methods are conducted to effectively complement biological experiments and narrow the search spaces into a preferred candidate domain. Whereas, most of the previous approaches cannot fully consider association behavior semantic information based on several schemas to represent complex the structure of heterogeneous biological networks. Additionally, the prediction of DTI based on single modalities cannot satisfy the demand for prediction accuracy.MethodsWe propose a multi-modal representation framework of ‘DeepMPF’ based on meta-path semantic analysis, which effectively utilizes heterogeneous information to predict DTI. Specifically, we first construct protein–drug-disease heterogeneous networks composed of three entities. Then the feature information is obtained under three views, containing sequence modality, heterogeneous structure modality and similarity modality. We proposed six representative schemas of meta-path to preserve the high-order nonlinear structure and catch hidden structural information of the heterogeneous network. Finally, DeepMPF generates highly representative comprehensive feature descriptors and calculates the probability of interaction through joint learning.ResultsTo evaluate the predictive performance of DeepMPF, comparison experiments are conducted on four gold datasets. Our method can obtain competitive performance in all datasets. We also explore the influence of the different feature embedding dimensions, learning strategies and classification methods. Meaningfully, the drug repositioning experiments on COVID-19 and HIV demonstrate DeepMPF can be applied to solve problems in reality and help drug discovery. The further analysis of molecular docking experiments enhances the credibility of the drug candidates predicted by DeepMPF.ConclusionsAll the results demonstrate the effectively predictive capability of DeepMPF for drug-target interactions. It can be utilized as a useful tool to prescreen the most potential drug candidates for the protein. The web server of the DeepMPF predictor is freely available at http://120.77.11.78/DeepMPF/, which can help relevant researchers to further study.

Two Directions for Clinical Data Generation with Large Language Models: Data-to-Label and Label-to-Data.

Large language models (LLMs) can generate natural language texts for various domains and tasks, but their potential for clinical text mining, a domain with scarce, sensitive, and imbalanced medical data, is under-explored. We investigate whether LLMs can augment clinical data for detecting Alzheimer's Disease (AD)-related signs and symptoms from electronic health records (EHRs), a challenging task that requires high expertise. We create a novel pragmatic taxonomy for AD sign and symptom progression based on expert knowledge and generated three datasets: (1) a gold dataset annotated by human experts on longitudinal EHRs of AD patients; (2) a silver dataset created by the data-to-label method, which labels sentences from a public EHR collection with AD-related signs and symptoms; and (3) a bronze dataset created by the label-to-data method which generates sentences with AD-related signs and symptoms based on the label definition. We train a system to detect AD-related signs and symptoms from EHRs. We find that the silver and bronze datasets improves the system performance, outperforming the system using only the gold dataset. This shows that LLMs can generate synthetic clinical data for a complex task by incorporating expert knowledge, and our label-to-data method can produce datasets that are free of sensitive information, while maintaining acceptable quality.

Analysis of Protein Structure for Drug Repurposing Using Computational Intelligence and ML Algorithm

Proteins are fundamental compounds in biological processes during the analysis of drug target indication for drug repurposing. The identification of relevant features is a necessary step in determining protein structure. A classification technique is used to identify the most important features in a dataset, which is why feature selection is so important. For protein structure prediction, recent research has developed a wide range of new methods to improve accuracy. The authors use principal component analysis (PCA) with correlation-matrix-based feature selection to analyse breast cancer data. In this paper, they discussed a therapeutic agent that is used to reduce the dataset by reduction-based algorithm and after that applied reduced dataset labelled as Standard Gold Dataset on machine learning model to analyze drug target indication. They get the higher accuracy of 92.8%, 93.9%, and 95.3%, each of the three datasets with 200, 500, and 1000 features with SVM with RBF kernel function. Also they found the best result, 97.8%, with the same classifier.

Automated Ascertainment of Typhlitis From the Electronic Health Record.

Adverse events (AEs) on Children's Oncology Group (COG) trials are manually ascertained using Common Terminology Criteria for Adverse Events. Despite significant effort, we previously demonstrated that COG typhlitis reporting sensitivity was only 37% when compared with gold standard physician chart abstraction. This study tested an automated typhlitis identification algorithm using electronic health record data. Electronic health record data from children with leukemia age 0-22 years treated at a single institution from 2006 to 2019 were included. Patients were divided into derivation and validation cohorts. Rigorous chart abstraction of validation cohort patients established a gold standard AE data set. We created an automated algorithm to identify typhlitis matching Common Terminology Criteria for Adverse Events v5 that included antibiotics, neutropenia, and non-negated mention of typhlitis in a note. We iteratively refined the algorithm using the derivation cohort and then applied the algorithm to the validation cohort; performance was compared with the gold standard. For patients on trial AAML1031, COG AE report performance was compared with the gold standard. The derivation cohort included 337 patients. The validation cohort included 270 patients (961 courses). Chart abstraction identified 16 courses with typhlitis. The algorithm identified 37 courses with typhlitis; 13 were true positives (sensitivity 81.3%, positive predictive value 35.1%). For patients on AAML1031, chart abstraction identified nine courses with typhlitis, and COG reporting correctly identified 4 (sensitivity 44.4%, positive predictive value 100.0%). The automated algorithm identified true cases of typhlitis with higher sensitivity than COG reporting. The algorithm identified false positives but reduced the number of courses needing manual review by 96% (961 to 37) by detecting potential typhlitis. This algorithm could provide a useful screening tool to reduce manual effort required for typhlitis AE reporting.

Autoimmune diseases and cardiovascular risk: a population-based study on 19 autoimmune diseases and 12 cardiovascular diseases in 22 million individuals in the UK

Some autoimmune diseases are associated with an increased risk of cardiovascular disease. We aimed to determine whether or not this is true, and to what extent, for a broad range of autoimmune conditions. In this population-based study, we used linked primary and secondary care records from the Clinical Practice Research Datalink (CPRD), GOLD and Aurum datasets, to assemble a cohort of individuals across the UK who were newly diagnosed with any of 19 autoimmune diseases between Jan 1, 2000, and Dec 31, 2017, younger than 80 years at diagnosis, and free of cardiovascular diseases up to 12 months after diagnosis. We also assembled a matched cohort with up to five individuals matched on age, sex, socioeconomic status, region, and calendar year, who were free of autoimmune disease and free of cardiovascular diseases up to 12 months after study entry. Both cohorts were followed up until June 30, 2019. We investigated the incidence of 12 cardiovascular outcomes and used Cox proportional hazards models to examine differences in patients with and without autoimmune diseases. Of 22 009 375 individuals identified from the CPRD databases, we identified 446 449 eligible individuals with autoimmune diseases and 2 102 830 matched controls. In the autoimmune cohort, mean age at diagnosis was 46·2 years (SD 19·8), and 271 410 (60·8%) were women and 175 039 (39·2%) were men. 68 413 (15·3%) people with and 231 410 (11·0%) without autoimmune diseases developed incident cardiovascular disease during a median of 6·2 years (IQR 2·7-10·8) of follow-up. The incidence rate of cardiovascular disease was 23·3 events per 1000 patient-years among patients with autoimmune disease and 15·0 events per 1000 patient-years among those without an autoimmune disease (hazard ratio [HR] 1·56 [95% CI 1·52-1·59]). An increased risk of cardiovascular disease with autoimmune disease was seen for every individual cardiovascular disease and increased progressively with the number of autoimmune diseases present (one disease: HR 1·41 [95% CI 1·37-1·45]; two diseases: 2·63 [2·49-2·78]); three or more diseases: 3·79 [3·36-4·27]), and in younger age groups (age <45 years: 2·33 [2·16-2·51]; 55-64 years: 1·76 [1·67-1·85]; ≥75 years: 1·30 [1·24-1·36]). Among autoimmune diseases, systemic sclerosis (3·59 [2·81-4·59]), Addison's disease (2·83 [1·96-4·09]), systemic lupus erythematosus (2·82 [2·38-3·33]), and type 1 diabetes (2·36 [2·21-2·52]) had the highest overall cardiovascular risk. These findings warrant targeted cardiovascular prevention measures, in particular in younger patients with autoimmune diseases, and further research into pathophysiological mechanisms underlying these complications. Horizon 2020 Marie Skłodowska-Curie Actions and European Society of Cardiology.

Studying the Effect of Syntactic Simplification on Text Summarization

The need for automatic text summarization (ATS) is increased manifold in recent times due to the overwhelming growth of textual data available in electronic form. However, existing ATS systems suffer from two major shortcomings. Summarizers of extractive type, that is, the ones which select important sentences of the documents in their original form as the output, tend to copy some irrelevant or unimportant parts of the input text in the output summary. On the other hand, abstractive summarizers, that is, the ones that produce a gist of the limited size of the original document, often fail to include important contents in the generated summary. Simplification of the input texts before submitting them to the ATS system(s) may obliterate the above difficulties. The present work examines the effectiveness of simplification of input for five different known ATS systems. In this work, DEPSYM++ simplifier has been used for the above purpose, which carries out four different kinds of simplification on sentences of the input text corresponding to the presence of appositive clause, relative clause, conjoint clause, and passive voice. The results obtained are found to be very encouraging when experiments were carried out on three different gold data sets and under different evaluation metrics commonly used for performance evaluation for summarizers.

Suicide and death by other causes among patients with a severe mental illness: cohort study comparing risks among patients discharged from inpatient care v. those treated in the community.

People diagnosed with a severe mental illness (SMI) are at elevated risk of dying prematurely compared to the general population. We aimed to understand the additional risk among people with SMI after discharge from inpatient psychiatric care, when many patients experience an acute phase of their illness. In the Clinical Practice Research Datalink (CPRD) GOLD and Aurum datasets, adults aged 18 years and older who were discharged from psychiatric inpatient care in England between 2001 and 2018 with primary diagnoses of SMI (schizophrenia, bipolar disorder, other psychoses) were matched by age and gender with up to five individuals with SMI and without recent hospital stays. Using survival analysis approaches, cumulative incidence and adjusted hazard ratios were estimated for all-cause mortality, external and natural causes of death, and suicide. All analyses were stratified by younger, middle and older ages and also by gender. In the year after their discharge, the risk of dying by all causes examined was higher than among individuals with SMI who had not received inpatient psychiatric care recently. Suicide risk was 11.6 times (95% CI 6.4-20.9) higher in the first 3 months and remained greater at 2-5 years after discharge (HR 2.3, 1.7-3.2). This risk elevation remained after adjustment for self-harm in the 6 months prior to the discharge date. The relative risk of dying by natural causes was raised in the first 3 months (HR 1.6, 1.3-1.9), with no evidence of elevation during the second year following discharge. There is an additional risk of death by suicide and natural causes for people with SMI who have been recently discharged from inpatient care over and above the general risk among people with the same diagnosis who have not recently been treated as an inpatient. This mortality gap shows the importance of continued focus, following discharge, on individuals who require inpatient care.

A Word Selection Method for Producing Interpretable Distributional Semantic Word Vectors

Distributional semantic models represent the meaning of words as vectors. We introduce a selection method to learn a vector space that each of its dimensions is a natural word. The selection method starts from the most frequent words and selects a subset, which has the best performance. The method produces a vector space that each of its dimensions is a word. This is the main advantage of the method compared to fusion methods such as NMF, and neural embedding models. We apply the method to the ukWaC corpus and train a vector space of N=1500 basis words. We report tests results on word similarity tasks for MEN, RG-65, SimLex-999, and WordSim353 gold datasets. Also, results show that reducing the number of basis vectors from 5000 to 1500 reduces accuracy by about 1.5-2%. So, we achieve good interpretability without a large penalty. Interpretability evaluation results indicate that the word vectors obtained by the proposed method using N=1500 are more interpretable than word embedding models, and the baseline method. We report the top 15 words of 1500 selected basis words in this paper.

Enhanced sentiment extraction architecture for social media content analysis using capsule networks.

Recent research has produced efficient algorithms based on deep learning for text-based analytics. Such architectures could be readily applied to text-based social media content analysis. The deep learning techniques, which require comparatively fewer resources for language modeling, can be effectively used to process social media content data that change regularly. Convolutional Neural networks and recurrent neural networks based approaches have reported prominent performance in this domain, yet their limitations make them sub-optimal. Capsule networks sufficiently warrant their applicability in language modelling tasks as a promising technique beyond their initial usage of image classification. This study proposes an approach based on capsule networks for social media content analysis, especially for Twitter. We empirically show that our approach is optimal even without the use of any linguistic resources. The proposed architectures produced an accuracy of 86.87% for the Twitter Sentiment Gold dataset and an accuracy of 82.04% for the CrowdFlower US Airline dataset, indicating state-of-the-art performance. Hence, the research findings indicate noteworthy accuracy enhancement for text processing within social media content analysis.

Temporal trends in annual incidence rates for psychiatric disorders and self-harm among children and adolescents in the UK, 2003\u20132018

BackgroundThere has been growing concern in the UK over recent years that a perceived mental health crisis is affecting children and adolescents, although published epidemiological evidence is limited.MethodsTwo population-based UK primary care cohorts were delineated in the Aurum and GOLD datasets of the Clinical Practice Research Datalink (CPRD). We included data from 9,133,246 individuals aged 1–20 who contributed 117,682,651 person-years of observation time. Sex- and age-stratified annual incidence rates were estimated for attention-deficit/hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) (age groups: 1–5, 6–9, 10–12, 13–16, 17–19), depression, anxiety disorders (6–9, 10–12, 13–16, 17–19), eating disorders and self-harm (10–12, 13–16, 17–19) during 2003–2018. We fitted negative binomial regressions to estimate incidence rate ratios (IRRs) to examine change in incidence between the first (2003) and final year (2018) year of observation and to examine sex-specific incidence.ResultsThe results indicated that the overall incidence has increased substantially in both boys and girls in between 2003 and 2018 for anxiety disorders (IRR 3.51 95% CI 3.18–3.89), depression (2.37; 2.03–2.77), ASD (2.36; 1.72–3.26), ADHD (2.3; 1.73–3.25), and self-harm (2.25; 1.82–2.79). The incidence for eating disorders also increased (IRR 1.3 95% CI 1.06–1.61), but less sharply. The incidence of anxiety disorders, depression, self-harm and eating disorders was in absolute terms higher in girls, whereas the opposite was true for the incidence of ADHD and ASD, which were higher among boys. The largest relative increases in incidence were observed for neurodevelopmental disorders, particularly among girls diagnosed with ADHD or ASD. However, in absolute terms, the incidence was much higher for depression and anxiety disorders.ConclusionThe number of young people seeking help for psychological distress appears to have increased in recent years. Changes to diagnostic criteria, reduced stigma, and increased awareness may partly explain our results, but we cannot rule out true increases in incidence occurring in the population. Whatever the explanation, the marked rise in demand for healthcare services means that it may be more challenging for affected young people to promptly access the care and support that they need.

FarsBase-KBP: A knowledge base population system for the Persian Knowledge Graph

While most of the knowledge bases already support the English language, there is only one knowledge base for the Persian language, known as FarsBase, which is automatically created via semi-structured web information. Unlike English knowledge bases such as Wikidata, which have tremendous community support, the population of a knowledge base like FarsBase must rely on automatically extracted knowledge. Knowledge base population can let FarsBase keep growing in size, as the system continues working. In this paper, we present a knowledge base population system for the Persian language, which extracts knowledge from unlabelled raw text, crawled from the Web. The proposed system consists of a set of state-of-the-art modules such as an entity linking module as well as information and relation extraction modules designed for FarsBase. Moreover, a canonicalization system is introduced to link extracted relations to FarsBase properties. Then, the system uses knowledge fusion techniques with minimal intervention of human experts to integrate and filter the proper knowledge instances, extracted by each module. To evaluate the performance of the presented knowledge base population system, we present the first gold dataset for benchmarking knowledge base population in the Persian language, which consisting of 22015 FarsBase triples and verified by human experts. The evaluation results demonstrate the efficiency of the proposed system.