Assessing English language sentences readability using machine learning models
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ABSTRACT:
SUBMITTER: Maqsood S
PROVIDER: S-EPMC8771811 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Computer programs that can accurately interpret natural human language and carry out instructions would improve the lives of people with language processing deficits and greatly benefit society in general. von Neumann in theorized that the human brain utilizes its own unique statistical neuronal computation to decode language and that this produces specific patterns of neuronal activity. This paper extends von Neumann's theory to the processing of partial semantics of declarative sentences. I developed semantic neuronal network models that emulate key features of cortical language processing and accurately compute partial semantics of English sentences. The method of computation implements the MAYA Semantic Technique, a mathematical technique I previously developed to determine partial semantics of sentences within a natural language processing program. Here I further simplified the technique by grouping repeating patterns into fewer categories. Unlike other natural language programs, my approach computes three partial semantics. The results of this research show that the computation of partial semantics of a sentence uses both feedforward and feedback projection which suggest that the partial semantic presented in this research might be a conscious activity within the human brain.
| S-EPMC4460779 | biostudies-literature
Project description:BackgroundThe Internet has become a powerful, accessible resource for many patients to use for their own medical management and knowledge. Vestibular disorders are prevalent, especially in the elderly. As the Internet is increasingly a major source of health-related information to the general public, it is often used to search for information regarding dizziness and vertigo. Ensuring that the information is accessible, unbiased, and appropriate can aid informed decision-making.ObjectiveTo evaluate the quality and readability of English-language Internet information related to vestibular disorders.MethodsA cross-sectional website search using three keywords (nausea, dizziness, and vertigo) in five country-specific versions of the most commonly used Internet search engine was conducted in March 2018. The language was limited to English for all websites. Quality was assessed by presence of Health on the Net (HON) certification and DISCERN scores. Readability was assessed using the Flesch Reading Ease (FRE) score, Flesch-Kincaid Grade Level Formula (F-KGL), and Simple Measure of Gobbledygook (SMOG).ResultsIn total, 112 websites were included and analyzed. The majority were commercial (61%) websites. A total of 42% had obtained HON certification. No association was found between the presence of HON certification and the resource of the website. The DISCERN scores had a mean of 2.52 (SD 1.1). Readability measures indicated that an average of 14-18 years of education was required to read and understand the Internet information provided regarding vestibular disorders.ConclusionsTo ensure the accessible to the general population, it is necessary to improve the quality and readability of Internet-based information regarding vestibular disorders.
| S-EPMC9249283 | biostudies-literature
Project description:We investigated the robust correlation between American Sign Language (ASL) and English reading ability in 51 young deaf signers ages 7;3 to 19;0. Signers were divided into 'skilled' and 'less-skilled' signer groups based on their performance on three measures of ASL. We next assessed reading comprehension of four English sentence structures (actives, passives, pronouns, reflexive pronouns) using a sentence-to-picture-matching task. Of interest was the extent to which ASL proficiency provided a foundation for lexical and syntactic processes of English. Skilled signers outperformed less-skilled signers overall. Error analyses further indicated greater single-word recognition difficulties in less-skilled signers marked by a higher rate of errors reflecting an inability to identify the actors and actions described in the sentence. Our findings provide evidence that increased ASL ability supports English sentence comprehension both at the levels of individual words and syntax. This is consistent with the theory that first language learning promotes second language through transference of linguistic elements irrespective of the transparency of mapping of grammatical structures between the two languages.
| S-EPMC3938551 | biostudies-literature
Project description:Children learning English as an additional language (EAL) often experience lower academic attainment than monolingual peers. In this study, teachers provided ratings of English language proficiency and social, emotional, and behavioral functioning for 782 children with EAL and 6,485 monolingual children in reception year (ages 4-5). Academic attainment was assessed in reception and Year 2 (ages 6-7). Relative to monolingual peers with comparable English language proficiency, children with EAL displayed fewer social, emotional, and behavioral difficulties in reception, were equally likely to meet curriculum targets in reception, and were more likely to meet targets in Year 2. Academic attainment and social, emotional, and behavioral functioning in children with EAL are associated with English language proficiency at school entry.
| S-EPMC5434859 | biostudies-other
Project description:MotivationBiomedical and healthcare domains generate vast amounts of complex data that can be challenging to analyze using machine learning tools, especially for researchers without computer science training.ResultsAliro is an open-source software package designed to automate machine learning analysis through a clean web interface. By infusing the power of large language models, the user can interact with their data by seamlessly retrieving and executing code pulled from the large language model, accelerating automated discovery of new insights from data. Aliro includes a pre-trained machine learning recommendation system that can assist the user to automate the selection of machine learning algorithms and its hyperparameters and provides visualization of the evaluated model and data.Availability and implementationAliro is deployed by running its custom Docker containers. Aliro is available as open-source from GitHub at: https://github.com/EpistasisLab/Aliro.
| S-EPMC10576163 | biostudies-literature
Project description:BACKGROUND AND PURPOSE:MR imaging-based modeling of tumor cell density can substantially improve targeted treatment of glioblastoma. Unfortunately, interpatient variability limits the predictive ability of many modeling approaches. We present a transfer learning method that generates individualized patient models, grounded in the wealth of population data, while also detecting and adjusting for interpatient variabilities based on each patient's own histologic data. MATERIALS AND METHODS:We recruited patients with primary glioblastoma undergoing image-guided biopsies and preoperative imaging, including contrast-enhanced MR imaging, dynamic susceptibility contrast MR imaging, and diffusion tensor imaging. We calculated relative cerebral blood volume from DSC-MR imaging and mean diffusivity and fractional anisotropy from DTI. Following image coregistration, we assessed tumor cell density for each biopsy and identified corresponding localized MR imaging measurements. We then explored a range of univariate and multivariate predictive models of tumor cell density based on MR imaging measurements in a generalized one-model-fits-all approach. We then implemented both univariate and multivariate individualized transfer learning predictive models, which harness the available population-level data but allow individual variability in their predictions. Finally, we compared Pearson correlation coefficients and mean absolute error between the individualized transfer learning and generalized one-model-fits-all models. RESULTS:Tumor cell density significantly correlated with relative CBV (r = 0.33, P < .001), and T1-weighted postcontrast (r = 0.36, P < .001) on univariate analysis after correcting for multiple comparisons. With single-variable modeling (using relative CBV), transfer learning increased predictive performance (r = 0.53, mean absolute error = 15.19%) compared with one-model-fits-all (r = 0.27, mean absolute error = 17.79%). With multivariate modeling, transfer learning further improved performance (r = 0.88, mean absolute error = 5.66%) compared with one-model-fits-all (r = 0.39, mean absolute error = 16.55%). CONCLUSIONS:Transfer learning significantly improves predictive modeling performance for quantifying tumor cell density in glioblastoma.
| S-EPMC6474354 | biostudies-other
Project description:BackgroundPreterm birth can lead to impaired language development. This study aimed to predict language outcomes at 2 years corrected gestational age (CGA) for children born preterm.MethodsWe analysed data from 89 preterm neonates (median GA 29 weeks) who underwent diffusion MRI (dMRI) at term-equivalent age and language assessment at 2 years CGA using the Bayley-III. Feature selection and a random forests classifier were used to differentiate typical versus delayed (Bayley-III language composite score <85) language development.ResultsThe model achieved balanced accuracy: 91%, sensitivity: 86%, and specificity: 96%. The probability of language delay at 2 years CGA is increased with: increasing values of peak width of skeletonized fractional anisotropy (PSFA), radial diffusivity (PSRD), and axial diffusivity (PSAD) derived from dMRI; among twins; and after an incomplete course of, or no exposure to, antenatal corticosteroids. Female sex and breastfeeding during the neonatal period reduced the risk of language delay.ConclusionsThe combination of perinatal clinical information and MRI features leads to accurate prediction of preterm infants who are likely to develop language deficits in early childhood. This model could potentially enable stratification of preterm children at risk of language dysfunction who may benefit from targeted early interventions.ImpactA combination of clinical perinatal factors and neonatal DTI measures of white matter microstructure leads to accurate prediction of language outcome at 2 years corrected gestational age following preterm birth. A model that comprises clinical and MRI features that has potential to be scalable across centres. It offers a basis for enhancing the power and generalizability of diagnostic and prognostic studies of neurodevelopmental disorders associated with language impairment. Early identification of infants who are at risk of language delay, facilitating targeted early interventions and support services, which could improve the quality of life for children born preterm.
| S-EPMC8503721 | biostudies-literature
Project description:The global efforts to control COVID-19 are threatened by the rapid emergence of novel SARS-CoV-2 variants that may display undesirable characteristics such as immune escape, increased transmissibility or pathogenicity. Early prediction for emergence of new strains with these features is critical for pandemic preparedness. We present Strainflow, a supervised and causally predictive model using unsupervised latent space features of SARS-CoV-2 genome sequences. Strainflow was trained and validated on 0.9 million sequences for the period December, 2019 to June, 2021 and the frozen model was prospectively validated from July, 2021 to December, 2021. Strainflow captured the rise in cases 2 months ahead of the Delta and Omicron surges in most countries including the prediction of a surge in India as early as beginning of November, 2021. Entropy analysis of Strainflow unsupervised embeddings clearly reveals the explore-exploit cycles in genomic feature-space, thus adding interpretability to the deep learning based model. We also conducted codon-level analysis of our model for interpretability and biological validity of our unsupervised features. Strainflow application is openly available as an interactive web-application for prospective genomic surveillance of COVID-19 across the globe.
| S-EPMC9024110 | biostudies-literature
Project description:Radiogenomics use non-invasively obtained imaging data, such as magnetic resonance imaging (MRI), to predict critical biomarkers of patients. Developing an accurate machine learning (ML) technique for MRI requires data from hundreds of patients, which cannot be gathered from any single local hospital. Hence, a model universally applicable to multiple cohorts/hospitals is required. We applied various ML and image pre-processing procedures on a glioma dataset from The Cancer Image Archive (TCIA, n = 159). The models that showed a high level of accuracy in predicting glioblastoma or WHO Grade II and III glioma using the TCIA dataset, were then tested for the data from the National Cancer Center Hospital, Japan (NCC, n = 166) whether they could maintain similar levels of high accuracy. Results: we confirmed that our ML procedure achieved a level of accuracy (AUROC = 0.904) comparable to that shown previously by the deep-learning methods using TCIA. However, when we directly applied the model to the NCC dataset, its AUROC dropped to 0.383. Introduction of standardization and dimension reduction procedures before classification without re-training improved the prediction accuracy obtained using NCC (0.804) without a loss in prediction accuracy for the TCIA dataset. Furthermore, we confirmed the same tendency in a model for IDH1/2 mutation prediction with standardization and application of dimension reduction that was also applicable to multiple hospitals. Our results demonstrated that overfitting may occur when an ML method providing the highest accuracy in a small training dataset is used for different heterogeneous data sets, and suggested a promising process for developing an ML method applicable to multiple cohorts.
| S-EPMC8306149 | biostudies-literature
Project description:Advanced bone imaging with quantitative computed tomography (QCT) has had limited success in significantly improving fracture prediction beyond standard areal bone mineral density (aBMD) measurements. Thus, we examined whether a machine learning paradigm, gradient boosting machine (GBM) modeling, which can incorporate diverse measurements of bone density and geometry from central QCT imaging and of bone microstructure from high-resolution peripheral QCT imaging, can improve fracture prediction. We studied two cohorts of postmenopausal women: 105 with and 99 without distal forearm fractures (Distal Forearm Cohort) and 40 with at least one grade 2 or 3 vertebral deformity and 78 with no vertebral fracture (Vertebral Cohort). Within each cohort, individual bone density, structure, or strength variables had areas under receiver operating characteristic curves (AUCs) ranging from 0.50 to 0.84 (median 0.61) for discriminating women with and without fracture. Using all possible variables in the GBM model, the AUCs were close to 1.0. Fracture predictions in the Vertebral Cohort using the GBM models built with the Distal Forearm Cohort had AUCs of 0.82-0.95, while predictions in the Distal Forearm Cohort using models built with the Vertebral Cohort had AUCs of 0.80-0.83. Attempts at capturing a comparable parametric model using the top variables from the Distal Forearm Cohort resulted in resulted in an AUC of 0.81. Relatively high AUCs for differing fracture types suggest that an underlying fracture propensity is being captured by this modeling approach. More complex modeling, such as with GBM, creates stronger fracture predictions and may allow deeper insights into information provided by advanced bone imaging techniques.
| S-EPMC3408850 | biostudies-literature