Applications of Supervised Machine Learning in Autism Spectrum Disorder Research: a Review <s> Support Vector Machines <s> The Autism Observation Scale for Infants (AOSI) was developed to detect and monitor early signs of autism as they emerge in high-risk infants (all with an older sibling with an autistic spectrum disorder). Here we describe the scale and its development, and provide preliminary data on its reliability. Inter-rater reliability both for total scores and total number of endorsed items is good to excellent at 6, 12 and 18 months; reliability is more modest for individual items, particularly in 6-month-olds. Test-retest reliability of the AOSI at 12 months of age is within acceptable limits. Evidence that the AOSI provides reliable data is the first critical step towards evaluating its efficacy in distinguishing high-risk infants who develop ASD. <s> BIB001 </s> Applications of Supervised Machine Learning in Autism Spectrum Disorder Research: a Review <s> Support Vector Machines <s> Autism spectrum disorders (ASD) are a group of related neurodevelopmental disorders with significant combined prevalence (∼1%) and high heritability. Dozens of individually rare genes and loci associated with high-risk for ASD have been identified, which overlap extensively with genes for intellectual disability (ID). However, studies indicate that there may be hundreds of genes that remain to be identified. The advent of inexpensive massively parallel nucleotide sequencing can reveal the genetic underpinnings of heritable complex diseases, including ASD and ID. However, whole exome sequencing (WES) and whole genome sequencing (WGS) provides an embarrassment of riches, where many candidate variants emerge. It has been argued that genetic variation for ASD and ID will cluster in genes involved in distinct pathways and protein complexes. For this reason, computational methods that prioritize candidate genes based on additional functional information such as protein-protein interactions or association with specific canonical or empirical pathways, or other attributes, can be useful. In this study we applied several supervised learning approaches to prioritize ASD or ID disease gene candidates based on curated lists of known ASD and ID disease genes. We implemented two network-based classifiers and one attribute-based classifier to show that we can rank and classify known, and predict new, genes for these neurodevelopmental disorders. We also show that ID and ASD share common pathways that perturb an overlapping synaptic regulatory subnetwork. We also show that features relating to neuronal phenotypes in mouse knockouts can help in classifying neurodevelopmental genes. Our methods can be applied broadly to other diseases helping in prioritizing newly identified genetic variation that emerge from disease gene discovery based on WES and WGS. <s> BIB002 </s> Applications of Supervised Machine Learning in Autism Spectrum Disorder Research: a Review <s> Support Vector Machines <s> A health social network is an online information service which facilitates information sharing between closely related members of a community with the same or a similar health condition. Over the years, many automated recommender systems have been developed for social networking in order to help users find their communities of interest. For health social networking, the ideal source of information for measuring similarities of patients is the medical information of the patients. However, it is not desirable that such sensitive and private information be shared over the Internet. This is also true for many other security sensitive domains. A new information-sharing scheme is developed where each patient is represented as a small number of (possibly disjoint) d-words (discriminant words) and the d-words are used to measure similarities between patients without revealing sensitive personal information. The d-words are simple words like “food,'' and thus do not contain identifiable personal information. This makes our method an effective one-way hashing of patient assessments for a similarity measure. The d-words can be easily shared on the Internet to find peers who might have similar health conditions. <s> BIB003 </s> Applications of Supervised Machine Learning in Autism Spectrum Disorder Research: a Review <s> Support Vector Machines <s> Autism spectrum disorders (ASDs) represent a formidable challenge for psychiatry and neuroscience because of their high prevalence, lifelong nature, complexity and substantial heterogeneity. Facing these obstacles requires large-scale multidisciplinary efforts. Although the field of genetics has pioneered data sharing for these reasons, neuroimaging had not kept pace. In response, we introduce the Autism Brain Imaging Data Exchange (ABIDE)—a grassroots consortium aggregating and openly sharing 1112 existing resting-state functional magnetic resonance imaging (R-fMRI) data sets with corresponding structural MRI and phenotypic information from 539 individuals with ASDs and 573 age-matched typical controls (TCs; 7–64 years) (http://fcon_1000.projects.nitrc.org/indi/abide/). Here, we present this resource and demonstrate its suitability for advancing knowledge of ASD neurobiology based on analyses of 360 male subjects with ASDs and 403 male age-matched TCs. We focused on whole-brain intrinsic functional connectivity and also survey a range of voxel-wise measures of intrinsic functional brain architecture. Whole-brain analyses reconciled seemingly disparate themes of both hypo- and hyperconnectivity in the ASD literature; both were detected, although hypoconnectivity dominated, particularly for corticocortical and interhemispheric functional connectivity. Exploratory analyses using an array of regional metrics of intrinsic brain function converged on common loci of dysfunction in ASDs (mid- and posterior insula and posterior cingulate cortex), and highlighted less commonly explored regions such as the thalamus. The survey of the ABIDE R-fMRI data sets provides unprecedented demonstrations of both replication and novel discovery. By pooling multiple international data sets, ABIDE is expected to accelerate the pace of discovery setting the stage for the next generation of ASD studies. <s> BIB004 </s> Applications of Supervised Machine Learning in Autism Spectrum Disorder Research: a Review <s> Support Vector Machines <s> In the present work, we have undertaken a proof-of-concept study to determine whether a simple upper-limb movement could be useful to accurately classify low-functioning children with autism spectrum disorder (ASD) aged 2–4. To answer this question, we developed a supervised machine-learning method to correctly discriminate 15 preschool children with ASD from 15 typically developing children by means of kinematic analysis of a simple reach-to-drop task. Our method reached a maximum classification accuracy of 96.7 % with seven features related to the goal-oriented part of the movement. These preliminary findings offer insight into a possible motor signature of ASD that may be potentially useful in identifying a well-defined subset of patients, reducing the clinical heterogeneity within the broad behavioral phenotype. <s> BIB005 </s> Applications of Supervised Machine Learning in Autism Spectrum Disorder Research: a Review <s> Support Vector Machines <s> Background ::: Machine learning (ML) provides novel opportunities for human behavior research and clinical translation, yet its application can have noted pitfalls (Bone et al., 2015). In this work, we fastidiously utilize ML to derive autism spectrum disorder (ASD) instrument algorithms in an attempt to improve upon widely used ASD screening and diagnostic tools. ::: ::: Methods ::: The data consisted of Autism Diagnostic Interview-Revised (ADI-R) and Social Responsiveness Scale (SRS) scores for 1,264 verbal individuals with ASD and 462 verbal individuals with non-ASD developmental or psychiatric disorders, split at age 10. Algorithms were created via a robust ML classifier, support vector machine, while targeting best-estimate clinical diagnosis of ASD versus non-ASD. Parameter settings were tuned in multiple levels of cross-validation. ::: ::: Results ::: The created algorithms were more effective (higher performing) than the current algorithms, were tunable (sensitivity and specificity can be differentially weighted), and were more efficient (achieving near-peak performance with five or fewer codes). Results from ML-based fusion of ADI-R and SRS are reported. We present a screener algorithm for below (above) age 10 that reached 89.2% (86.7%) sensitivity and 59.0% (53.4%) specificity with only five behavioral codes. ::: ::: Conclusions ::: ML is useful for creating robust, customizable instrument algorithms. In a unique dataset comprised of controls with other difficulties, our findings highlight the limitations of current caregiver-report instruments and indicate possible avenues for improving ASD screening and diagnostic tools. <s> BIB006 </s> Applications of Supervised Machine Learning in Autism Spectrum Disorder Research: a Review <s> Support Vector Machines <s> The atypical face scanning patterns in individuals with Autism Spectrum Disorder (ASD) has been repeatedly discovered by previous research. The present study examined whether their face scanning patterns could be potentially useful to identify children with ASD by adopting the machine learning algorithm for the classification purpose. Particularly, we applied the machine learning method to analyze an eye movement dataset from a face recognition task [Yi et al., 2016], to classify children with and without ASD. We evaluated the performance of our model in terms of its accuracy, sensitivity, and specificity of classifying ASD. Results indicated promising evidence for applying the machine learning algorithm based on the face scanning patterns to identify children with ASD, with a maximum classification accuracy of 88.51%. Nevertheless, our study is still preliminary with some constraints that may apply in the clinical practice. Future research should shed light on further valuation of our method and contribute to the development of a multitask and multimodel approach to aid the process of early detection and diagnosis of ASD. Autism Res 2016, 9: 888-898. © 2016 International Society for Autism Research, Wiley Periodicals, Inc. <s> BIB007 </s> Applications of Supervised Machine Learning in Autism Spectrum Disorder Research: a Review <s> Support Vector Machines <s> Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder with a strong genetic basis. Yet, only a small fraction of potentially causal genes-about 65 genes out of an estimated several hundred-are known with strong genetic evidence from sequencing studies. We developed a complementary machine-learning approach based on a human brain-specific gene network to present a genome-wide prediction of autism risk genes, including hundreds of candidates for which there is minimal or no prior genetic evidence. Our approach was validated in a large independent case-control sequencing study. Leveraging these genome-wide predictions and the brain-specific network, we demonstrated that the large set of ASD genes converges on a smaller number of key pathways and developmental stages of the brain. Finally, we identified likely pathogenic genes within frequent autism-associated copy-number variants and proposed genes and pathways that are likely mediators of ASD across multiple copy-number variants. All predictions and functional insights are available at http://asd.princeton.edu. <s> BIB008 </s> Applications of Supervised Machine Learning in Autism Spectrum Disorder Research: a Review <s> Support Vector Machines <s> Autism spectrum condition (ASC) is primarily diagnosed by behavioural symptoms ::: including social, sensory and motor aspects. Although stereotyped, repetitive motor ::: movements are considered during diagnosis, quantitative measures that identify ::: kinematic characteristics in the movement patterns of autistic individuals are poorly ::: studied, preventing advances in understanding the aetiology of motor impairment, or ::: whether a wider range of motor characteristics could be used for diagnosis. The aim of ::: this study was to investigate whether data-driven machine learning based methods ::: could be used to address some fundamental problems with regard to identifying ::: discriminative test conditions and kinematic parameters to classify between ASC and ::: neurotypical controls. Data was based on a previous task where 16 ASC participants ::: and 14 age, IQ matched controls observed then imitated a series of hand movements. 40 ::: kinematic parameters extracted from eight imitation conditions were analysed using ::: machine learning based methods. Two optimal imitation conditions and nine most ::: significant kinematic parameters were identified and compared with some standard ::: attribute evaluators. To our knowledge, this is the first attempt to apply machine ::: learning to kinematic movement parameters measured during imitation of hand ::: movements to investigate the identification of ASC. Although based on a small sample, ::: the work demonstrates the feasibility of applying machine learning methods to analyse ::: high-dimensional data and suggest the potential of machine learning for identifying ::: kinematic biomarkers that could contribute to the diagnostic classification of autism. <s> BIB009 </s> Applications of Supervised Machine Learning in Autism Spectrum Disorder Research: a Review <s> Support Vector Machines <s> Abstract Machine learning approaches have been widely used for the identification of neuropathology from neuroimaging data. However, these approaches require large samples and suffer from the challenges associated with multi-site, multi-protocol data. We propose a novel approach to address these challenges, and demonstrate its usefulness with the Autism Brain Imaging Data Exchange (ABIDE) database. We predict symptom severity based on cortical thickness measurements from 156 individuals with autism spectrum disorder (ASD) from four different sites. The proposed approach consists of two main stages: a domain adaptation stage using partial least squares regression to maximize the consistency of imaging data across sites; and a learning stage combining support vector regression for regional prediction of severity with elastic-net penalized linear regression for integrating regional predictions into a whole-brain severity prediction. The proposed method performed markedly better than simpler alternatives, better with multi-site than single-site data, and resulted in a considerably higher cross-validated correlation score than has previously been reported in the literature for multi-site data. This demonstration of the utility of the proposed approach for detecting structural brain abnormalities in ASD from the multi-site, multi-protocol ABIDE dataset indicates the potential of designing machine learning methods to meet the challenges of agglomerative data. <s> BIB010 </s> Applications of Supervised Machine Learning in Autism Spectrum Disorder Research: a Review <s> Support Vector Machines <s> Abstract In this paper, we propose an automated white matter connectivity analysis method for machine learning classification and characterization of white matter abnormality via identification of discriminative fiber tracts. The proposed method uses diffusion MRI tractography and a data-driven approach to find fiber clusters corresponding to subdivisions of the white matter anatomy. Features extracted from each fiber cluster describe its diffusion properties and are used for machine learning. The method is demonstrated by application to a pediatric neuroimaging dataset from 149 individuals, including 70 children with autism spectrum disorder (ASD) and 79 typically developing controls (TDC). A classification accuracy of 78.33% is achieved in this cross-validation study. We investigate the discriminative diffusion features based on a two-tensor fiber tracking model. We observe that the mean fractional anisotropy from the second tensor (associated with crossing fibers) is most affected in ASD. We also find that local along-tract (central cores and endpoint regions) differences between ASD and TDC are helpful in differentiating the two groups. These altered diffusion properties in ASD are associated with multiple robustly discriminative fiber clusters, which belong to several major white matter tracts including the corpus callosum, arcuate fasciculus, uncinate fasciculus and aslant tract; and the white matter structures related to the cerebellum, brain stem, and ventral diencephalon. These discriminative fiber clusters, a small part of the whole brain tractography, represent the white matter connections that could be most affected in ASD. Our results indicate the potential of a machine learning pipeline based on white matter fiber clustering. <s> BIB011 </s> Applications of Supervised Machine Learning in Autism Spectrum Disorder Research: a Review <s> Support Vector Machines <s> We integrated multiple behavioural and developmental measures from multiple time-points using machine learning to improve early prediction of individual Autism Spectrum Disorder (ASD) outcome. We examined Mullen Scales of Early Learning, Vineland Adaptive Behavior Scales, and early ASD symptoms between 8 and 36 months in high-risk siblings (HR; n = 161) and low-risk controls (LR; n = 71). Longitudinally, LR and HR-Typical showed higher developmental level and functioning, and fewer ASD symptoms than HR-Atypical and HR-ASD. At 8 months, machine learning classified HR-ASD at chance level, and broader atypical development with 69.2% Area Under the Curve (AUC). At 14 months, ASD and broader atypical development were classified with approximately 71% AUC. Thus, prediction of ASD was only possible with moderate accuracy at 14 months. <s> BIB012
An SVM is a supervised learning algorithm that fits an optimal hyperplane in an n-dimensional space to correctly categorize the target result using the independent variables in the dataset. An SVM is a maximum margin classifier, meaning it maximizes the separation between n classes of data effectively in a highdimensional space  . SVMs are especially useful when the boundary between groups is non-linear because points can be easily transformed to a space in which the boundary is linear  . Because of this feature, SVMs are generally used in classification problems in which the distinction between groups is non-linear. SVM algorithms have been used in the research included in this review to classify individuals (e.g., according to diagnosis) based on standardized assessments, genes, neuroimaging, and other measurements. Behavioral and Neuroimaging Data Machine learning algorithms-such as SVMs-have been used in ASD research in attempts to improve screening and diagnostic practices. In general, these models have been shown to improve the accuracy of diagnoses and provide insight into how different characteristics (such as standardized assessments, eye movement data, upper limb and general kinesthetic data, and neuroimaging data) can help distinguish between patients with and without ASD. Currently, standardized assessments reliant on behavioral observation are the clinical standard for diagnosing ASD. One limitation of standardized assessments is the potential for misdiagnoses, particularly when distinguishing one disorder from another. BIB006 trained and cross-validated an SVM classifier to differentiate ASD from other developmental disorders (DD) based on data from two standardized assessments, the Autism Diagnostic Interview, Revised (ADI-R; Le  ) and the Social Responsiveness Scale (SRS;  . The sample consisted of 1264 individuals with ASD and 462 individuals with a DD other than ASD. Participants were segmented into two groups: individuals 10 years of age or older and individuals under 10 years of age. Using only five behavioral codes, results of the SVM screen algorithm showed a sensitivity of 89.2% for individuals 10 years of age or older and 86.7% for individuals under 10 years old. A specificity of 59.0% was reported for individuals 10 years of age or older and 53.4% for individuals under 10 years old. Given the potential for standardized assessments to fail to distinguish one disorder from another, these findings suggest that machine learning may be used to improve accuracy of standardized assessments. Machine learning models have also been employed to evaluate potential screening tools, alternative to behavioral assessments, including computer-aided methods. BIB007 used an SVM classification algorithm to explore whether patterns of eye movement during face recognition tasks can differentiate children with ASD from children with typical development (TD). Participants included 29 children with ASD and 29 children with TD ages 4 to 11 years old. Using data recorded from eye tracking software, an SVM classifier model was generated using a LOOCV strategy to train the model and test its ability to detect ASD. Feature representation was conducted in two parts. The SVM classifier was trained based on the extracted histograms containing the visual attention information on each face area (defined by K clusters). The results of the analysis revealed an accuracy of 88.51% in discriminating participants with ASD from participants with typical development, with a sensitivity of 93.10%, a specificity of 86.21%, and an AUC of 0.8963. Furthermore, the top seven of 64 discrimination dimensions were found to produce a relatively strong accuracy of 79.31%, with a sensitivity of 68.96% and a specificity of 84.48%. These findings reveal preliminary support for the application of machine learning analysis of face scanning patterns as a computer-aided screening method for the detection of ASD. Similarly, BIB005 evaluated a potential computer-aided screening method for ASD using an SVM classifier. The authors set out to investigate whether upper limb movement could accurately differentiate low functioning children with ASD from children with TD. A total of 15 preschoolaged children with ASD and 15 children with TD, matched on developmental age, participated in the study. An SVM was used to classify participants. Using seven movement-related features, the method produced a maximum classification accuracy of 96.7%, with a sensitivity of 100% and a specificity of 93.8%. Leveraging LOOCV, the overall mean classification revealed an accuracy of 84.9%, with a specificity of 89.1% a sensitivity of 82.2%. Findings suggest that machine learning analysis of kinematic data may detect motor features that are useful in identifying at least a portion of individuals with ASD (i.e., low functioning and preschool aged). Kinematic data was also used by BIB009 in a model to distinguish adults with ASD from adults with TD. Participants included 16 adults with ASD and 14 adults with TD, matched on IQ. Based on the performance of hand movement tasks, a naïve Bayes classifier of the means and standard deviations of 20 kinematic parameters was used to choose which of the eight imitation conditions were most useful to discriminate between subjects with ASD and subjects with TD. Then, an SVM was used on the chosen parameters. The performance of the SVM was lower than the accuracy of the naïve Bayes classifier, which the authors speculate was due to overfitting. Four models were then tested, including an SVM, RF, naïve Bayes, and decision tree. Both the naïve Bayes and SVM models outperformed the other models on average. A linear SVM achieved the highest accuracy of 86.7%, with 85.7% sensitivity and 87.5% specificity. Voice prosody was examined by  , comparing the performance of machine learning vs the clinical judgment of speech therapists in classifying children with ASD and children with TD based on single-word utterances. Participants included 30 children with ASD and 51 children with typical development. All children were between the ages of 3 and 10 years old and had no comorbid disorders. After isolating their single-word responses, an SVM classifier with cross-validation on 24 features was employed to identify ASD or TD. The SVM proved more accurate (76%) than the 10 speech therapists whose classifications were also based on the same audio recordings (69%). The SVM had a sensitivity of 81% and specificity of 73% compared to the therapists, who demonstrated a sensitivity of 54% and specificity of 80%. This study shows the potential of machine learning analysis of speech prosody as a useful screening tool. Neuroimaging data has also been examined using machine learning approaches. BIB010 predicted symptom severity of individuals with ASD based on cortical thickness using support vector regression (SVR) and ENet penalized linear regression. Participants included 156 individuals with ASD ages 8 to 40 years old. The dataset was compiled from four sites in the Autism Brain Imaging Data Exchange (ABIDE; Di BIB004 . ASD severity was based on the participants' Autism Diagnostic Observation Schedule (ADOS;  scores. As compared to SVM, SVR uses the same mathematical representation of the input data, but the output is continuous instead of discretized. In SVR, a regression line is fit to the data with a tube of predetermined radius. A total of 78 outputs from the SVR were used as the input for an ENet linear regression to produce the desired symptom severity scores. The authors used 10-fold cross-validation and found an average correlation of 0.51 and an average mean absolute error of 1.36. These results show an improved correlation score across multiple sites over previous studies and show the potential of machine learning in identifying biomarkers linked to behavioral symptoms of ASD. BIB011 sought to identify male children with ASD from TD children though diffusion magnetic resonance imaging (dMRI). Using the data of 70 children diagnosed with ASD and 79 typically developed controls obtained from the Center for Autism Research through Children's Hospital of Philadelphia, this study analyzed their whole brain white matter connectivity with the help of a SVM and 10-fold crossvalidation. By extracting multiple diffusion features from each fiber cluster of each subject, they were able to classify subjects as ASD or TD. The model with the highest accuracy, 78.33%, occurred with 4697 valid fiber clusters. This model produced a sensitivity of 84.81% and specificity of 72.86%. This study found that the most discriminatory fiber tracks (corpus callosum, cerebellum, brain stem, uncinate fasciculus, arcuate fasciculus, superior longitudinal fasciculus, and aslant) correspond to the fiber tracks identified with ASD in previous research. Using machine learning allowed Zhang and colleagues to better study locally specific brain regions via white matter parcellation into a large number of fiber clusters. Behavioral and Developmental Data Differentiated from many studies, BIB012 used longitudinal data captured at multiple points in development (8 and 14 months of age) for high-risk siblings to increase accuracy of predicting ASD diagnosis at 36 months. The behavioral measures used include Mullen Scales of Early Learning (MSEL; Mullen 1995), Vineland Behavioral Scales (VABS;  , and the Autism Observation Scale for Infants (AOSI; BIB001 . A total of 161 high-risk siblings were separated into three groups: HR-ASD (n = 32), HR-Atypical (n = 43), and HR-Typical (n = 86). Two binary classification targets were created from the three groups where the final classification is either HR-ASD vs (HR-Atypical + HRTypical) or (HR-ASD + HR-Atypical) vs HR-Typical. A lease squares SVM was used to train the classifiers with a 10-fold cross validation to tune model parameters. Based on the highest AUC, the classifier with the best performance for HR-ASD vs (HR-Atypical + HR Typical) at 14 months uses the Daily Living Score with an AUC of 71.3 which in comparison to data at 8 months having an AUC of 65.1 using motor scores, while the classifier using data at 8 months and the change factor leveraged motor, social, and daily living scores resulting an AUC of 65.9 which is a slight improvement over the 8-month scores. The best classified of (HR-ASD + HR-Atypical) vs HR Typical is also using data at 14 months but uses VABS scores and AOSI total score with an AUC of 70.8, in comparison the data at 8 months has an AUC of 69.2 using motor and communication scores, while the classifier using data at 8 months and the change factor had an AUC of 69.4. For this classifier, the performance did not differ from an AUC of 70 substantially as we saw in the HR-ASD vs (HRAtypical + HR Typical) modeling. This longitudinal study allows for predictive measures to be used at their individual time points but also the observed change between 8 and 14 months. Genetic Data Machine learning has also been applied in ASD genetics research. SVMs allow researchers to determine which genes are related to ASD. These findings highlight the potential of machine learning to improve understanding of the role genes play in the development of ASD and other disorders. The identification of genes associated with ASD helps to shed light on the etiology of the disorder. Using curated lists of genes known to be associated with ASD and intellectual disability (ID), BIB002 employed supervised machine learning techniques to classify known and predict new genes linked to these diagnoses. Computational methods, including two network-based classifiers and one attribute-based classifier, were employed. Finally, 10 SVM classifiers were employed using positive gene sets (i.e., genes associated with ASD or ID) and negative sets, which were randomly generated with 200 genes in each set. The SVM performed better than both network-based classifiers. Two non-overlapping lists were generated for truth data, containing 114 known rare, highrisk genes associated with ASD and 223 genes associated with ID. The SVM classifiers were capable of discriminating between genes associated with ASD, genes associated with ID, and other genes with an accuracy of 80 to 98%. Sensitivity for the classifiers ranged from 76 to 89%, specificity ranged from 89 to 96%, and AUC ranged from 0.94 to 0.97. Furthermore, the ASD classifiers performed better than ID classifiers. BIB008 also used an SVM to find potential genes correlated with ASD. A brain-specific functional network was created using a regularized Bayesian model. The network was used to identify 594 genes that may be predictors for ASD with varying levels of evidence (E1-E4). SVM models were trained and tested using 5-fold cross-validation in order to classify a patient with ASD or TD. Their best combination of phenotypes performed fairly, attaining an AUC of 0.80 using only E1 (high confidence genes). All evidence-weighted classifiers outperformed their unweighted counterparts. SVM algorithms, described earlier in this paper, have been used in the reviewed text mining articles to reveal potentially valuable links in existing ASD research and to help protect private health information shared over the Internet. Electronic Health Records SVMs have also been used in text mining to help protect personal health information shared on the Internet. BIB003 developed a new information sharing scheme, which conceals sensitive personal information. Each patient is identified by a small set of discriminant words, which can be used to measure similarities in health conditions. The authors developed a new keyword extraction method, called linear variance analysis (LVA), which uses SVMs to calculate the variances from a centroid of the population. LVA was found to be a more effective measure of similarity than existing measures across various sets of text documents collected from online health forums, including 250 text documents with stories and experiences shared by parents of children with ASD and ADHD, 100 health descriptions related to respiratory infections, and 200 assessment reports of children being evaluated for ASD. These findings show promise in the use of text mining to help protect private health information shared over the Internet. Ontologies  used text mining to uncover infrequent connections between ASD research articles in the hope of revealing new areas of exploration for researchers. A total of 214 publicly accessible articles relating to ASD were obtained from the PubMed database. The tool OntoGen  ) was used to create and explore various ASD ontologies. Ontologies are useful in sharing a common understanding of domain structure, explicitly formalizing domain assumptions, and analyzing domain knowledge. First, frequent research topics were explored. Using the wordvector text representation, OntoGen employed clustering to identify seven subconcepts in the ASD domain. Keywords used to describe the subconcepts were generated using a concept centroid vector and an SVM-based linear model. Rare connections between articles were also explored within the ontology. The authors specifically highlighted the relationship between ASD and calcineurin to be a potential area of future investigation. These findings demonstrate the potential of text mining to reveal infrequent and potentially valuable links within large collections of research.