A Health Social Network Recommender System

Insu Song1, Denise Dillon2, Tze Jui Goh3, and Min Sung3

1,2 School of Business Information Technology, and PsychologyJames Cook University Australia

{insu.song,denise.dillon}@jcu.edu.au3 Institute of Mental Health (IMH), Singapore{Tze Jui Goh,Min SUNG}@imh.com.sg

Abstract. People with chronic health conditions require support beyond normalhealth care systems. Social networking has shown great potential to provide theneeded support. Because of the privacy and security issues of health informa-tion systems, it is often difficult to find patients who can support each other inthe community. We propose a social-networking framework for patient care, inparticular for parents of children with Autism Spectrum Disorders (ASD). In theframework, health service providers facilitate social links between parents usingsimilarities of assessment reports without revealing sensitive information. A ma-chine learning approach was developed to generate explanations of ASD assess-ments in order to assist clinicians in their assessment. The generated explanationsare then used to measure similarities between assessments in order to recommenda community of related parents. For the first time, we report on the accuracy ofsocial linking using an explanation-based similarity measure.

Keywords: Social networking, health social network, health informatics, recom-mender system.

1 Introduction

1.1 Motivation

Recently, social networking for health care has shown great potential to empower pa-tient self-care. Examples include PatientsLikeMe1 and the IBM Patient EmpowermentSystem. These newly emerging patient-driven health care services facilitate informationexchange and collaboration between patients and between patients and doctors. The ser-vices provided by health social networks include (a) emotional support and informationsharing, (b) physician Q&As, and (c) self-tracking of a condition, its symptoms, treat-ment options and other biological information [16].

In this paper, we propose a social networking framework for parents of autistic chil-dren. Autism is characterized by a triad of impairments [18] in the areas of reciprocalsocial interaction, communication and repetitive and stereotyped behaviors. Asperger’sDisorder is similar to Autism, but involves no deviance or delay in language devel-opment. Studies have demonstrated that early diagnosis can lead to better prognosis

1 (http://www.patientslikeme.com/all/patients)

D. Kinny et al. (Eds.): PRIMA 2011, LNAI 7047, pp. 361–372, 2011.c© Springer-Verlag Berlin Heidelberg 2011

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Fig. 1. (a) illustrates the overall process of generating textual explanations to classification results.(b) shows an example use of the explanation method, where a clinician make use of textual expla-nations to previous or current assessments, such as Autism. Mobile devices such as smart phonescan be used to record interview questions and provide on the spot classification and explanationsto provide more objective mental health assessments.

for children with Autism Spectrum Disorders (ASD) [1]. However, most children getdiagnosed only upon entering the school environment when the behavioral difficultiesbecome more prominent. Hence, the implementation of early surveillance and screeningis crucial to identify children at risk for ASD at an earlier stage [8,7,11].

Recently, machine learning techniques such as support vector machines (SVMs)have shown significant potential for supporting the practice of medicine and psychi-atric classification [5]. The application of machine learning techniques in ASD diagno-sis has significant merits because of the potential to provide early diagnosis and morestandardized objective diagnosis. Conventionally, expert psychiatrists consciously andunconsciously analyze the language of their patients to make a clinical diagnosis usingdiagnostic classification schemas, such as the DSM IV [6] and ICD 10 [9]. Althoughthe DSM-IV and ICD-10 guidelines are helpful to clinicians in the diagnostic process,the effectiveness of their utilization depends on the experience of the clinician [12].

ASD is usually a lifelong condition such that long-term treatment planning and sup-ports from family and communities are essential. Social networking for health care mayempower parents of autistic children to share information with other parents and moreeasily collaborate with doctors. In this paper we develop a method of facilitating sociallinking of parents by similarities of assessment reports of their children. Explanationsof classification results of assessment reports are used to measure similarities of theassessment reports. The experiments describe a first attempt to generate explanationsof why practicing psychiatrists would have diagnosed autism cases using a decompo-sitional approach: learned SVM model parameters are analyzed to select informativefeatures, and then sensitivity filtering is used to select some subsets of more relevantfeatures.

Figure 1 (a) illustrates an overview of our approach to generating explanations forpsychological assessments using Support Vector Machines (SVMs). Explanation termsare extracted from assessment documents using both SVM models and classificationresults. Figure 1 (b) shows how our method can be used to provide explanation assistedassessment of autism and other mental health issues. For example, the explanations canhighlight the main issues that were used to differentiate the particular autism case fromnormal cases.

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1.2 Background

This work is based on social networking, text mining, text classification and, in par-ticular, recommendation systems. The following section provides a brief overview ofthe core techniques, focusing on social networking, recommendation systems, supportvector machines (SVMs), and the significance of generating human-comprehensibleexplanations from SVMs.

Social Network and Recommendation System. A health social network is an on-line information service which facilitates information sharing between closely relatedmembers of a community. Also known as social media on the Internet, or Health 2.0,a health social network empowers patients and health service providers by promotingcollaboration between patients, their caregivers, and clinicians [14]. At its basic level, ahealth social network provides emotional support by allowing patients to find others insimilar health situations. They can also share information about conditions, symptomsand treatments [16]. Other services include physician Q&A, and self-tracking of con-dition, symptom, treatment and other biological information [16]. The self-supportingcommunity is particularly important for lifelong conditions like autism.

The main means of finding patients with similar health conditions are based on labor-intensive methods such as searching the Internet, keywords in community titles and de-scriptions of other members in communities [15]. Over the years, many recommendersystems and similarity measurement methods have been developed [3]. The approachescan be broadly classified into two categories: content matching based on availablesemantic information and a collaborative filtering approach based on overlapping mem-bership of pairs of communities [15]. Our novel approach is based on semantic infor-mation of autism assessment reports.

Support Vector Machines. Cortes and Vapnik [2] introduced support vector machines(SVMs) which are a novel approach to machine learning. SVMs are based on the struc-tural risk minimization principle in order to overcome the overfitting problems. Supportvector machines find the hypotheses out of the hypothesis space H of a learning sys-tem which approximately minimizes the bound on the actual error by controlling theempirical error using training samples and the complexity of the model using the VC-dimension of H. SVMs are very universal learning systems [10]. In their basic form,SVMs learn maximal margin hyperplanes (linear threshold functions). A hyperplanecan be defined by a weight vector w and a bias b:

w ·x + b = 0

The corresponding threshold function for an input vector x is then given by:

f (x) = sign(w ·x + b)

However, it is possible learn polynomial classifiers, radial basis function (RBF) net-works and three or more layered neural networks by mapping input data x to some other(possibly infinite dimensional) feature space φ(x) and using kernel functions K(xi,x j)to obtain dot products, φ(xi) ·φ(x j), of feature data.

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Fig. 2. Parents of autistic children collaborate with hospital and the community to share expe-riences and learning to address their needs. The hospital engages in the community by provid-ing links between parents having children with similar assessment results. Families are matchedbased on similarity of their explanation terms.

Generating Explanations from SVMs. Much of the work that aims at providing anexplanation capability to SVMs has focused on rule extraction techniques [4] followingthe footsteps of the earlier effort to obtain human-comprehensible rules from artificialneural networks (ANNs). One approach to classifying rule extraction methods is thetranslucency dimension which includes decompositional and pedagogical (or learningbased) techniques as extremes [13]. The decompositional approach analyzes the internalrepresentation of the ANN. In general, decompositional rule extraction techniques startwith analyzing each individual neuron and their weight vectors to generate localizedrules. Initially, the inputs and outputs of the neurons form antecedents and consequentsof the rules, respectively. On the other hand, the strategy of the pedagogical approachesconsiders the trained ANN as a “black box” and aims at finding rules that map the ANNinputs directly to outputs [17]. For example, a decision tree can be generated from pairsof input and output values of the trained ANN.

1.3 Overview

In the next section, we propose a social networking framework for parents of autis-tic children that utilizes mobile phone-based ubiquitous computing. The remainderof the paper summarizes experiments and their results: text classification, explana-tion generation for classification results, statistical analysis on the model parametersthat are generated for autism diagnosis reports, and social linking using explanationsimilarities.

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2 Parent Network Framework

We propose a parent social-network framework, where health service providers canactively engage in facilitating information sharing and social links between parents.Figure 2 shows how hospitals can interact with communities of parents. Parents whoare concerned about their children can obtain preliminary assessment tools from hos-pitals via their mobile phones. They can fill in a standard assessment questionnaire toget a preliminary diagnosis and to obtain information on how to get help. Upon the firstconsultation with a clinician, the mobile agent on the parent can provide the clinician’sagent completed questionnaires and other preliminary diagnosis results improving boththe effectiveness and the efficiency of the clinician. The clinician can then, via the mo-bile agent of the parent, provide a treatment plan and tasks that parents can follow. Theassessment report is then stored in the data mining server to generate explanations andparent-link information about other parents of children with similar diagnoses. Subse-quent visits to the hospital will provide more refined treatment plans and informationabout communities who can share their experiences and should facilitate learning inorder to meet the parents’ needs, such as emotional supports and clinical knowledge.

3 Experimental Evaluation

3.1 Methodology

A preliminary study has been undertaken to generate explanations of autism diagnosisreports obtained from IMH (Institute of Mental Health, Singapore). Figure 3 illustratesour method of generating explanations. The autism diagnosis reports were obtainedfrom mental health clinics and comprise of a total of 236 reports: 217 positive casesand 19 negative cases. A small part of the observation section of an autism assessmentreport is shown below (the sentences are paraphrased to protect the identity of patients):

– He had difficulties in responding to questions.– He displayed difficulties in expressing himself and responded with only short in-

complete sentences.– He would respond with body gestures or single words when asked to elaborate on

his responses.– Often, he responded very slowly taking time to think before responding to ques-

tions.

The autism text documents are represented as attribute-value vectors (“bag of words”representation) where each distinct word corresponds to a feature whose value is thefrequency of the word in the text sample. A text document is represented as a featurevector x = (x1, .,x j, ..,xL) where x j is the j-th feature. Values were transformed withregard to the length of the sample. Function words were removed and stemming wasperformed on each extracted text. In summary, input vectors for machine learning con-sist of attributes (the words used in the sample) and values (the transformed frequencyof the words). Outputs are autism versus normal, that is, binary decision tasks werelearned. Clearly, the expressive power of the resulting explanations is limited by this“bag of words” representation.

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Fig. 3. Overview of the methodology and experiment of generating explanations to autism diag-nosis result

For LOO (leave-one-out) cross validation, 236 SVM models were generated usingthe linear kernel for the autism assessment data sets. Thus, each model is used to clas-sify one document. An SVM model is defined by support vectors xi and associatedparameters. The decision value of a text sample (represented as a feature vector x) isthen obtained as follows:

d(x) = ∑i∈SV

αiyiK(xi,x)+ b

where xi are support vectors and x is the feature vector, αi are Lagrangian multipliers,and b is the offset. The antecedent of the rule of inference is then this:

∑i∈SV

αiyixi ·x + b ≥ 0

That is, if d(x) ≥ 0, the feature vector x is positive or else negative. We use this insightinto the SVM models to define three types of explanations:

1. Explanation A comprising all the features contributing to the decision value d(x);2. Explanation B comprising top-N contributing features that are sufficient to classify

the features;3. Explanation C comprising top-N contributing features that also have their sensitiv-

ity values ∂d/∂x j greater than a set threshold value τ .

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Fig. 4. (a) Relationship between contribution (deviation), sensitivity, and word ranks. Each pointis a feature component that contributes to the decision of a feature. If a feature is a positive(negative) case, only the feature components having positive (negative) contributions are plotted.Rank 1 represents the most frequent term. (b) True-positive rate vs. false-positive rate of a linearsupport vector machine.

Technical details on generating each explanation type are described in Section 4.This approach is clearly a decompositional approach: analysis on the model parametersto select informative components and selecting subsets of more relevant components.Figure 4a summarizes the significance of each type of explanations. It plots contribu-tion, sensitivity, and word rank of all features of the autism data set. It shows that samplefeatures with higher ranking orders (more frequent words) and higher sensitivity valuestend to have larger contribution values. This suggests that features having higher sensi-tivity values and higher ranking order provide greater information in decision makingthan other features. It also shows that most of the large contributions are made by morefrequent words (high rank words).

3.2 Results

Support vector machines trained on the autism assessment data set achieved an accu-racy of 90% and AUC (Area Under the Curve) of 0.95. The corresponding ROC curve isshown in Figure 4 (b). The sensitivity values are adjusted manually to obtain a reason-able amount of terms for Explanation type C. Sample explanations of a positive autismdiagnosis case are provided below (Explanation A samples too big to show here):

1. Explanation B: social (94 46), mother (53 18), brother (50 23), old (44 58), in-terest (39 27), game (28 24), describe (27 24), share (21 41), computer (18 31),family (18 33), resource (17 42), limit (16 36), information (16 62), create (13 28),Strategies (11 36),.., (omitted the rest).

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2. Explanation C: social (94 46), old (44 58), interest (39 27), share (21 41), computer(18 31), family (18 33), resource (17 42), limit (16 36), inform (16 62), create (1328), strategies (11 36),.., (omitted the rest).

The numbers (d(x)i, ∂d/∂xi) indicate relative contribution values d(x)i to the decisionvalue d(x) and sensitivity ∂d/∂xi of the i-th term, respectively. Sensitivity-filtering(Explanation C) eliminates some of less sensitive terms (bold-faced terms) from Expla-nation B.

Sample explanations of a negative autism diagnosis case are provided below:

1. Explanation B: average (-190 -41), appropriate (-126 -71), his (-84 -18), attention(-62 -84), during (-41 -29), children (-32 -40), indicated (-20 -51), good (-19 -57),age (-19 -29), reason (-18 -20), attempt (-16 -26), regular (-14 -12), in (-12 -10),apparently (-11 -26), mental (-10 -22), well (-9 -35),.., (omitted the rest).

2. Explanation C: average (-190 -41), appropriate (-126 -71), attention (-62 -84), dur-ing (-41 -29), children (-32 -40), indicated (-20 -51), good (-19 -57), age (-19 -29),attempt (-16 -26), apparently (-11 -26), well (-9 -35),.., (omitted the rest).

Negative cases have negative contribution and sensitivity values.

4 Generating Explanations from SVM Models

In order to calculate the contribution values of each feature of a feature vector x, weuse the centroid C of the population, which is estimated using the centroid Csv of thesupport vectors:

Csv =1

Nsv ∑i∈SV

φ(xi)

where Nsv is the number of support vectors. We can then calculate the deviation of afeature vector x from the estimate population centroid:

D(x) = φ(x)−Csv

Suppose Csv is on the hyperplane: w ·φ(Csv) ≈− b. Then, we can obtain the decisionvalue d(x) using the deviation D(x):

d(x)≈(w · (φ(x)−Csv)) = ∑i∈SV

αiyi[K(xi,x)−K(xi,Csv)]

If K is the linear kernel, we can estimate the contribution of each j-th feature x j asfollows:

Csv, j =1

Nsv ∑i∈SV

xi, j

d(x) j = ∑i∈SV

αiyixi, j(x j−Csv, j)

Now, for a feature vector x, we can explain why a sample is positive (negative) bylisting the feature elements that contribute to the decision value. That is, we can rank

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the features of a feature vector according to the amount of contributions made by thefeatures. This is used as the basis of the explanation type A. We can also calculate thesum of all negative (positive) contributions and choose the top N positive (negative)contributions that are sufficient to push the decision value to positive (negative). This isused as the basis of the explanation type B.

It can be shown that Explanation A, B, and C are consistent: the same features arenot used to explain an opposite class. Consistency is one of the criteria for evaluatingrule quality (Andrew et al. [13]). Other important criteria for evaluating rule quality areaccuracy and fidelity. It can be shown that the accuracy of an SVM model is bounded bythe accuracy of explanation terms. Furthermore, we can achieve a similar performanceusing only the explanation-terms as the vocabulary: explanation-terms can mimic thebehavior of the SVM model from which the explanation terms are extracted. That is,the explanations display a high level of fidelity.

This method can easily be extended to non-linear SVM models with convex decisionboundaries. Applying the K-NN algorithm, N number of support vectors can be selectedas an explanation reference point forming a centroid and a hyperplane in the inputspace. This new hyperplane is now a linear SVM model that can be used to generateexplanations with regard to the selected support vectors.

4.1 Filtering Explanations with Sensitivity

Training a support vector machine for a data set of interest generates a hyperplane,which can be used to obtain the distance of a feature vector to the hyperplane to classify.The distance is normal to the hyperplane and thus the importance of a feature can bemeasured as the rate of change of the distance with respect to the feature. This can beeasily obtained for a linear classifier as follows:

∂d(x)∂x j

= ∑i∈SV

αiyixi, j

where d(x) is the distance of feature x to the hyperplane, x j is the j-th component ofthe feature x, and xi, j is the j-th component of a support vector xi. As we can see fromthe above equation, the importance of the j-th component for the hyperplane is the sumof j-th component of the support vectors multiplied by the class label and the Lagrangemultipliers.

5 Social Linking of Parents by Explanation Similarities

The explanations generated provide relevancy of each feature to the particular classes.We can use this information to measure the relevancy of each part of the explanationsto measure similarities between assessments. We use a semantic similarity measure be-tween two terms based on a common sense database called ConceptNet 2. This measurethen can be used to link parents having children with similar assessment results.

2 Used ConceptNet v2.1 from the Common Sense Computing Initiative at the MIT Media Lab(http://csc.media.mit.edu).

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Fig. 5. Error rates of linking 18 autism assessment reports to top-K most similar assessmentreports using top-N most contributing explanation terms

We start with a simple approach to generating similarity measures. The method isscoring each explanation term in one assessment with each explanation term in anotherassessment. In this approach, the similarity between two assessments is given by deter-mining contributions made by explanations terms and semantic relationships betweenterms. The similarity between two explanations A and B are defined as follows:

si, j =1

|A||B| ∑i∈A

∑j∈B−{i}

u(i, j)|d(A)i||d(B) j|

where u(i, j) is the semantic similarity function that measures how close the term i inexplanation A is to the term j in an explanation B where i �= j, d(A)i and d(A) j arethe amount of contributions of the features i and j, respectively. ConceptNet analogyspace is used as the similarity function. Each semantic similarity between terms is theL1 similarity measure for social networks defined in [15]. That is, dot products of twovectors

−→i and

−→j in the analogy space, but weighted with contributions of terms. The

parent link information is then generated by ranking assessments that are closed to anassessment and selecting N most similar assessments.

To test the effectiveness of this method, similarities between 16 assessments weremeasured: 8 assessments with autism diagnosis and 8 assessments with negative autismdiagnosis. The average of the similarity measure between assessments with same diag-nosis results was 8.66 and the average of the similarity measures between assessmentswith different diagnosis results was 6.83. The method of measuring similarities did nothave information on class labels, but was able to distinguish positive cases from nega-tive cases only using semantic similarity between the explanation terms. Figure 5 showsthe error rates of linking parents to other parents with the same diagnosis results, wherethe error rate is defined as follows:

Error Rate =Number of Incorrect Recommendations

Number of Recommendations

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The parents were linked by selecting top-K most similar assessments using top-N mostcontributing explanation terms. It shows that when one parent was linked to 32% orless proportions of the total community, the error rate is less than 35%, and it consis-tently gets better as K decreases further. The average error rate matrix in Figure 5 (b)clearly indicates that a small number of key explanation terms can provide good linkinformation and that the relevance information of explanation terms is useful.

Using this similarity measure, we can recommend a parent p to a community in a setC of communities by selecting the community with the maximum average-similaritybetween the parent p and all parents p′ in a community c:

R(p,C) = argmaxc∈C

1|c| ∑

p′∈c

sp,p′

6 Discussions and Future Work

This is the first report of a novel approach in providing social network-based healthcare services to families with ASD children. This is also the first report on the accuracyof social-linking using an explanation-based similarity measure. We showed that a se-mantic similarity measure between explanations of assessments has great potential fordiscovering close social communities of parents who can support each other for lifelongconditions like autism. It can also be used to find all potential hidden communities ofpatients and parents on the Internet. There is massive potential of incorporating thesesophisticated information extraction technologies in social networking more generally.

Long term disabilities likes ASD pose a significant burden for families, and thusit is essential that health care services actively participate in communities to supportpatients’ families. The community suggestion method facilitates social linking betweenparents with similar assessment reports to make information obtained through socialnetworking more relevant and useful.

The approach of extracting some piece of knowledge using machine learning in ex-plaining psychiatric assessments has the potential to provide early diagnosis and morestandardized assessments, and to improve the usability of machine learning techniquesin the medical and security domains. This approach of extracting explanations usingsome form of analysis on machine learning and associated parameters can be furtherexpanded by using alternative feature representations of text data sets, such as conceptterms or semantic terms.

References

1. Charman, T., Baird, G.: Practitioner review: Diagnosis of autism spectrum disorders in 2-and 3-year-old children. Journal of Child Psychology and Psychiatry 43(3), 289–305 (2002)

2. Cortes, C., Vapnik, V.: Support-vector networks. Machine Learning 20(3), 273–297 (1995)3. Deshpande, M., Karypis, G.: Item-based top-N recommendation algorithms. ACM Trans.

Inf. Syst. 22(1), 143–177 (2004)4. Diederich, J.: Rule extraction from support vector machines: An introduction. In: Rule Ex-

traction from Support Vector Machines. SCI, vol. 80, pp. 3–31. Springer, Heidelberg (2008)

372 I. Song et al.

5. Diederich, J., Al-Ajmi, A., Yellowlees, P.: Ex-ray: Data mining and mental health. AppliedSoft Computing 7(3), 923–928 (2007)

6. DSMIV: Diagnostic and Statistical Manual of Mental Disorders, Text Revision, 4th edn.American Psychiatric Association (2000)

7. Filipek, P.A., Accardo, P.J., Baranek, G.T., Cook, J.E.H., Dawson, G., Gordon, B., et al.: Thescreening and diagnosis of autistic spectrum disorders. Journal of Autism and DevelopmentalDisorders 29(6), 439–484 (1999)

8. Filipek, P.A., Accardo, P.J., Ashwal, S., Baranek, G.T., Cook Jr., E.H., Dawson, G., et al.:Practice parameter: Screening and diagnosis of autism. report of the quality standards sub-committee of the american academy of neurology and the child neurology society. Neurol-ogy 55(3), 468–479 (2000)

9. ICD10: International Statistical Classification of Disease and Related Health. World HealthOrganization, Geneva (1992)

10. Joachims, T.: Making large-scale support vector machine learning practical, pp. 169–184(1999)

11. Johnson, C.P., Myers, S.M.: The Council on Children with Disabilities: Identification andevaluation of children with autism spectrum disorders. Pediatrics 120, 1183–1215 (2007)

12. Klin, A., Lang, J., Cicchetti, D.V., Volkmar, F.: Brief report: Interrater reliability of clinicaldiagnosis and dsm-iv criteria for autistic disorder: Results of the dsm-iv autism field trial.Journal of Autism and Developmental Disorders 30(2), 163–167 (2000)

13. Robert Andrews, J.D., Tickle, A.B.: Survey and critique of techniques for extracting rulesfrom trained artificial neural networks. Knowledge-Based Systems 8(6), 373–389 (1995)

14. Sarasohn-Kahn, J.: The wisdom of patients: Health care meets online social media. CaliforniaHealthCare Foundation iHeath Reports (April 2008),http://www.chcf.org/publications/2008/04

15. Spertus, E., Sahami, M., Buyukkokten, O.: Evaluating similarity measures: a large-scalestudy in the orkut social network. In: Proceedings of the Eleventh ACM SIGKDD Inter-national Conference on Knowledge Discovery in Data Mining, KDD 2005, pp. 678–684.ACM, New York (2005)

16. Swan, M.: Emerging patient-driven health care models: an examination of health social net-works, consumer personalized medicine and quantified self-tracking. International Journalof Environmental Research and Public Health 6(2), 492–525 (2009)

17. Tickle, A., Andrews, R., Golea, M., Diederich, J.: The truth will come to light: Directions andchallenges in extracting the knowledge embedded within trained artificial neural networks.IEEE Transactions on Neural Networks 9(6), 1057–1068 (1998)

18. Wing, L., Gould, J.: Severe impairments of social interaction and associated abnormalitiesin children: Epidemiology and classification. Journal of Autism and Developmental Disor-ders. 9(1), 11–29 (1979)