Constructing a Cloud-based ADHD Screening System: a Perspective of Norm Development

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1 Constructing a Cloud-based ADHD Screening System: a Perspective of Norm Development Kuo-Chung Chu *, Lun-Ping Hung, and Chien-Fu Tseng Department of Information Management National Taipei University of Nursing and Health Sciences * kcchu@ntunhs.edu.tw Abstract - Attention deficit hyperactivity disorder (ADHD) is a popular child psychiatry disorder; the main symptoms are inattention, unable to suppress their impulsive behavior and restlessness of the situation. Previous studies had revealed that the prevalence of ADHD in school-age is about 5-12%. Without early diagnosis and treatment, it may cause cognitive dysfunction, low academic achievement, frustration, loss of self-esteem and self-confidence, sleep disorder, deviant behavior, and so on. Theoretically, screening criteria should be different because of world areas vary. However, in Taiwan, the existing screening systems are imported and referred to European and American norm. As the reference to non- Asian norm, screening results are not fully consistent with the situation in Taiwan. Therefore, this paper proposes a cloud-based ADHD screening system, which not only can screen ADHD symptoms, but also builds a domestic ADHD norm. The proposed system will help to accurately assess symptoms. To validate the system, we jointly discuss sensitivity and specificity to maximize the system feasibility of clinical diagnosis. Keywords: ADHD; ADHD Norm; Cloud computing; Model development; System implementation; Screening system; 1 Introduction If the children behave themselves improperly, they may be suffering from attention deficit hyperactivity disorder instead of deliberately naughty. Attentiondeficit/hyperactivity disorder (ADHD) is a common childhood behavioral disorder; it causes inattention and will be easily fatigue. The ADHD children have significant difficulties with inattentive, hyperactive, or impulsive behaviors; they are often regarded as unruly child. Inattention and hyperactivity problems may persist over time, and there are potential risks for additional difficulties, including conduct disorders, peer relationship difficulties, educational problems and underachievement, employment problems, a lack of involvement in social activities, suicidal behaviors, and criminality [1, 2] [3]. Past studies have shown that the prevalence of ADHD, regardless of race, gender showed significant trend, ranging from 0.9% to 12% [4] [5]. Generally believed, this symptom is common in men, prevalence will vary because of the different races. American Psychiatric Association (APA) reported that the prevalence of ADHD is 3~5% for pre-school children [6]. Naivety ADHD Taiwan association [7] estimated the prevalence is 5~7% in Taiwan, there are 2~4 ADHD students in a classroom. However, only less than 25% of children with ADHD have used specialist health services or been clinically diagnosed. Clinical diagnosis of ADHD is based on the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV-TR). Attention deficit (hyperactivity disorder) symptoms can be categorized into three types: (1) compound: inattention, hyperactivity, impulsivity symptoms; (2) attention deficit (ADD): only single symptoms of inattention; (3) hyperactive/impulsive: symptoms of hyperactivity and impulsivity. ADHD diagnosis is quite complicated process several dimensions, including (1) DSM-IV-TR diagnostic criteria; (2) differential diagnosis; (3) rating scale (questionnaire); (4) objective assessment (computer system testing); (5) scenario discussion. A joint expert team in which psychiatrist, professional clinical psychologist, special education, and pathology are involved conducts diagnosis and assessment. General diagnostics indicators, teachers scale and parents Scale, are provided by the APA DSM-IV to screen ADHD symptoms. The scales quickly help understanding of the children situation in the early stage. Associated scales are: Child Behavior Checklist (CBCL) and Teacher Report Form (TRF), Conner's Parent Rating Scale-Revised: Short Form (CPRS-R: S), Conner's Teacher Rating Scale- Revised: Short Form (CTRS-R: S), and SNAP-IV scale. By observing and determining performance of the patients in the school and the family is to assess whether they have the potential factors of ADHD. In the case of limited medical resources, after preliminary screening, the physician will further use of computer systems as a diagnostic tool. CBCL is used in the evaluation of children with epilepsy and ADHD assessment, psychiatrists make use

2 of the CBCL scale screening following symptoms, including social communication, physical factors, depression, childish, immature, schizophrenia, offensive, illegal, obesity, hyperactivity, adverse social, compulsion, cruelty, hostility, and anxiety. Test of Variables of Attention (TOVA), an objective method for diagnosis of ADHD patients and therapeutic, is a diagnosis-supported computer system to assess attention efficacy of children and adults. TOVA Studies accurately identified 87% of the normal population, 84% of the non-hyperactive ADHD patients, and 90% of the hyperactive patients. The general clinical diagnosis is based on experience, interviews, behavioral score and symptom checklist, to comprehensive diagnosis and treatment of ADHD [8]. Continuous Performance Tests (CPT), another quick and effective screening tool for assessing inattention, is widely used in research and clinical assessment of ADHD subjects over the age of 6. The CPT indicators include inattention, impulsiveness, and vigilance difficult problems [9]. In existing diagnosis computer systems, most of them are exported from European and American (E-A), ADHD norm (reference database) could not fit for world-wide ADHD assessment because the clinical diagnostic criteria may be different in the different areas. Due to the limitation of non-asian norm database, the diagnosis results are not fully consistent with the real situation. A possible case is that the criteria (ADHD norm) used in European-American area could be too low to differentiate the Asian patients; it leads to inaccurate assessment, Fig. 1. On the other hand, there are less data collected from domestic (Taiwan) patients, we need an ADHD norm to be a standard criteria for domestic diagnosis. This paper proposes a cloud-based diagnosis-supported ADHD (DS- ADHD) system which not only can screen ADHD symptoms, but also builds domestic ADHD norm. The rest of this paper is organized as follows: section 2 gives methodology of constructing the cloud-based DS- ADHD system and section 3 shows the system validation approach. We conclude the paper in section 4 with remarks on future work. Fig. 1 The diagnosis difference of varied Norm

3 Fig. 2 Framework of DS-ADHD system 2 Methodology 2.1 ADHD system framework The DS-ADHD system framework consists of four layers, Fig. 2: Conceptual layer: the conceptual mod el of the system. Based on literature survey and expert knowledge, we create screening standards (model indicators). Logical layer: using the indicators to build the system. Data collected from several end sites (hospitals) will be computed to generate indicators. Integrated layer: a local database is to collect diagnosis data, while integrated ADHD norm is a database to be compared with the diagnosis data. The integrated norm has two parts: 1) existing European- American norm; 2) domestic norm: a cloud-based norm (cloud site), which is periodically updated according to the local database. The relationship between cloud- and end- site is depicted in Fig. 3. Presentation layer: an operation interface of clinical diagnosis to collect data of ADHD patients.

4 Fig. 3 The relationship between cloud and end site. Fig. 4 The role of ADHD norm in DS-ADHD system 2.2 Development environment The system development applies rational unified process (RUP), which is a formal systems analysis and design approach with the concept of the spiral model, and of the iterative and incremental development principle. In programming language, object-oriented (OO) technology is easy to implement and modify the system. The development environment of software and hardware is further described in Table 1. The role of ADHD norm in DS-ADHD system is shown in Fig Cloud-based architecture To build a cloud-based screening system, the architecture is shown in Fig. 5. Table 1 The basic specification of the DS-ADHD system Cloud site Software Hardware.Microsoft Windows Server 2005 (SP2) +.MS SQL 2008.Visual Studio 2010.Visual Basic 2008.AMD Turion 64+.2GB 以上 DDR/RAM.100 GB+.CD-ROM Fig. 5 Architecture of cloud-based ADHD norm development Software.Microsoft Windows XP (SP3) +.Microsoft Access End site Hardware.AMD Turion GB+ DDR/RAM.HDD 100 GB+.CD-ROM 3 System validation approach Validity refers to the capability to measure the effectiveness of the system. The DS-ADHD validation is to discriminate the ADHD symptoms variables, including sensitivity and specificity. Sensitivity is the ability to correctly identify true ADHD cases; specificity is to the correctly identify normal (non-adhd) cases. The higher the

5 sensitivity, the higher the identification of ADHD symptoms; higher sensitivity is better to screen out those who may be suffering from ADHD cases. The higher the specificity, the lower the false positives (false alarm); higher specificity is more correctly discriminate from normal (non-adhd) to abnormal (ADHD). There is a tradeoff (cutoff point) between sensitivity and specificity. When a value increases, the other value will be reduced. The tradeoff must be carefully determined. Fig. 6 is an example of tradeoff (cutoff point) between sensitivity and specificity, in which two samples (Sample1 and Sample2) are illustrated [10]. The objective of system validation is to choose a proper value of cutoff point to maximize the system feasibility of clinical diagnosis. Fig. 6 Discrimination analysis of sensitivity and specificity 4 Conclusions This paper propose a cloud-based ADHD screening system, it not only can screen ADHD symptoms, but also builds domestic ADHD norm. The system creates several ADHD indicators, including inattention, hyperactivity, impulsiveness, and vigilance. By collecting indicators data to build an ADHD norm, the system will help to accurately assess domestic patients. After screening out the patients, then joint expert team put limited resource to treat the patients on difficulties with inattentive, hyperactive, or impulsive behaviors. It will greatly avoid the waste of medical resources. We integrate both the theoretical model and the practical application, and facilitate the domain cooperation for clinical medicine and informatics. This study provides a comprehensive mechanism to implement an ADHD screening system. To validate the proposed system, we discuss both sensitivity and specificity. Choosing a proper cutoff point between them will be a key successful factor, and e system feasibility of clinical diagnosis will be maximized. In the future, we intend to investigate other factors that will affect diagnosis results, including subject background (growth environment, lifestyle, drug habit) and user interface design (layout, targets size, displaying pattern and frequency). 5 References [1] C. Merrell and P. B. Tymms, "Inattention, hyperactivity and impulsiveness: Their impact on academic achievement and progress," British Journal of Educational Psychology, vol. 71, pp , Mar [2] C. Galera, M. P. Bouvard, G. Encrenaz, A. Messiah, and E. Fombonne, "Hyperactivity-inattention symptoms in childhood and suicidal behaviors in adolescence: the Youth Gazel Cohort," Acta Psychiatrica Scandinavica, vol. 118, pp , Dec [3] W. J. Barbaresi, S. K. Katusic, R. C. Colligan, A. L. Weaver, and S. J. Jacobsen, "Long-term school outcomes for children with attentiondeficit/hyperactivity disorder: A population-based perspective," Journal of Developmental and Behavioral Pediatrics, vol. 28, pp , Aug [4] J. C. Anderson, "DSM-III disorders in preadolescent children. Prevalence in a large sample from the general population," Arch Gen Psychiatry, vol. 44, pp , [5] E. J. Costello, "Psychopathology in pediatric primary care: the new hidden morbidity," Pediatrics, vol. 82, pp , [6] P. C. Buncher, "Attention-Deficit / Hyperactivity Disorder: A diagnosis for the '90s," Nurse Practitioner, vol. 21, pp , [7] Naivety-ADHD-Taiwan-association. (2010). Naivety ADHD Taiwan association. Available: [8] J. B. Lawrence, R. A. Yomtovian, C. Dillman, S. R. Masarik, V. Chongkolwatana, R. J. Creger, et al., "Reliability of automated platelet counts: comparison with manual method and utility for prediction of clinical bleeding," Am J Hematol, vol. 48, pp , Apr [9] C. K. Conners, Ed., Conners' Continuous Performance Test (CPT II) Version 5 for Windows Technical Guide and Software Manual. 2004, p.^pp. Pages. [10] L. M. Greenberg, C. L. Kindschi, T. R. Dupuy, and S. J. Hughes, T.O.V.A. Clinical Manual Test Of Variables of Attention Continuous Performance Test, Acknowledgements This research was funded by the National Science Council of Taiwan (Grant No: NSC H ).