Knowledge discovery in clinical decision support systems for pain management: A systematic review

Artificial Intelligence in Medicine

Volumn 60, Issue 1, 2014, Pages 1-11

  Pombo, Nuno ^a   Araújo, Pedro ^a   Viana, Joaquim ^b  

^a UNIVERSITY OF BEIRA INTERIOR   (Portugal)

^b UNIVERSITY OF BEIRA INTERIOR   (Portugal)

Author keywords

Clinical decision support system; Machine learning; Pain measurement; Systematic review

Indexed keywords

CLINICAL DECISION SUPPORT SYSTEMS; CLUSTERS OF COMPUTERS; COMPUTERISED SYSTEMS; HEALTH CARE PROFESSIONALS; METHODS AND MATERIALS; RULE BASED ALGORITHMS; SYSTEMATIC REVIEW; WORKER PRODUCTIVITIES;

DECISION SUPPORT SYSTEMS; LEARNING ALGORITHMS; LEARNING SYSTEMS; MEDICAL COMPUTING; MOBILE DEVICES; NEURAL NETWORKS; SURVEYS;

SEARCH ENGINES;

ABDOMINAL PAIN; ACCURACY; ANALGESIA; ARTIFICIAL NEURAL NETWORK; CANCER PAIN; CLINICAL DECISION MAKING; DECISION SUPPORT SYSTEM; DECISION TREE; HEALTH CARE COST; HUMAN; KNEE PAIN; KNOWLEDGE DISCOVERY; LEARNING ALGORITHM; LOW BACK PAIN; MYOFASCIAL PAIN; NOMENCLATURE; PAIN ASSESSMENT; POSTOPERATIVE PAIN; PRIORITY JOURNAL; QUALITY OF LIFE; QUESTIONNAIRE; RISK ASSESSMENT; SCROTAL PAIN; SHORT SURVEY; SYSTEMATIC REVIEW (TOPIC); THORAX PAIN;

CLINICAL DECISION SUPPORT SYSTEM; MACHINE LEARNING; PAIN MEASUREMENT; SYSTEMATIC REVIEW;

DECISION SUPPORT SYSTEMS, CLINICAL; HUMANS; KNOWLEDGE; PAIN MANAGEMENT;

EID: 84892889305     PISSN: 09333657     EISSN: 18732860     Source Type: Journal    
DOI: 10.1016/j.artmed.2013.11.005     Document Type: Short Survey

References (120)

1. Loeser J.D., Treede R.-D. The Kyoto protocol of IASP basic pain terminology. Pain 2008, 137:473-477.
2. Committee on advancing pain research, care, medicine EI of relieving pain in America: a blueprint for transforming prevention, care, education, and research 2011, The National Academies Press.
3. Ashburn M.A., Staats P.S. Management of chronic pain. Lancet 1999, 353:1865-1869.
4. Langley P., Muller-Schwefe G., Nicolaou A., Liedgens H., Pergolizzi J., Varrassi G. The impact of pain on labor force participation, absenteeism and presenteeism in the European Union. Journal of Medical Economics 2010, 13:662-672.
5. Stewart W.F., Ricci J.A., Chee E., Hahn S.R., Morganstein D. Cost of lost productive work time among US workers with depression. JAMA 2003, 289:3135-3144.
6. Apkarian A.V., Baliki M.N., Geha P.Y. Towards a theory of chronic pain. Progress in Neurobiology 2009, 87:81-97.
7. McCaffery M., Pasero C.L. Pain ratings: the fifth vital sign. American Journal of Nursing 1997, 97:15-16.
8. Kong G., Xu D.-L., Body R., Yang J.-B., Mackway-Jones K., Carley S. A belief rule-based decision support system for clinical risk assessment of cardiac chest pain. European Journal of Operational Research 2012, 219:564-573.
9. Higgins J.P.T., Green S. Cochrane handbook for systematic reviews of interventions version 5.1.0 2011, The Cochrane Collaboration, [updated March 2011].
10. Michalowski W., Wilk S., Farion K., Pike J., Rubin S., Slowinsk R. Development of a decision algorithm to support emergency triage of scrotal pain and its implementation in the met system. INFOR 2005, 43:287-301.
11. Blaszczynski J., Farion K., Michalowski W., Wilk S., Rubin S., Weiss D. Mining clinical data: selecting decision support algorithm for the MET-AP system. AIME 2005, 429-433.
12. Farion K., Michalowski W., Slowinski R., Wilk S., Rubin S. Rough set methodology in clinical practice: controlled hospital trial of the MET system. Rough sets and current trends in computing 2004, vol. 3066:805-814. Springer, Berlin Heidelberg.
13. Michalowski W., Rubin S., Slowinski R., Wilk S. Triage of acute abdominal pain in childhood: clinical use of a palm handheld in a pediatric emergency department. Proceedings of the 37th annual Hawaii international conference on system sciences (HICSS'04) - track 6, vol. 6 2004, 60161a. IEEE Computer Society, Washington, DC, USA.
14. Michalowski W., Slowinski R., Wilk S., Farion K. Mobile emergency triage: lessons from a clinical trial. 35th annual meeting of the decision sciences institute 2004, 6601-6606. Conference Proceedings (CD-ROM), Boston, MA. J.E. Kendall (Ed.).
15. Michalowski W., Slowinski R., Wilk S., Farion K.J., Pike J., Rubin S. Design and development of a mobile system for supporting emergency triage. Methods of Information in Medicine 2005, 44:14-24.
16. Farion K.J., Michalowski W., Rubin S., Wilk S., Correll R., Gaboury I. Prospective evaluation of the MET-AP system providing triage plans for acute pediatric abdominal pain. International Journal of Medical Informatics 2008, 77:208-218.
17. Farion K., Michalowski W., Wilk S., O'Sullivan D., Rubin S., Weiss D. Clinical decision support system for point of care use: ontology driven design and software implementation. Methods of Information in Medicine 2009, 48:381-390.
18. Fathi-Torbaghan M., Meyer D. MEDUSA: a fuzzy expert system for medical diagnosis of acute abdominal pain. Methods of Information in Medicine 1994, 33:522-529.
19. Blazadonakis M., Moustakis V., Charissis G. Deep assessment of machine learning techniques using patient treatment in acute abdominal pain in children. Artificial Intelligence in Medicine 1996, 8:527-542.
20. Ohmann C., Moustakis V., Yang Q., Lang K., Group A.A.P.S. Evaluation of automatic knowledge acquisition techniques in the diagnosis of acute abdominal pain. Artificial Intelligence in Medicine 1996, 8:23-36.
21. Eich H.P., Ohmann C., Lang K. Decision support in acute abdominal pain using an expert system for different knowledge bases. Proceedings of the tenth IEEE symposium on computer-based medical systems 1997, 2-7.
22. Ellenius J., Groth T., Lindahl B., Wallentin L. Early assessment of patients with suspected acute myocardial infarction by biochemical monitoring and neural network analysis. Clinical Chemistry 1997, 43:1919-1925.
23. Ellenius J., Groth T. Transferability of neural network-based decision support algorithms for early assessment of chest-pain patients. International Journal of Medical Informatics 2000, 60:1-20.
24. Kennedy R.L., Harrison R.F., Burton A.M., Fraser H.S., Hamer W.G., MacArthur D., et al. An artificial neural network system for diagnosis of acute myocardial infarction (AMI) in the accident and emergency department: evaluation and comparison with serum myoglobin measurements. Computer Methods and Programs in Biomedicine 1997, 52:93-103.
25. Pesonen E., Eskelinen M., Juhola M. Treatment of missing data values in a neural network based decision support system for acute abdominal pain. Artificial Intelligence in Medicine 1998, 13:139-146.
26. Vaughn M.L., Cavill S.J., Taylor S.J., Foy M.A., Fogg A.J.B. Interpretation and knowledge discovery from a MLP network that performs low back pain classification. IEE colloquium on knowledge discovery and data mining (1998/434), vol. 2 1998, 1-4.
27. Aase O. Clinical experience with a decision support computer program using Bayes' theorem to diagnose chest pain patients. Cardiology 1999, 92:128-134.
28. Wang S.J., Ohno-Machado L., Fraser H.S.F., Kennedy R.L. Using patient-reportable clinical history factors to predict myocardial infarction. Computers in Biology and Medicine 2001, 31:1-13.
29. Baxt W.G., Shofer F.S., Sites F.D., Hollander J.E. A neural computational aid to the diagnosis of acute myocardial infarction. Annals of Emergency Medicine 2002, 39:366-373.
30. Kuziemsky C.E., Lau F., Bilykh I., Jahnke J.H., McCallum G., Obry C., et al. Ontology-based information integration in health care: a focus on palliative care. Proceedings of the eleventh annual international workshop on software technology and engineering practice 2003, 164-172. IEEE Computer Society, Washington, DC, USA.
31. Wilkie D.J., Judge M.K.M., Berry D.L., Dell J., Zong S., Gilespie R. Usability of a computerized PAINReportIt in the general public with pain and people with cancer pain. Journal of Pain and Symptom Management 2003, 25:213-224.
32. Huang H.-Y., Wilkie D.J., Zong S.-P.S., Berry D., Hairabedian D., Judge M.K., et al. Developing a computerized data collection and decision support system for cancer pain management. Computers Informatics Nursing: CIN 2003, 21:206-217.
33. Lin L., Hu P.J-H., Sheng O.R.L. A decision support system for lower back pain diagnosis: uncertainty management and clinical evaluations. Decision Support Systems 2006, 42:1152-1169.
34. Sadeghi S., Barzi A., Sadeghi N., King B. A Bayesian model for triage decision support. International Journal of Medical Informatics 2006, 75:403-411.
35. Westfall J.M., Van Vorst R.F., McGloin J., Selker H.P. Triage and diagnosis of chest pain in rural hospitals: implementation of the ACI-TIPI in the High Plains Research Network. Annals of Family Medicine 2006, 4:153-161.
36. Chang C.-H., Boni-Saenz A.A., Durazo-Arvizu R.A., DesHarnais S., Lau D.T., Emanuel L.L. A system for interactive assessment and management in palliative care. Journal of Pain and Symptom Management 2007, 33:745-755.
37. Lai D.T.H., Levinger P., Begg R.K., Gilleard W., Palaniswami M. Identification of patellofemoral pain syndrome using a Support Vector Machine approach. Engineering in medicine and biology society, 2007. EMBS 2007. 29th annual international conference of the IEEE 2007, 3144-3147.
38. van Gerven M.A.J., Jurgelenaite R., Taal B.G., Heskes T., Lucas P.J.F. Predicting carcinoid heart disease with the noisy-threshold classifier. Artificial Intelligence in Medicine 2007, 40:45-55.
39. van Gerven M.A.J., Taal B.G., Lucas P.J.F. Dynamic Bayesian networks as prognostic models for clinical patient management. Journal of Biomedical Informatics 2008, 41:515-529.
40. Binaghi E., Gallo I., Ghiselli C., Levrini L., Biondi K. An integrated fuzzy logic and web-based framework for active protocol support. International Journal of Medical Informatics 2008, 77:256-271.
41. Elvidge K. Improving pain & symptom management for advanced cancer patients with a clinical decision support system. eHealth beyond the horizon - get IT there, proceedings of MIE2008, the XXIst international congress of the European federation for medical informatics, vol. 136 2008, 169-174. IOS Press, Göteborg, Sweden. S.K. Andersen, G.O. Klein, S. Schulz, J. Aarts (Eds.).
42. Lu H.-M., Zeng D., Trujillo L., Komatsu K., Chen H. Ontology-enhanced automatic chief complaint classification for syndromic surveillance. Journal of Biomedical Informatics 2008, 41:340-356.
43. Watt E., Bui A.A. Evaluation of a dynamic Bayesian belief network to predict osteoarthritic knee pain using data from the osteoarthritis initiative. AMIA annual symposium proceedings 2008.
44. Abas H.I., Yusof M.M., Noah S.A.M. The application of ontology in a clinical decision support system for acute postoperative pain management. International conference on Semantic Technology and Information Retrieval (STAIR) 2011, 106-112.
45. Farooq K., Hussain A., Leslie S., Eckl C., Slack W. Ontology-driven cardiovascular decision support system 2011, 283-286. PervasiveHealth, IEEE, Dublin, Ireland.
46. Jinglin Y., Li H.-X., Yong H. A probabilistic SVM based decision system for pain diagnosis. Expert Systems with Applications 2011, 38:9346-9351.
47. Simonic K.-M., Holzinger A., Bloice M., Hermann J. Optimizing long-term treatment of rheumatoid arthritis with systematic documentation. 5th international conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth) 2011, 504-550.
48. Michalski R., Mozetic I., Hong J., Lavrac N. The multi-purpose incremental learning system AQ15 and its testing application to three medical domains. Proceedings of the fifth national conference on artificial intelligence 1986.
49. Quinlan J.R. C4.5: programs for machine learning 1993, Morgan Kaufmann Publishers Inc., San Francisco, CA, USA.
50. Breiman L., Friedman J.H., Olshen R.A., Stone C.J. Classification and regression trees 1984, Wadsworth Inc., Belmont, CA.
51. Clark P., Niblett T. The CN2 induction algorithm. Machine Learning 1989, 3:261-283.
52. Quinlan J.R. Induction of decision trees. Machine Learning 1986, 1:81-106.
53. Boswell R.A. (TI) TI. Manual for NEWID version 2.0: technical report 1990, Turing Institute.
54. Smyth P., Goodman R.M. An information theoretic approach to rule induction from databases. IEEE Transactions on Knowledge and Data Engineering 1992, 4:301-316.
55. Cendrowska J. PRISM: an algorithm for inducing modular rules. International Journal of Man-Machine Studies 1987, 27:349-370.
56. Gamberger D. A minimization approach to propositional inductive learning. Proceedings of the 8th European conference on machine learning 1995, 151-160. Springer-Verlag, UK.
57. Clarke E.J., Waclawiw M.A. Probabilistic rule induction from a medical research study database. Computers and Biomedical Research 1996, 29:271-283.
58. Lorena A.C., Jacintho L.F.O., Siqueira M.F., Giovanni R.D., Lohmann L.G., de Carvalho A.C.P.L.F., et al. Comparing machine learning classifiers in potential distribution modelling. Expert Systems with Applications 2011, 38:5268-5275.
59. Elomaa T. The biases of decision tree pruning strategies. Proceedings of the third international symposium on advances in intelligent data analysis 1999, 63-74. Springer-Verlag, UK.
60. Bramer M. Using J-pruning to reduce overfitting in classification trees. Knowledge-Based Systems 2002, 15:301-308.
61. Kotsiantis S.B., Zaharakis I.D., Pintelas P.E. Machine learning: a review of classification and combining techniques. Artificial Intelligence Review 2006, 26:159-190.
62. Li H., Wang M., Zhou X., Zhao J. An interval set model for learning rules from incomplete information table. International Journal of Approximate Reasoning 2012, 53:24-37.
63. Morik K. Applications of machine learning. Proceedings of the 6th European knowledge acquisition workshop on current developments in knowledge acquisition 1992, 9-13. Springer-Verlag, UK.
64. Hansel D., Sompolinsky H. Learning from examples in a single-layer neural network. Europhysics Letters 1990, 11:687-692.
65. Bourlard H., Wellekens C.J. Links between Markov models and multilayer perceptrons. IEEE Transactions on Pattern Analysis and Machine Intelligence 1990, 12:1167-1178.
66. Meyfroidt G., Güiza F., Ramon J., Bruynooghe M. Machine learning techniques to examine large patient databases. Best Practice & Research Clinical Anaesthesiology 2009, 23:127-143.
67. Kononenko I. Machine learning for medical diagnosis: history, state of the art and perspective. Artificial Intelligence in Medicine 2001, 23:89-109.
68. Kon M.A., Plaskota L. Information complexity of neural networks. Neural Networks 2000, 13:365-375.
69. Camargo L.S., Yoneyama T. Specification of training sets and the number of hidden neurons for multilayer perceptrons. Neural Computation 2001, 13:2673-2680.
70. Pawlak Z. Rough set theory and its applications. Information Systems Journal 1998, 29:7-10.
71. Hu X.T., Lin T.Y., Han J. A new rough sets model based on database systems. Proceedings of the 9th international conference on rough sets, fuzzy sets, data mining, and granular computing 2003, 114-121. Springer-Verlag, Berlin, Heidelberg.
72. Hu X. Ensembles of classifiers based on rough sets theory and set-oriented database operations. IEEE international conference on granular computing 2006, 67-73.
73. Pawlak Z., Skowron A. Rudiments of rough sets. Information Sciences 2007, 177:3-27.
74. Zadeh L.A. Fuzzy sets. Information and Control 1965, 8:338-353.
75. Zwick R. Combining stochastic uncertainty and linguistic inexactness: theory and experimental evaluation of four fuzzy probability models. International Journal of Man-Machine Studies 1989, 30:69-111.
76. Yaguinuma C.A., Santos M.T.P., Camargo H.A., Nogueira T.M. A meta-ontology approach for representing vague linguistic terms and fuzzy rules for classification in ontologies. Enterprise distributed object computing conference workshops (EDOCW), 2010 14th IEEE international 2010, 263-271.
77. Dombi J. Membership function as an evaluation. Fuzzy Sets and Systems 1990, 35:1-21.
78. Hastie T., Tibshirani R., Friedman J. The elements of statistical learning: data mining, inference, and prediction 2009, Corr. 3rd printing 5th printing. Springer. 2nd ed.
79. Andersen S.K., Olesen K.G., Jensen F.V. Readings in uncertain reasoning 1990, 332-337. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA. G. Shafer, J. Pearl (Eds.).
80. John G., Langley P. Estimating continuous distributions in Bayesian classifiers. Proceedings of the eleventh conference on uncertainty in artificial intelligence 1995, 338-345. Morgan Kaufmann.
81. Heckerman D. A tutorial on learning with Bayesian networks 1995, Microsoft Research, Redmond, Washington.
82. Diez F.J. Parameter adjustment in Bayes networks: the generalized noisy or-gate. Proceedings of the ninth conference on uncertainty in artificial intelligence 1993, 99-105. Morgan Kaufmann.
83. Vomlel J. Exploiting functional dependence in Bayesian network inference. Proceedings of the eighteenth conference on uncertainty in artificial intelligence 2002, 528-535. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA.
84. Jurgelenaite R., Heskes T. EM algorithm for symmetric causal independence models. Proceedings of the 17th European conference on machine learning 2006, 234-245. Springer-Verlag, Berlin, Heidelberg.
85. Indyk P., Motwani R. Approximate nearest neighbors: towards removing the curse of dimensionality. Proceedings of the thirtieth annual ACM symposium on theory of computing 1998, 604-613. ACM, New York, NY, USA.
86. Aha D.W., Kibler D., Albert M.K. Instance-based learning algorithms. Machine Learning 1991, 6:37-66.
87. Cole T.J. Applied logistic regression. Statistics in Medicine 1991, 10:1162-1163.
88. Guo G., Wang H., Bell D.A., Bi Y., Greer K. KNN model-based approach in classification. CoopIS/DOA/ODBASE 2003, vol. 2888:986-996. Springer.
89. Dreiseitl S., Ohno-Machado L. Logistic regression and artificial neural network classification models: a methodology review. Journal of Biomedical Informatics 2002, 35:352-359.
90. Khemphila A., Boonjing V. Comparing performances of logistic regression, decision trees, and neural networks for classifying heart disease patients. International conference on computer information systems and industrial management applications (CISIM) 2010, 193-198.
91. Vapnik V.N. The nature of statistical learning theory 1995, Springer-Verlag New York, Inc., New York, NY, USA.
92. Tan K.C., Teoh E.J., Yu Q., Goh K.C. A hybrid evolutionary algorithm for attribute selection in data mining. Expert Systems with Applications 2009, 36:8616-8630.
93. Burges C.J.C. A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery 1998, 2:121-167.
94. Bodenreider O. The Unified Medical Language System (UMLS): integrating biomedical terminology. Nucleic Acids Research 2004, 32:267-270.
95. De Lima L.R.S., Laender A.H.F., Ribeiro-Neto B.A. A hierarchical approach to the automatic categorization of medical documents. Proceedings of the seventh international conference on information and knowledge management 1998, 132-139. ACM Press.
96. Forrey A.W., McDonald C.J., DeMoor G., Huff S.M., Leavelle D., Leland D., et al. Logical observation identifier names and codes (LOINC) database: a public use set of codes and names for electronic reporting of clinical laboratory test results. Clinical Chemistry 1996, 42:81-90.
97. Lowe H.J., Barnett G.O. Understanding and using the medical subject headings (MeSH) vocabulary to perform literature searches. Journal of the American Medical Association 1994, 271:1103-1108.
98. Wang A.Y., Barrett J.W., Bentley T., Markwell D., Price C., Spackman K.A., et al. Mapping between SNOMED RT and clinical terms version 3: a key component of the SNOMED CT development process. Proceedings of the AMIA symposium 2001, 741-745.
99. Lu H.-M., Zeng D., Chen H. Ontology-based automatic chief complaints classification for syndromic surveillance. IEEE international conference on systems, man and cybernetics, 2006. SMC' 06, vol. 2 2006, 1137-1142.
100. Cimino J.J. Desiderata for controlled medical vocabularies in the twenty-first century. Methods of Information in Medicine 1998, 37:394-403.
101. Cimino J., Zhu X. The practical impact of ontologies on biomedical informatics. IMIA Yearbook of Medical Informatics 2006, 1:124-135.
102. Lee Y., Supekar K., Geller J. Ontology integration: experience with medical terminologies. Computers in Biology and Medicine 2006, 36:893-919.
103. Melzack R. The McGill Pain Questionnaire: major properties and scoring methods. Pain 1975, 1:277-299.
104. Wainer H. Computerized adaptive testing: a primer 2000, Lawrence Erlbaum Associates, Mahwah, New Jersey. 2nd ed.
105. Hambleton R.K., Swaminathan H. Item response theory: principles and applications/Ronald K. Hambleton, Hariharan Swaminathan 1985, Kluwer-Nijhoff Pub.; Distributors for North America Kluwer Boston, Boston; Hingham, MA, USA.
106. Selker H.P., Beshansky J.R., Griffith J.L., Aufderheide T.P., Ballin D.S., Bernard S.A., et al. Use of the acute cardiac ischemia time-insensitive predictive instrument (ACI-TIPI) to assist with triage of patients with chest pain or other symptoms suggestive of acute cardiac ischemia: a multicenter, controlled clinical trial. Annals of Internal Medicine 1998, 129:845-855.
107. Van der Heijde D.M., van't Hof M.A., van Riel P.L., Theunisse L.A., Lubberts E.W., van Leeuwen M.A., et al. Judging disease activity in clinical practice in rheumatoid arthritis: first step in the development of a disease activity score. Annals of the Rheumatic Diseases 1990, 49:916-920.
108. Ramey D.R., Raynauld J.-P., Fries J.F. The health assessment questionnaire 1992. Status and review. Arthritis & Rheumatism 1992, 5:119-129.
109. Abad-Grau M.M., Ierache J., Cervino C., Sebastiani P. Evolution and challenges in the design of computational systems for triage assistance. Journal of Biomedical Informatics 2008, 41:432-441.
110. Midboe A., Lewis E., Cronkite R., Chambers D., Goldstein M., Kerns R., et al. Behavioral medicine perspectives on the design of health information technology to improve decision-making, guideline adherence, and care coordination in chronic pain management. Translational Behavioral Medicine 2011, 1:35-44.
111. Roshanov P.S., Misra S., Gerstein H.C., Garg A.X., Sebaldt R.J., Mackay J.A., et al. Computerized clinical decision support systems for chronic disease management: a decision-maker-researcher partnership systematic review. Implementation Science 2011, 6:92+.
112. Smith M.Y., DePue J.D., Rini C. Computerized decision-support systems for chronic pain management in primary care. Pain Medicine 2007, 8:S155-S166.
113. Ball M.J. Hospital information systems: perspectives on problems and prospects, 1979 and 2002. International Journal of Medical Informatics 2003, 69:83-89.
114. Yina W. Application of EHR in health care. 2010 second international conference on multimedia and information technology (MMIT), vol. 1 2010, 60-63.
115. Wang M., Lau C., Matsen F.A.I., Kim Y. Personal health information management system and its application in referral management. IEEE Transactions on Information Technology in Biomedicine 2004, 8:287-297.
116. Erl T. Service-oriented architecture: concepts, technology, and design 2005, Prentice Hall PTR, Upper Saddle River, NJ, USA.
117. Hayes B. Cloud computing. Communications of the ACM 2008, 51:9-11.
118. Pombo N., Araújo P., Viana J., Junior B., Serrano R. Contribution of web services to improve pain diaries experience. Lecture notes in engineering and computer science: proceedings of the international multiconference of engineers and computer scientists, vol. 1 2012, 589-592.
119. Stone A.A., Shiffman S. Ecological momentary assessment (EMA) in behavorial medicine. Annals of Behavioral Medicine 1994, 16:199-202.
120. Rosser B.A., Eccleston C. Smartphone applications for pain management. Journal of Telemedicine and Telecare 2011, 17:308-312.