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Book Chapter

Enhancing Disaster Resilience Studies: Leveraging Linked Data and Natural Language Processing for Consistent Open-Ended Interviews

  • Milad Katebi
  • Mani Poshdar
  • Mostafa Babaeian Jelodar
  • Morteza Zihayat Kermani

Researchers have long focused on disaster resilience to mitigate calamity disruption. Disaster resilience is a complex and multi-faceted concept that is challenging to measure. Quantitative methods have traditionally been used to assess disaster resilience, but a growing interest in qualitative methods like open-ended interviews has emerged to understand experiences and perspectives. To gain deep and consistent knowledge, an open-ended interview should focus on an interviewee’s point of view and ask follow-up questions from a knowledge base that consists of relevant information; otherwise, this can lead an open-ended interview to deviate from the interviewee’s point of view to the interviewer’s point of view. In contrast to what is desired, individual interviews with last year's students in the field of civil engineering with a predefined and limited knowledge base demonstrated inconsistency in asking a follow-up question from an already existing open-ended interview. To tackle this gap, firstly, we suggest a knowledge base that can be built from peer-reviewed papers published in the disaster resilience field; secondly, we suggest a Natural Language Processing based Decision Support System using Sentence Embedding that can analyze the interviewee’s response and find resources from the knowledge base to assist the interviewer in making a consistent follow-up question

  • Keywords:
  • Disaster resilience; Decision support systems; Open-ended interviews; Knowledge management; NLP,
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Milad Katebi

Auckland University, New Zealand

Mani Poshdar

Auckland University, New Zealand - ORCID: 0000-0001-9132-2985

Mostafa Babaeian Jelodar

Massey University, New Zealand - ORCID: 0000-0003-1956-7384

Morteza Zihayat Kermani

Toronto Metropolitan University, Canada

  1. Afshar, M., Adelaine, S., Resnik, F., Mundt, M. P., Long, J., Leaf, M., … others. (2023). Deployment of Real-time Natural Language Processing and Deep Learning Clinical Decision Support in the Electronic Health Record: Pipeline Implementation for an Opioid Misuse Screener in Hospitalized Adults. JMIR Medical Informatics, 11, e44977. DOI: 10.2196/44977
  2. Arnott, D. (2006). Cognitive biases and decision support systems development: A design science approach. Information Systems Journal, 16(1), 55–78. DOI: 10.1111/j.1365-2575.2006.00208.x
  3. Bagheri, E., Ensan, F., & Gasevic, D. (2012). Decision support for the software product line domain engineering lifecycle. Automated Software Engineering, 19(3), 335–377. DOI: 10.1007/s10515-011-0099-7
  4. Barale, C. (2022). Human-centered computing in legal NLP-An application to refugee status determination. Proceedings of the Second Workshop on Bridging Human–Computer Interaction and Natural Language Processing, 28–33. DOI: 10.3390/app11020870
  5. Barr, P. J., Haslett, W., Dannenberg, M. D., Oh, L., Elwyn, G., Hassanpour, S., … others. (2021). An audio personal health library of clinic visit recordings for patients and their caregivers (HealthPAL): User-centered design approach. Journal of Medical Internet Research, 23(10), e25512. DOI: 10.2196/25512
  6. Bautista, Y. J. P., Aló, R., & Wang, N. (2020). Deep learning, cloud computing for credit/debit industry analysis of consumer behavior. 2020 7th IEEE International Conference on Cyber Security and Cloud Computing (CSCloud)/2020 6th IEEE International Conference on Edge Computing and Scalable Cloud (EdgeCom), 1–7. IEEE. DOI: 10.1109/CSCloud-EdgeCom49738.2020.00010
  7. Bazzan, J., Echeveste, M. E., Formoso, C. T., Altenbernd, B., & Barbian, M. H. (2023). An Information Management Model for Addressing Residents’ Complaints through Artificial Intelligence Techniques. Buildings, 13(3), 737. DOI: 10.3390/buildings13030737
  8. Berquand, A., Murdaca, F., Riccardi, A., Soares, T., Generé, S., Brauer, N., & Kumar, K. (2019). Artificial intelligence for the early design phases of space missions. 2019 IEEE Aerospace Conference, 1–20. IEEE. DOI: 10.1109/AERO.2019.8742082
  9. Cai, H., Lam, N. S., Qiang, Y., Zou, L., Correll, R. M., & Mihunov, V. (2018). A synthesis of disaster resilience measurement methods and indices. International Journal of Disaster Risk Reduction, 31, 844–855. DOI: 10.1016/j.ijdrr.2018.07.015
  10. Chaichulee, S., Promchai, C., Kaewkomon, T., Kongkamol, C., Ingviya, T., & Sangsupawanich, P. (2022). Multi-label classification of symptom terms from free-text bilingual adverse drug reaction reports using natural language processing. PLoS One, 17(8), e0270595. DOI: 10.1371/journal.pone.0270595
  11. Flores, R., Tlachac, M., Toto, E., & Rundensteiner, E. (2022a). AudiFace: Multimodal Deep Learning for Depression Screening. Machine Learning for Healthcare Conference, 609–630. PMLR. Retrieved from https://proceedings.mlr.press/v182/flores22a.html
  12. Flores, R., Tlachac, M., Toto, E., & Rundensteiner, E. (2022b). Transfer learning for depression screening from follow-up clinical interview questions. In Deep Learning Applications, Volume 4 (pp. 53–78). Springer. Retrieved from DOI: 10.1007/978-981-19-6153-3_3
  13. Fujimori, R., Liu, K., Soeno, S., Naraba, H., Ogura, K., Hara, K., … others. (2022). Acceptance, barriers, and facilitators to implementing artificial intelligence–based decision support systems in emergency departments: Quantitative and qualitative evaluation. JMIR Formative Research, 6(6), e36501. DOI: 10.2196/36501
  14. Gluyas, H., & Morrison, P. (2014). Human factors and medication errors: A case study. Nursing Standard (2014+), 29(15), 37. DOI: 10.7748/ns.29.15.37.e9520
  15. Harrison, C. G., & Williams, P. R. (2016). A systems approach to natural disaster resilience. Simulation Modelling Practice and Theory, 65, 11–31. DOI: 10.1016/j.simpat.2016.02.008
  16. Højen, A. R., Elberga, P. B., & Andersena, S. K. (2014). SNOMED CT adoption in Denmark-why is it so hard. EHealth-For Continuity of Care: Proceedings of MIE2014, 205, 226. DOI: 10.3233/978-1-61499-432-9-226
  17. Huang, Y., Liu, D.-R., & Lee, S.-J. (2023). Talent recommendation based on attentive deep neural network and implicit relationships of resumes. Information Processing & Management, 60(4), 103357. DOI: 10.1016/j.ipm.2023.103357
  18. Huang, Y., Zisook, D., Chen, Y., Selter, M., Minardi, P., & Mattison, J. (2011). Lessons learned in improving the adoption of a real-time NLP decision support system. 2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW), 643–648. DOI: 10.1109/BIBMW.2011.6112446
  19. Hunt, M. R. (2009). Strengths and challenges in the use of interpretive description: Reflections arising from a study of the moral experience of health professionals in humanitarian work. Qualitative Health Research, 19(9), 1284–1292. DOI: 10.1177/1049732309344612
  20. Ivanchikj, A., Serbout, S., & Pautasso, C. (2020). From text to visual BPMN process models: Design and evaluation. Proceedings of the 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems, 229–239. DOI: 10.1145/3365438.3410990
  21. Jameel, B., Shaheen, S., & Majid, U. (2018). Introduction to qualitative research for novice investigators. Undergraduate Research in Natural and Clinical Science and Technology Journal, 2, 1–6. DOI: 10.26685/urncst.57
  22. Jan, Z., Ai-Ansari, N., Mousa, O., Abd-Alrazaq, A., Ahmed, A., Alam, T., & Househ, M. (2021). The role of machine learning in diagnosing bipolar disorder: Scoping review. Journal of Medical Internet Research, 23(11), e29749. DOI: 10.2196/29749
  23. Jenkins, C., Jenkins, T., Person, T. N., Robinson, P. N., Rahm, A. K., Walton, N. A., … others. (2021). User testing of a diagnostic decision support system with machine-assisted chart review to facilitate clinical genomic diagnosis. BMJ Health & Care Informatics, 28(1). DOI: 10.1136/bmjhci-2021-100331
  24. Kallio, H., Pietilä, A.-M., Johnson, M., & Kangasniemi, M. (2016). Systematic methodological review: Developing a framework for a qualitative semi-structured interview guide. Journal of Advanced Nursing, 72(12), 2954–2965. DOI: 10.1111/jan.13031
  25. Kramer, H. S., & Drews, F. A. (2017). Checking the lists: A systematic review of electronic checklist use in health care. Journal of Biomedical Informatics, 71, S6–S12. DOI: 10.1016/j.jbi.2016.09.006
  26. Ku, C.-H., & Leroy, G. (2014). A decision support system: Automated crime report analysis and classification for e-government. Government Information Quarterly, 31(4), 534–544. DOI: 10.1016/j.giq.2014.08.003
  27. Lau, C., Zhu, X., & Chan, W.-Y. (2023). Automatic depression severity assessment with deep learning using parameter-efficient tuning. Frontiers in Psychiatry, 14, 1160291. DOI: 10.3389/fpsyt.2023.1160291
  28. Lefebvre, M. (2009). Elementary probability. In Basic Probability Theory with Applications (pp. 27–53). New York, NY: Springer New York. DOI: 10.1007/978-0-387-74995-2_2
  29. Mai, P. X., Goknil, A., Shar, L. K., Pastore, F., Briand, L. C., & Shaame, S. (2018). Modeling security and privacy requirements: A use case-driven approach. Information and Software Technology, 100, 165–182. DOI: 10.1016/j.infsof.2018.04.007
  30. Malalgoda, C., Amaratunga, D., & Haigh, R. (2014). Challenges in creating a disaster resilient built environment. Procedia Economics and Finance, 18, 736–744. DOI: 10.1016/S2212-5671(14)00997-6
  31. Nanjundeswaraswamy, T., & Divakar, S. (2021). Determination of sample size and sampling methods in applied research. Proceedings on Engineering Sciences, 3(1), 25–32. DOI: 10.24874/PES03.01.003
  32. Nogueira, R., Yang, W., Lin, J., & Cho, K. (2019). Document expansion by query prediction. arXiv Preprint arXiv:1904.08375. DOI: 10.48550/arXiv.1904.08375
  33. Ouyang, M., Dueñas-Osorio, L., & Min, X. (2012). A three-stage resilience analysis framework for urban infrastructure systems. Structural Safety, 36, 23–31. DOI: 10.1016/j.strusafe.2011.12.004
  34. Rachana, V. T., Vishwas, N. H., & Priyanka, N. C. (2022). HR based Chatbot using Deep Neural Network. 2022 International Conference on Inventive Computation Technologies (ICICT), 130–139. DOI: 10.1109/ICICT54344.2022.9850474
  35. Ryu, S., Kim, S., Choi, J., Yu, H., & Lee, G. G. (2017). Neural sentence embedding using only in-domain sentences for out-of-domain sentence detection in dialog systems. Pattern Recognition Letters, 88, 26–32. DOI: 10.1016/j.patrec.2017.01.008
  36. Saloun, P., Ondrejka, A., Malčík, M., & Zelinka, I. (2016). Personality disorders identification in written texts. AETA 2015: Recent Advances in Electrical Engineering and Related Sciences, 143–154. Springer. DOI: 10.1007/978-3-319-27247-4_13
  37. Santelices, L. C., Wang, Y., Severyn, D., Druzdzel, M. J., Kormos, R. L., & Antaki, J. F. (2010). Development of a Hybrid Decision Support Model for Optimal Ventricular Assist Device Weaning. The Annals of Thoracic Surgery, 90(3), 713–720. DOI: 10.1016/j.athoracsur.2010.03.073
  38. Sharda, P., Das, A. K., Cohen, T. A., & Patel, V. (2006). Customizing clinical narratives for the electronic medical record interface using cognitive methods. International Journal of Medical Informatics, 75(5), 346–368. DOI: 10.1016/j.ijmedinf.2005.07.027
  39. Sultanum, N., Naeem, F., Brudno, M., & Chevalier, F. (2022). ChartWalk: Navigating large collections of text notes in electronic health records for clinical chart review. IEEE Transactions on Visualization and Computer Graphics, 29(1), 1244–1254. DOI: 10.1109/TVCG.2022.3209444
  40. Toto, E., Tlachac, M., & Rundensteiner, E. A. (2021). Audibert: A deep transfer learning multimodal classification framework for depression screening. Proceedings of the 30th ACM International Conference on Information & Knowledge Management, 4145–4154. DOI: 10.1145/3459637.3481895
  41. Uttarwar, S., Gambani, S., Thakkar, T., & Mulla, N. (2020). Artificial intelligence based system for preliminary rounds of recruitment process. Computational Vision and Bio-Inspired Computing: ICCVBIC 2019, 909–920. Springer. DOI: 10.1007/978-3-030-37218-7_97
  42. Wang, C., Liu, D., Tao, K., Cui, X., Wang, G., Zhao, Y., & Liu, Z. (2022). A Multi-modal Feature Layer Fusion Model for Assessment of Depression Based on Attention Mechanisms. 2022 15th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), 1–6. IEEE. DOI: 10.1109/CISP-BMEI56279.2022.9979894
  43. Wang, J., Yang, J., Zhang, H., Lu, H., Skreta, M., Husić, M., … Brudno, M. (2022). PhenoPad: Building AI enabled note-taking interfaces for patient encounters. NPJ Digital Medicine, 5(1), 12. DOI: 10.1038/s41746-021-00555-9
  44. Warren, J. R. (1998). Better, more cost-effective intake interviews. IEEE Intelligent Systems and Their Applications, 13(1), 40–48. DOI: 10.1109/5254.653223
  45. Warren, Warren, & Freedman. (1994). Interviewing expertise in primary care medicine: A knowledge-based support system. 1994 Proceedings of the Twenty-Seventh Hawaii International Conference on System Sciences, 3, 173–182 DOI: 10.1109/HICSS.1994.323354
  46. Wreathall, J., & Reason, J. (1992). Human errors and disasters. Conference Record for 1992 Fifth Conference on Human Factors and Power Plants, 448–452. DOI: 10.1109/HFPP.1992.283368
  47. Xiao, Y., & Watson, M. (2019). Guidance on conducting a systematic literature review. Journal of Planning Education and Research, 39(1), 93–112. DOI: 10.1177/0739456X17723971
  48. Xiao, Z., Zhou, M. X., Chen, W., Yang, H., & Chi, C. (2020). If I hear you correctly: Building and evaluating interview chatbots with active listening skills. Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, 1–14. DOI: 10.1145/3313831.3376131
  49. Yadav, U., & Sharma, A. K. (2023). A novel automated depression detection technique using text transcript. International Journal of Imaging Systems and Technology, 33(1), 108–122. DOI: 10.1002/ima.22793
  50. Yang, P., Fang, H., & Lin, J. (2017). Anserini: Enabling the use of lucene for information retrieval research. Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, 1253–1256. DOI: 10.1145/3077136.3080721
  51. Yang, X., Joukova, A., Ayanso, A., & Zihayat, M. (2022). Social influence-based contrast language analysis framework for clinical decision support systems. Decision Support Systems, 159, 113813. DOI: 10.1016/j.dss.2022.113813
  52. Young, O., Shahar, Y., Liel, Y., Lunenfeld, E., Bar, G., Shalom, E., … Goldstein, M. K. (2007). Runtime application of Hybrid-Asbru clinical guidelines. Journal of Biomedical Informatics, 40(5), 507–526. DOI: 10.1016/j.jbi.2006.12.004
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  • Publication Year: 2023
  • Pages: 998-1009

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  • Publication Year: 2023

Chapter Information

Chapter Title

Enhancing Disaster Resilience Studies: Leveraging Linked Data and Natural Language Processing for Consistent Open-Ended Interviews

Authors

Milad Katebi, Mani Poshdar, Mostafa Babaeian Jelodar, Morteza Zihayat Kermani

DOI

10.36253/979-12-215-0289-3.100

Peer Reviewed

Publication Year

2023

Copyright Information

© 2023 Author(s)

Content License

CC BY-NC 4.0

Metadata License

CC0 1.0

Bibliographic Information

Book Title

CONVR 2023 - Proceedings of the 23rd International Conference on Construction Applications of Virtual Reality

Book Subtitle

Managing the Digital Transformation of Construction Industry

Editors

Pietro Capone, Vito Getuli, Farzad Pour Rahimian, Nashwan Dawood, Alessandro Bruttini, Tommaso Sorbi

Peer Reviewed

Publication Year

2023

Copyright Information

© 2023 Author(s)

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CC BY-NC 4.0

Metadata License

CC0 1.0

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Firenze University Press

DOI

10.36253/979-12-215-0289-3

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979-12-215-0289-3

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979-12-215-0257-2

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2704-601X

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2704-5846

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