Outputs (4)

Clinical dialogue transcription error correction using Seq2Seq models. (2022)
Presentation / Conference Contribution
NANAYAKKARA, G., WIRATURNGA, N., CORSAR, D., MARTIN, K. and WIJEKOON, A. 2022. Clinical dialogue transcription error correction using Seq2Seq models. In Shaban-Nejad, A., Michalowski, M. and Bianco, S. (eds.) Multimodal AI in healthcare: a paradigm shift in health intelligence; selected papers from the 6th International workshop on health intelligence (W3PHIAI-22), co-located with the 34th AAAI (Association for the Advancement of Artificial Intelligence) Innovative applications of artificial intelligence (IAAI-22), 28 February - 1 March 2022, [virtual event]. Studies in computational intelligence, 1060. Cham: Springer [online], pages 41-57. Available from: https://doi.org/10.1007/978-3-031-14771-5_4

Good communication is critical to good healthcare. Clinical dialogue is a conversation between health practitioners and their patients, with the explicit goal of obtaining and sharing medical information. This information contributes to medical decis... Read More about Clinical dialogue transcription error correction using Seq2Seq models..

How close is too close? Role of feature attributions in discovering counterfactual explanations. (2022)
Presentation / Conference Contribution
WIJEKOON, A., WIRATUNGA, N., NKISI-ORJI, I., PALIHAWADANA, C., CORSAR, D. and MARTIN, K. 2022. How close is too close? Role of feature attributions in discovering counterfactual explanations. In Keane, M.T. and Wiratunga, N. (eds.) Case-based reasoning research and development: proceedings of the 30th International conference on case-based reasoning (ICCBR 2022), 12-15 September 2022, Nancy, France. Lecture notes in computer science, 13405. Cham: Springer [online], pages 33-47. Available from: https://doi.org/10.1007/978-3-031-14923-8_3

Counterfactual explanations describe how an outcome can be changed to a more desirable one. In XAI, counterfactuals are "actionable" explanations that help users to understand how model decisions can be changed by adapting features of an input. A cas... Read More about How close is too close? Role of feature attributions in discovering counterfactual explanations..

A case-based approach for content planning in data-to-text generation. (2022)
Presentation / Conference Contribution
UPADHYAY, A. and MASSIE, S. 2022. A case-based approach for content planning in data-to-text generation. In Keane, M.T. and Wiratunga, N. (eds.) Case-based reasoning research and development: proceedings of the 30th International conference on case-based reasoning (ICCBR 2022), 12-15 September 2022, Nancy, France. Lecture notes in computer science, 13405. Cham: Springer [online], pages 380-394. Available from: https://doi.org/10.1007/978-3-031-14923-8_25

The problem of Data-to-Text Generation (D2T) is usually solved using a modular approach by breaking the generation process into some variant of planning and realisation phases. Traditional methods have been very good at producing high quality texts b... Read More about A case-based approach for content planning in data-to-text generation..

Using artificial intelligence methods for systematic review in health sciences: a systematic review. (2022)
Journal Article
BLAIZOT, A., VEETTIL, S.K., SAIDOUNG, P., MORENO-GARCIA, C.F., WIRATUNGA, N., ACEVES-MARTINS, M., LAI, N.M. and CHAIYAKUNAPRUK, N. 2022. Using artificial intelligence methods for systematic review in health sciences: a systematic review. Research synthesis methods [online], 13(3), pages 353-362. Available from: https://doi.org/10.1002/jrsm.1553

The exponential increase in published articles makes a thorough and expedient review of literature increasingly challenging. This review delineated automated tools and platforms that employ artificial intelligence (AI) approaches and evaluated the re... Read More about Using artificial intelligence methods for systematic review in health sciences: a systematic review..