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Detecting and predicting septic shock early is crucial for the best possible outcome for patients.
Kumar, A., Roberts, D., Wood, K.E., Light, B., Parrillo, J.E., Sharma, S., Suppes, R., Feinstein, D., Zanotti, S., Taiberg, L., et al.: Duration of hypotension before initiation of effective antimicrobial therapy is the critical determinant of survival in human septic shock. Critical care medicine 34
2006
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Desautels, T., Calvert, J., Hoffman, J., Jay, M., Kerem, Y., Shieh, L., Shimabukuro, D., Chettipally, U., Feldman, M.D., Barton, C., et al.: Prediction of sepsis in the intensive care unit with minimal electronic health record data: a machine learning approach. JMIR medical informatics 4
2016
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Ribeiro, M.T., Singh, S., Guestrin, C.: ” why should i trust you?” explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining. pp. 1135–1144 (2016)
2016
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Singer, M., Deutschman, C.S., Seymour, C.W., Shankar-Hari, M., Annane, D., Bauer, M., Bellomo, R., Bernard, G.R., Chiche, J.D., Coopersmith, C.M., et al.: The third international consensus definitions for sepsis and septic shock (sepsis-3). Jama 315
2016
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Rhodes, A., Evans, L.E., Alhazzani, W., Levy, M.M., Antonelli, M., Ferrer, R., Kumar, A., Sevransky, J.E., Sprung, C.L., Nunnally, M.E., et al.: Surviving sepsis campaign: international guidelines for management of sepsis and septic shock: 2016. Intensive care medicine 43
2017
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Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30
2017
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Wachter, S., Mittelstadt, B., Russell, C.: Counterfactual explanations without opening the black box: Automated decisions and the gdpr. Harv. JL & Tech. 31
2017
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Komorowski, M., Celi, L.A., Badawi, O., Gordon, A.C., Faisal, A.A.: The artificial intelligence clinician learns optimal treatment strategies for sepsis in intensive care. Nature medicine 24
2018
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Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5
2018
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Rizzo, L., Longo, L.: A qualitative investigation of the degree of explainability of defeasible argumentation and non-monotonic fuzzy reasoning. In: Proceedings of the 26th AIAI Irish conference on artificial intelligence and cognitive science (2018)
2018
Cited alongside, same era.
Rizzo, L., Majnaric, L., Longo, L.: A comparative study of defeasible argumentation and non-monotonic fuzzy reasoning for elderly survival prediction using biomarkers. In: AI* IA 2018–Advances in Artificial Intelligence: XVIIth International Conference of the Italian Association for Artificial Intelligence, Trento, Italy, November 20–23, 2018, Proceedings 17. pp. 197–209. Springer (2018)
2018
Cited alongside, same era.
Rizzo, L., Longo, L.: Inferential models of mental workload with defeasible argumentation and non-monotonic fuzzy reasoning: a comparative study (2019)
Rizzo, L., Longo, L.: An empirical evaluation of the inferential capacity of defeasible argumentation, non-monotonic fuzzy reasoning and expert systems. Expert Systems with Applications 147
2020
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Vilone, G., Rizzo, L., Longo, L.: A comparative analysis of rule-based, model-agnostic methods for explainable artificial intelligence (2020)
2020
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Longo, L., Rizzo, L., Dondio, P.: Examining the modelling capabilities of defeasible argumentation and non-monotonic fuzzy reasoning. Knowledge-Based Systems 211
2021
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Vilone, G., Longo, L.: Notions of explainability and evaluation approaches for explainable artificial intelligence. Information Fusion 76
2021
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Vilone, G., Longo, L.: A quantitative evaluation of global, rule-based explanations of post-hoc, model agnostic methods. Frontiers in artificial intelligence 4
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2019
Cited alongside, same era.
Arrieta, A.B., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., García, S., Gil-López, S., Molina, D., Benjamins, R., et al.: Explainable artificial intelligence (xai): Concepts, taxonomies, opportunities and challenges toward responsible ai. Information fusion 58
2020
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Oreshkin, B.N., Carpov, D., Chapados, N., Bengio, Y.: N-beats: Neural basis expansion analysis for interpretable time series forecasting (2020)
2020
Cited alongside, same era.
O’Halloran, H.M., Kwong, K., Veldhoen, R.A., Maslove, D.M.: Characterizing the patients, hospitals, and data quality of the eicu collaborative research database. Critical Care Medicine 48
2020
Cited alongside, same era.
2021
Later among the works it cites.
Vilone, G., Longo, L.: A novel human-centred evaluation approach and an argument-based method for explainable artificial intelligence. In: Artificial Intelligence Applications and Innovations: 18th IFIP WG 12.5 International Conference, AIAI 2022, Hersonissos, Crete, Greece, June 17–20, 2022, Proceedings, Part I. pp. 447–460. Springer (2022)
2022
Later among the works it cites.
Rizzo, L., Longo, L.: Comparing and extending the use of defeasible argumentation with quantitative data in real-world contexts. Information Fusion 89
2023
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