Explainable AI for Medical Event Prediction for Heart Failure Patients
Author:
Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-34344-5_12
Reference24 articles.
1. Adams, K.F., Fonarow, G.C., Emerman, C.L., et al.: Characteristics and outcomes of patients hospitalized for heart failure in the united states: Rationale, design, and preliminary observations from the first 100, 000 cases in the acute decompensated heart failure national registry (ADHERE). Am. Heart J. 149(2), 209–216 (2005). https://doi.org/10.1016/j.ahj.2004.08.005
2. Amann, J., Blasimme, A., Vayena, E., et al.: Explainability for artificial intelligence in healthcare. BMC Med. Inform. Decis. Mak. 20, 310 (2020)
3. Bundgaard, J.S., Thune, J.J., Torp-Pedersen, C., et al.: Self-reported health status and the associated risk of mortality in heart failure: the DANISH trial. J. Psychosom. Res. 137, 110220 (2020). https://doi.org/10.1016/j.jpsychores.2020.110220
4. Chen, Y., Qi, B.: Representation learning in intraoperative vital signs for heart failure risk prediction. BMC Med. Inform. Decis. Mak. 19, 260 (2019)
5. Costanzo, M.R., Guglin, M.E., Saltzberg, M.T., et al.: Ultrafiltration versus intravenous diuretics for patients hospitalized for acute decompensated heart failure. J. Am. Coll. Cardiol. 49(6), 675–683 (2007). https://doi.org/10.1016/j.jacc.2006.07.073
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