Research Inspirations

Journal Club

In our weekly team meetings, we host a journal club where each lab member is expected to present a research paper in turn. This forum encourages critical thinking and keeps our team updated on recent advancements in the field. Below are some of the papers previously presented by our members:

  • Richens JG, Lee CM, Johri S. Improving the accuracy of medical diagnosis with causal machine learning. Nature Communications. 2020;11(1). doi:10.1038/s41467-020-17419-7
  • Grolleau F, Petit F, Gaudry S, et al. Personalizing renal replacement therapy initiation in the intensive care unit: a reinforcement learning-based strategy with external validation on the AKIKI randomized controlled trials. Journal of the American Medical Informatics Association. 2024;31(5):1074-1083. doi:10.1093/jamia/ocae004
  • Pan HC, Chen HY, Teng NC, et al. Recovery Dynamics and Prognosis after Dialysis for Acute Kidney Injury. JAMA Network Open. 2024;7(3):E240351. doi:10.1001/jamanetworkopen.2024.0351
  • Yang J, Soltan AAS, Eyre DW, Yang Y, Clifton DA. An adversarial training framework for mitigating algorithmic biases in clinical machine learning. npj Digital Medicine. 2023;6(1). doi:10.1038/s41746-023-00805-y
  • Dutta S, McEvoy DS, Dunham LN, et al. External Validation of a Commercial Acute Kidney Injury Predictive Model. NEJM AI. 2024;1(3). doi:10.1056/aioa2300099

Paper Reading List

The reading list below is tailored for new lab members, featuring a selection of the latest papers from leading journals. These publications reflect a broad spectrum of topics that are loosely connected to our main research interests. All new members are encouraged to explore these articles to stay at the forefront of emerging trends and developments in our field:

Acute Kidney Injury:

  • Feng Y, Wang AY, Jun M, et al. Characterization of Risk Prediction Models for Acute Kidney Injury A Systematic Review and Meta-analysis. JAMA Network Open. 2023;6(5). doi:10.1001/jamanetworkopen.2023.13359
  • Haredasht FN, Vanhoutte L, Vens C, et al. Validated risk prediction models for outcomes of acute kidney injury: a systematic review. BMC Nephrology. 2023;24(1). doi:10.1186/s12882-023-03150-0
  • Hu J, Xu J, Li M, et al. Identification and validation of an explainable prediction model of acute kidney injury with prognostic implications in critically ill children: a prospective multicenter cohort study. eClinicalMedicine. 2024;68. doi:10.1016/j.eclinm.2023.102409
  • Rank N, Pfahringer B, Kempfert J, et al. Deep-learning-based real-time prediction of acute kidney injury outperforms human predictive performance. npj Digital Medicine. 2020;3(1). doi:10.1038/s41746-020-00346-8
  • Wu C, Zhang Y, Nie S, et al. Predicting in-hospital outcomes of patients with acute kidney injury. Nature Communications. 2023;14(1). doi:10.1038/s41467-023-39474-6

Dialysis:

  • Cooper BA, Branley P, Bulfone L, et al. A Randomized, Controlled Trial of Early versus Late Initiation of Dialysis. New England Journal of Medicine. 2010;363(7):609-619. doi:10.1056/nejmoa1000552
  • Hemmige V, Deshpande P, Norris KC, et al. Geographic Dialysis Facility Density and Early Dialysis Initiation. JAMA Network Open. 2024;7(1):E2350009. doi:10.1001/jamanetworkopen.2023.50009
  • McCoy IE, Weinhandl E, Hussein W, Hsu CY. Initial Management and Potential Opportunities to Deprescribe Dialysis among Patients with AKI-D Patients after Hospital Discharge. Journal of the American Society of Nephrology. 2023;34(12):1949-1951. doi:10.1681/ASN.0000000000000225
  • The STARRT‐AKI Investigators. Timing of Initiation of Renal-Replacement Therapy in Acute Kidney Injury. New England Journal of Medicine. 2020;383(3):240-251. doi:10.1056/NEJMoa2000741

Transfer Learning:

  • Ebbehoj A, Thunbo MØ, Andersen OE, et al. Transfer learning for non-image data in clinical research: A scoping review. PLOS Digital Health. 2022;1(2):e0000014. doi:10.1371/journal.pdig.0000014
  • Gao Y, Cui Y. Deep transfer learning for reducing health care disparities arising from biomedical data inequality. Nature Communications. 2020;11(1). doi:10.1038/s41467-020-18918-3
  • Qi T, Wu F, Wu C, et al. Differentially private knowledge transfer for federated learning. Nature Communications. 2023;14(1). doi:10.1038/s41467-023-38794-x
  • Radhakrishnan A, Ruiz Luyten M, Prasad N, et al. Transfer Learning with Kernel Methods. Nature Communications. 2023;14(1). doi:10.1038/s41467-023-41215-8
  • Zhao Z, Alzubaidi L, Zhang J, et al. A comparison review of transfer learning and self-supervised learning: Definitions, applications, advantages and limitations. Expert Systems with Applications. 2024;242. doi:10.1016/j.eswa.2023.122807

Federated Learning:

  • Dayan I, Roth HR, Zhong A, et al. Federated learning for predicting clinical outcomes in patients with COVID-19. Nature Medicine. 2021;27(10):1735-1743. doi:10.1038/s41591-021-01506-3
  • Kaissis G, Ziller A, Passerat-Palmbach J, et al. End-to-end privacy preserving deep learning on multi-institutional medical imaging. Nature Machine Intelligence. 2021;3(6):473-484. doi:10.1038/s42256-021-00337-8
  • Zhang D, Tong J, Jing N, et al. Learning competing risks across multiple hospitals: one-shot distributed algorithms. Journal of the American Medical Informatics Association. 2024;31(5):1102-1112. doi:10.1093/jamia/ocae027
  • Zhang D, Tong J, Stein R, et al. One-shot distributed algorithms for addressing heterogeneity in competing risks data across clinical sites. Journal of Biomedical Informatics. 2024;150. doi:10.1016/j.jbi.2024.104595

Causal Learning:

  • Feuerriegel S, Frauen D, Melnychuk V, et al. Causal machine learning for predicting treatment outcomes. Nature Medicine. 2024;30(4):958-968. doi:10.1038/s41591-024-02902-1
  • Madumal P, Miller T, Sonenberg L, Vetere Victoria F. Explainable Reinforcement Learning through a Causal Lens. www.aaai.org
  • Prosperi M, Guo Y, Sperrin M, et al. Causal inference and counterfactual prediction in machine learning for actionable healthcare. Nature Machine Intelligence. 2020;2(7):369-375. doi:10.1038/s42256-020-0197-y