Skip to content
DraconiticResearch
Search Sources API
203,952 studies31 sourcesUpdated 10 min ago
Peer-reviewedCC BY-NC-ND 4.0Scientific Reports

An interpretable machine learning framework for retrospective attribution of hospitalization costs in patients with chronic kidney disease

Related

  1. Interpretable Machine Learning for Compressive Strength Prediction of GGBS–Fly Ash Blended Concrete: Generalization Across Training-Set Compositions and Feature-Attribution Sensitivity
    Materials (Basel, Switzerland)Peer-reviewedCC BYFull textEurope PMC
  2. A falsifiable framework for retrospective neuro-type (ADHD/ASD/AuDHD) attribution in historical figures: a Registered Report Stage 1 protocol
    Xin Xu
    ZenodoPreprintCC BY 4.0Full textZenodo
  3. Interpretable machine learning for predicting intraoperative oxygenation impairment during one-lung ventilation in thoracic surgery: a retrospective cohort study
    Yijiang Deng, Jiarui Chen, Wenzhi Liu +1
    Scientific ReportsPeer-reviewedCC BY-NC-ND 4.0Full textGeneral medicineCrossref
  4. Eye-movement-based machine learning for identifying Parkinson’s disease dementia in a retrospective cohort: an interpretable model-development study
    Jing Wu, Qing Liu, Song Yang +5
    Frontiers in aging neurosciencePeer-reviewedCC BYFull textGeneral medicineEurope PMC
  5. KT-CBHNET: a spatio-temporal deep learning framework for interpretable ECG signal classification for cardiovascular disease prediction
    Krantee Singh, Vasudev Dehalwar
    Scientific ReportsPeer-reviewedCC BY-NC-ND 4.0Full textCardiovascular medicineCrossref
BibTeX
@article{wu2026interpretable,
  title = {{An interpretable machine learning framework for retrospective attribution of hospitalization costs in patients with chronic kidney disease}},
  author = {Xusheng Wu and Zhen Peng and Fei Liu and Rui Chen and Peixuan Chen and Ling Bai and Wei Zhang and Zirui Xin and Pingping Dai and Linyong Xu},
  journal = {Scientific Reports},
  year = {2026},
  month = oct,
  doi = {10.1038/s41598-026-74741-8},
  url = {https://www.nature.com/articles/s41598-026-74741-8},
  note = {CC BY-NC-ND 4.0},
}
RIS
TY  - JOUR
TI  - An interpretable machine learning framework for retrospective attribution of hospitalization costs in patients with chronic kidney disease
AU  - Xusheng Wu
AU  - Zhen Peng
AU  - Fei Liu
AU  - Rui Chen
AU  - Peixuan Chen
AU  - Ling Bai
AU  - Wei Zhang
AU  - Zirui Xin
AU  - Pingping Dai
AU  - Linyong Xu
PY  - 2026
DA  - 2026/10/14
JO  - Scientific Reports
DO  - 10.1038/s41598-026-74741-8
UR  - https://www.nature.com/articles/s41598-026-74741-8
SN  - 2045-2322
ER  -