Skip to content
DraconiticResearch
Search Sources API
190,332 studies31 sourcesUpdated 1 min ago
Peer-reviewedCC BY-NC-ND 4.0Scientific Reports

An explainable artificial intelligence framework using electrocardiograms to predict mortality and major adverse cardiovascular events

  • Jerry Peng
  • Michael D. Glidden
  • Sai Rahul Ponnana
  • Santosh K. Sirasapalli
  • Tong Zhang
  • Mark Yoder
  • Nour Tashtish
  • Judith A. Mackall
  • Ravi Ramani
  • Sadeer Al-Kindi
  • Yu Yin
  • Jing Ma
2 more authors
  • Vipin Chaudhary
  • Sanjay Rajagopalan
Open at sourcePDFDOICite

Related

  1. An explainable artificial intelligence system for prognostic assessment and decision support in diabetic foot ulcer care: a multicentre derivation and validation study with prospective randomised evaluation
    Jia-Yu Zhang, Yangyang Zhuang, Qingping Cao +9
    eClinicalMedicinePeer-reviewedCC BY-NC-ND 4.0General medicineCrossref
  2. Artificial Intelligence for Cardiovascular Health in China
    Hangkuan Liu, Linjie Li, Xin Zhou +1
    JACC AsiaPeer-reviewedCC BY 4.0Full textEpidemiology & public healthPMC
  3. Artificial Intelligence in Gastroenterology: Towards Explainable, Generalisable and Clinically Actionable Decision Support.
    İsmail Baydili, Burak Tasci, Sengul Dogan +1
    Diagnostics (Basel, Switzerland)Peer-reviewedCC BYFull textEpidemiology & public healthEurope PMC
  4. Explainable artificial intelligence in prostate and bladder cancer: A review of imaging, digital pathology, molecular profiling, and clinical data.
    Dimitrios Diamantidis, Georgios Tsakaldimis, Nikolaos Smyrlis +10
    Current urologyPeer-reviewedCC BY-NC-NDFull textGeneral medicineEurope PMCPMC
  5. The Emerging Role of Artificial Intelligence in Cardiovascular Care: From Imaging to Predictive Analytics
    Taha Hatab, Maan Fares
    Heart ViewsPeer-reviewedCC BY-NC-SAFull textEpidemiology & public healthDOAJ

Cite

BibTeXRIS
BibTeX
@article{peng2026explainable,
  title = {{An explainable artificial intelligence framework using electrocardiograms to predict mortality and major adverse cardiovascular events}},
  author = {Jerry Peng and Michael D. Glidden and Sai Rahul Ponnana and Santosh K. Sirasapalli and Tong Zhang and Mark Yoder and Nour Tashtish and Judith A. Mackall and Ravi Ramani and Sadeer Al-Kindi and Yu Yin and Jing Ma and Vipin Chaudhary and Sanjay Rajagopalan},
  journal = {Scientific Reports},
  year = {2026},
  month = oct,
  doi = {10.1038/s41598-026-72179-6},
  url = {https://www.nature.com/articles/s41598-026-72179-6},
  note = {CC BY-NC-ND 4.0},
}
RIS
TY  - JOUR
TI  - An explainable artificial intelligence framework using electrocardiograms to predict mortality and major adverse cardiovascular events
AU  - Jerry Peng
AU  - Michael D. Glidden
AU  - Sai Rahul Ponnana
AU  - Santosh K. Sirasapalli
AU  - Tong Zhang
AU  - Mark Yoder
AU  - Nour Tashtish
AU  - Judith A. Mackall
AU  - Ravi Ramani
AU  - Sadeer Al-Kindi
AU  - Yu Yin
AU  - Jing Ma
AU  - Vipin Chaudhary
AU  - Sanjay Rajagopalan
PY  - 2026
DA  - 2026/10/05
JO  - Scientific Reports
DO  - 10.1038/s41598-026-72179-6
UR  - https://www.nature.com/articles/s41598-026-72179-6
SN  - 2045-2322
ER  -