227 related articles for article (PubMed ID: 32885937)
1. An Explainable Artificial Intelligence Predictor for Early Detection of Sepsis.
Yang M; Liu C; Wang X; Li Y; Gao H; Liu X; Li J
Crit Care Med; 2020 Nov; 48(11):e1091-e1096. PubMed ID: 32885937
[TBL] [Abstract][Full Text] [Related]
2. Early Sepsis Prediction Using Ensemble Learning With Deep Features and Artificial Features Extracted From Clinical Electronic Health Records.
He Z; Du L; Zhang P; Zhao R; Chen X; Fang Z
Crit Care Med; 2020 Dec; 48(12):e1337-e1342. PubMed ID: 33044286
[TBL] [Abstract][Full Text] [Related]
3. An Interpretable Machine Learning Model for Accurate Prediction of Sepsis in the ICU.
Nemati S; Holder A; Razmi F; Stanley MD; Clifford GD; Buchman TG
Crit Care Med; 2018 Apr; 46(4):547-553. PubMed ID: 29286945
[TBL] [Abstract][Full Text] [Related]
4. Early Prediction of Sepsis From Clinical Data: The PhysioNet/Computing in Cardiology Challenge 2019.
Reyna MA; Josef CS; Jeter R; Shashikumar SP; Westover MB; Nemati S; Clifford GD; Sharma A
Crit Care Med; 2020 Feb; 48(2):210-217. PubMed ID: 31939789
[TBL] [Abstract][Full Text] [Related]
5. A Time-Phased Machine Learning Model for Real-Time Prediction of Sepsis in Critical Care.
Li X; Xu X; Xie F; Xu X; Sun Y; Liu X; Jia X; Kang Y; Xie L; Wang F; Xie G
Crit Care Med; 2020 Oct; 48(10):e884-e888. PubMed ID: 32931194
[TBL] [Abstract][Full Text] [Related]
6. Early Prediction of Sepsis From Clinical Data Using Ratio and Power-Based Features.
Nesaragi N; Patidar S
Crit Care Med; 2020 Dec; 48(12):e1343-e1349. PubMed ID: 33048903
[TBL] [Abstract][Full Text] [Related]
7. Graph Convolutional Networks-Based Noisy Data Imputation in Electronic Health Record.
Lee BT; Kwon OY; Park H; Cho KJ; Kwon JM; Lee Y
Crit Care Med; 2020 Nov; 48(11):e1106-e1111. PubMed ID: 32947466
[TBL] [Abstract][Full Text] [Related]
8. The development an artificial intelligence algorithm for early sepsis diagnosis in the intensive care unit.
Yuan KC; Tsai LW; Lee KH; Cheng YW; Hsu SC; Lo YS; Chen RJ
Int J Med Inform; 2020 Sep; 141():104176. PubMed ID: 32485555
[TBL] [Abstract][Full Text] [Related]
9. Utilization of the Signature Method to Identify the Early Onset of Sepsis From Multivariate Physiological Time Series in Critical Care Monitoring.
Morrill JH; Kormilitzin A; Nevado-Holgado AJ; Swaminathan S; Howison SD; Lyons TJ
Crit Care Med; 2020 Oct; 48(10):e976-e981. PubMed ID: 32897664
[TBL] [Abstract][Full Text] [Related]
10. Development and Validation of an Interpretable Conformal Predictor to Predict Sepsis Mortality Risk: Retrospective Cohort Study.
Yang M; Chen H; Hu W; Mischi M; Shan C; Li J; Long X; Liu C
J Med Internet Res; 2024 Mar; 26():e50369. PubMed ID: 38498038
[TBL] [Abstract][Full Text] [Related]
11. Multicentre validation of a sepsis prediction algorithm using only vital sign data in the emergency department, general ward and ICU.
Mao Q; Jay M; Hoffman JL; Calvert J; Barton C; Shimabukuro D; Shieh L; Chettipally U; Fletcher G; Kerem Y; Zhou Y; Das R
BMJ Open; 2018 Jan; 8(1):e017833. PubMed ID: 29374661
[TBL] [Abstract][Full Text] [Related]
12. Explainable artificial intelligence model to predict acute critical illness from electronic health records.
Lauritsen SM; Kristensen M; Olsen MV; Larsen MS; Lauritsen KM; Jørgensen MJ; Lange J; Thiesson B
Nat Commun; 2020 Jul; 11(1):3852. PubMed ID: 32737308
[TBL] [Abstract][Full Text] [Related]
13. Two-Step Imputation and AdaBoost-Based Classification for Early Prediction of Sepsis on Imbalanced Clinical Data.
Baniasadi A; Rezaeirad S; Zare H; Ghassemi MM
Crit Care Med; 2021 Jan; 49(1):e91-e97. PubMed ID: 33156121
[TBL] [Abstract][Full Text] [Related]
14. Artificial intelligence in sepsis early prediction and diagnosis using unstructured data in healthcare.
Goh KH; Wang L; Yeow AYK; Poh H; Li K; Yeow JJL; Tan GYH
Nat Commun; 2021 Jan; 12(1):711. PubMed ID: 33514699
[TBL] [Abstract][Full Text] [Related]
15. Using machine learning methods to predict in-hospital mortality of sepsis patients in the ICU.
Kong G; Lin K; Hu Y
BMC Med Inform Decis Mak; 2020 Oct; 20(1):251. PubMed ID: 33008381
[TBL] [Abstract][Full Text] [Related]
16. Tensor learning of pointwise mutual information from EHR data for early prediction of sepsis.
Nesaragi N; Patidar S; Aggarwal V
Comput Biol Med; 2021 Jul; 134():104430. PubMed ID: 33991856
[TBL] [Abstract][Full Text] [Related]
17. A Machine Learning Model for Accurate Prediction of Sepsis in ICU Patients.
Wang D; Li J; Sun Y; Ding X; Zhang X; Liu S; Han B; Wang H; Duan X; Sun T
Front Public Health; 2021; 9():754348. PubMed ID: 34722452
[No Abstract] [Full Text] [Related]
18. Evaluation of a machine learning algorithm for up to 48-hour advance prediction of sepsis using six vital signs.
Barton C; Chettipally U; Zhou Y; Jiang Z; Lynn-Palevsky A; Le S; Calvert J; Das R
Comput Biol Med; 2019 Jun; 109():79-84. PubMed ID: 31035074
[TBL] [Abstract][Full Text] [Related]
19. Customization of a Severity of Illness Score Using Local Electronic Medical Record Data.
Lee J; Maslove DM
J Intensive Care Med; 2017 Jan; 32(1):38-47. PubMed ID: 25969432
[TBL] [Abstract][Full Text] [Related]
20. Applying Artificial Intelligence to Identify Physiomarkers Predicting Severe Sepsis in the PICU.
Kamaleswaran R; Akbilgic O; Hallman MA; West AN; Davis RL; Shah SH
Pediatr Crit Care Med; 2018 Oct; 19(10):e495-e503. PubMed ID: 30052552
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
