239 related articles for article (PubMed ID: 34951595)
1. Health Care Analytics With Time-Invariant and Time-Variant Feature Importance to Predict Hospital-Acquired Acute Kidney Injury: Observational Longitudinal Study.
Chua HR; Zheng K; Vathsala A; Ngiam KY; Yap HK; Lu L; Tiong HY; Mukhopadhyay A; MacLaren G; Lim SL; Akalya K; Ooi BC
J Med Internet Res; 2021 Dec; 23(12):e30805. PubMed ID: 34951595
[TBL] [Abstract][Full Text] [Related]
2. Machine Learning Model for Risk Prediction of Community-Acquired Acute Kidney Injury Hospitalization From Electronic Health Records: Development and Validation Study.
Hsu CN; Liu CL; Tain YL; Kuo CY; Lin YC
J Med Internet Res; 2020 Aug; 22(8):e16903. PubMed ID: 32749223
[TBL] [Abstract][Full Text] [Related]
3. Electronic health records accurately predict renal replacement therapy in acute kidney injury.
Low S; Vathsala A; Murali TM; Pang L; MacLaren G; Ng WY; Haroon S; Mukhopadhyay A; Lim SL; Tan BH; Lau T; Chua HR
BMC Nephrol; 2019 Jan; 20(1):32. PubMed ID: 30704418
[TBL] [Abstract][Full Text] [Related]
4. Machine learning model for early prediction of acute kidney injury (AKI) in pediatric critical care.
Dong J; Feng T; Thapa-Chhetry B; Cho BG; Shum T; Inwald DP; Newth CJL; Vaidya VU
Crit Care; 2021 Aug; 25(1):288. PubMed ID: 34376222
[TBL] [Abstract][Full Text] [Related]
5. Internal and External Validation of a Machine Learning Risk Score for Acute Kidney Injury.
Churpek MM; Carey KA; Edelson DP; Singh T; Astor BC; Gilbert ER; Winslow C; Shah N; Afshar M; Koyner JL
JAMA Netw Open; 2020 Aug; 3(8):e2012892. PubMed ID: 32780123
[TBL] [Abstract][Full Text] [Related]
6. A Machine Learning Algorithm Predicting Acute Kidney Injury in Intensive Care Unit Patients (NAVOY Acute Kidney Injury): Proof-of-Concept Study.
Persson I; Grünwald A; Morvan L; Becedas D; Arlbrandt M
JMIR Form Res; 2023 Dec; 7():e45979. PubMed ID: 38096015
[TBL] [Abstract][Full Text] [Related]
7. Real-Time Prediction of Acute Kidney Injury in Hospitalized Adults: Implementation and Proof of Concept.
Ugwuowo U; Yamamoto Y; Arora T; Saran I; Partridge C; Biswas A; Martin M; Moledina DG; Greenberg JH; Simonov M; Mansour SG; Vela R; Testani JM; Rao V; Rentfro K; Obeid W; Parikh CR; Wilson FP
Am J Kidney Dis; 2020 Dec; 76(6):806-814.e1. PubMed ID: 32505812
[TBL] [Abstract][Full Text] [Related]
8. Real-Time Clinical Decision Support Based on Recurrent Neural Networks for In-Hospital Acute Kidney Injury: External Validation and Model Interpretation.
Kim K; Yang H; Yi J; Son HE; Ryu JY; Kim YC; Jeong JC; Chin HJ; Na KY; Chae DW; Han SS; Kim S
J Med Internet Res; 2021 Apr; 23(4):e24120. PubMed ID: 33861200
[TBL] [Abstract][Full Text] [Related]
9. Prediction of Acute Kidney Injury With a Machine Learning Algorithm Using Electronic Health Record Data.
Mohamadlou H; Lynn-Palevsky A; Barton C; Chettipally U; Shieh L; Calvert J; Saber NR; Das R
Can J Kidney Health Dis; 2018; 5():2054358118776326. PubMed ID: 30094049
[TBL] [Abstract][Full Text] [Related]
10. [Comparison of machine learning method and logistic regression model in prediction of acute kidney injury in severely burned patients].
Tang CQ; Li JQ; Xu DY; Liu XB; Hou WJ; Lyu KY; Xiao SC; Xia ZF
Zhonghua Shao Shang Za Zhi; 2018 Jun; 34(6):343-348. PubMed ID: 29961290
[No Abstract] [Full Text] [Related]
11. A novel multivariable time series prediction model for acute kidney injury in general hospitalization.
Xu J; Hu Y; Liu H; Mi W; Li G; Guo J; Feng Y
Int J Med Inform; 2022 May; 161():104729. PubMed ID: 35279551
[TBL] [Abstract][Full Text] [Related]
12. A continual prediction model for inpatient acute kidney injury.
Kate RJ; Pearce N; Mazumdar D; Nilakantan V
Comput Biol Med; 2020 Jan; 116():103580. PubMed ID: 32001013
[TBL] [Abstract][Full Text] [Related]
13. A clinical, proteomics, and artificial intelligence-driven model to predict acute kidney injury in patients undergoing coronary angiography.
Ibrahim NE; McCarthy CP; Shrestha S; Gaggin HK; Mukai R; Magaret CA; Rhyne RF; Januzzi JL
Clin Cardiol; 2019 Feb; 42(2):292-298. PubMed ID: 30582197
[TBL] [Abstract][Full Text] [Related]
14. Acute kidney injury identification for pharmacoepidemiologic studies: Use of laboratory electronic acute kidney injury alerts versus electronic health records in Hospital Episode Statistics.
Savino M; Plumb L; Casula A; Evans K; Wong E; Kolhe N; Medcalf JF; Nitsch D
Pharmacoepidemiol Drug Saf; 2021 Dec; 30(12):1687-1695. PubMed ID: 34418198
[TBL] [Abstract][Full Text] [Related]
15. Developing a supervised machine learning model for predicting perioperative acute kidney injury in arthroplasty patients.
Nikkinen O; Kolehmainen T; Aaltonen T; Jämsä E; Alahuhta S; Vakkala M
Comput Biol Med; 2022 May; 144():105351. PubMed ID: 35286890
[TBL] [Abstract][Full Text] [Related]
16. Predicting AKI in emergency admissions: an external validation study of the acute kidney injury prediction score (APS).
Hodgson LE; Dimitrov BD; Roderick PJ; Venn R; Forni LG
BMJ Open; 2017 Mar; 7(3):e013511. PubMed ID: 28274964
[TBL] [Abstract][Full Text] [Related]
17. Machine learning clinical prediction models for acute kidney injury: the impact of baseline creatinine on prediction efficacy.
Kamel Rahimi A; Ghadimi M; van der Vegt AH; Canfell OJ; Pole JD; Sullivan C; Shrapnel S
BMC Med Inform Decis Mak; 2023 Oct; 23(1):207. PubMed ID: 37814311
[TBL] [Abstract][Full Text] [Related]
18. A Time-Updated, Parsimonious Model to Predict AKI in Hospitalized Children.
Sandokji I; Yamamoto Y; Biswas A; Arora T; Ugwuowo U; Simonov M; Saran I; Martin M; Testani JM; Mansour S; Moledina DG; Greenberg JH; Wilson FP
J Am Soc Nephrol; 2020 Jun; 31(6):1348-1357. PubMed ID: 32381598
[TBL] [Abstract][Full Text] [Related]
19. Development and Validation of a Personalized Model With Transfer Learning for Acute Kidney Injury Risk Estimation Using Electronic Health Records.
Liu K; Zhang X; Chen W; Yu ASL; Kellum JA; Matheny ME; Simpson SQ; Hu Y; Liu M
JAMA Netw Open; 2022 Jul; 5(7):e2219776. PubMed ID: 35796212
[TBL] [Abstract][Full Text] [Related]
20. Machine learning for the prediction of acute kidney injury in critical care patients with acute cerebrovascular disease.
Zhang X; Chen S; Lai K; Chen Z; Wan J; Xu Y
Ren Fail; 2022 Dec; 44(1):43-53. PubMed ID: 35166177
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
