166 related articles for article (PubMed ID: 38616284)
1. Development and validation of machine learning-based models for predicting healthcare-associated bacterial/fungal infections among COVID-19 inpatients: a retrospective cohort study.
Wang M; Li W; Wang H; Song P
Antimicrob Resist Infect Control; 2024 Apr; 13(1):42. PubMed ID: 38616284
[TBL] [Abstract][Full Text] [Related]
2. Risk Factors and Predictive Models for Peripherally Inserted Central Catheter Unplanned Extubation in Patients With Cancer: Prospective, Machine Learning Study.
Zhang J; Ma G; Peng S; Hou J; Xu R; Luo L; Hu J; Yao N; Wang J; Huang X
J Med Internet Res; 2023 Nov; 25():e49016. PubMed ID: 37971792
[TBL] [Abstract][Full Text] [Related]
3. Host transcriptomics and machine learning for secondary bacterial infections in patients with COVID-19: a prospective, observational cohort study.
Carney M; Pelaia TM; Chew T; Teoh S; Phu A; Kim K; Wang Y; Iredell J; Zerbib Y; McLean A; Schughart K; Tang B; Shojaei M; Short KR;
Lancet Microbe; 2024 Mar; 5(3):e272-e281. PubMed ID: 38310908
[TBL] [Abstract][Full Text] [Related]
4. Antibiotic Use and Bacterial Infection among Inpatients in the First Wave of COVID-19: a Retrospective Cohort Study of 64,691 Patients.
Baghdadi JD; Coffey KC; Adediran T; Goodman KE; Pineles L; Magder LS; O'Hara LM; Pineles BL; Nadimpalli G; Morgan DJ; Harris AD
Antimicrob Agents Chemother; 2021 Oct; 65(11):e0134121. PubMed ID: 34491806
[TBL] [Abstract][Full Text] [Related]
5. Bacterial and Fungal Coinfection in Individuals With Coronavirus: A Rapid Review To Support COVID-19 Antimicrobial Prescribing.
Rawson TM; Moore LSP; Zhu N; Ranganathan N; Skolimowska K; Gilchrist M; Satta G; Cooke G; Holmes A
Clin Infect Dis; 2020 Dec; 71(9):2459-2468. PubMed ID: 32358954
[TBL] [Abstract][Full Text] [Related]
6. Can Predictive Modeling Tools Identify Patients at High Risk of Prolonged Opioid Use After ACL Reconstruction?
Anderson AB; Grazal CF; Balazs GC; Potter BK; Dickens JF; Forsberg JA
Clin Orthop Relat Res; 2020 Jul; 478(7):0-1618. PubMed ID: 32282466
[TBL] [Abstract][Full Text] [Related]
7. National surveillance of bacterial and fungal coinfection and secondary infection in COVID-19 patients in England: lessons from the first wave.
Gerver SM; Guy R; Wilson K; Thelwall S; Nsonwu O; Rooney G; Brown CS; Muller-Pebody B; Hope R; Hall V
Clin Microbiol Infect; 2021 Nov; 27(11):1658-1665. PubMed ID: 34481722
[TBL] [Abstract][Full Text] [Related]
8. Clinical Timing-Sequence Warning Models for Serious Bacterial Infections in Adults Based on Machine Learning: Retrospective Study.
Liu J; Chen J; Dong Y; Lou Y; Tian Y; Sun H; Jin Y; Li J; Qiu Y
J Med Internet Res; 2023 Dec; 25():e45515. PubMed ID: 38109177
[TBL] [Abstract][Full Text] [Related]
9. Construction and validation of a machine learning-based nomogram: A tool to predict the risk of getting severe coronavirus disease 2019 (COVID-19).
Yao Z; Zheng X; Zheng Z; Wu K; Zheng J
Immun Inflamm Dis; 2021 Jun; 9(2):595-607. PubMed ID: 33713584
[TBL] [Abstract][Full Text] [Related]
10. Machine-learning Models Predict 30-Day Mortality, Cardiovascular Complications, and Respiratory Complications After Aseptic Revision Total Joint Arthroplasty.
Abraham VM; Booth G; Geiger P; Balazs GC; Goldman A
Clin Orthop Relat Res; 2022 Nov; 480(11):2137-2145. PubMed ID: 35767804
[TBL] [Abstract][Full Text] [Related]
11. Fungal and bacterial coinfections increase mortality of severely ill COVID-19 patients.
Silva DL; Lima CM; Magalhães VCR; Baltazar LM; Peres NTA; Caligiorne RB; Moura AS; Fereguetti T; Martins JC; Rabelo LF; Abrahão JS; Lyon AC; Johann S; Santos DA
J Hosp Infect; 2021 Jul; 113():145-154. PubMed ID: 33852950
[TBL] [Abstract][Full Text] [Related]
12. Predicting mortality in SARS-COV-2 (COVID-19) positive patients in the inpatient setting using a novel deep neural network.
Naseem M; Arshad H; Hashmi SA; Irfan F; Ahmed FS
Int J Med Inform; 2021 Oct; 154():104556. PubMed ID: 34455118
[TBL] [Abstract][Full Text] [Related]
13. Bacterial and fungal coinfection among hospitalized patients with COVID-19: a retrospective cohort study in a UK secondary-care setting.
Hughes S; Troise O; Donaldson H; Mughal N; Moore LSP
Clin Microbiol Infect; 2020 Oct; 26(10):1395-1399. PubMed ID: 32603803
[TBL] [Abstract][Full Text] [Related]
14. Predicting conversion of ambulatory ACDF patients to inpatient: a machine learning approach.
Schönnagel L; Tani S; Vu-Han TL; Zhu J; Camino-Willhuber G; Dodo Y; Caffard T; Chiapparelli E; Oezel L; Shue J; Zelenty WD; Lebl DR; Cammisa FP; Girardi FP; Sokunbi G; Hughes AP; Sama AA
Spine J; 2024 Apr; 24(4):563-571. PubMed ID: 37980960
[TBL] [Abstract][Full Text] [Related]
15. Coinfections in Patients With Cancer and COVID-19: A COVID-19 and Cancer Consortium (CCC19) Study.
Satyanarayana G; Enriquez KT; Sun T; Klein EJ; Abidi M; Advani SM; Awosika J; Bakouny Z; Bashir B; Berg S; Bernardes M; Egan PC; Elkrief A; Feldman LE; Friese CR; Goel S; Gomez CG; Grant KL; Griffiths EA; Gulati S; Gupta S; Hwang C; Jain J; Jani C; Kaltsas A; Kasi A; Khan H; Knox N; Koshkin VS; Kwon DH; Labaki C; Lyman GH; McKay RR; McNair C; Nagaraj G; Nakasone ES; Nguyen R; Nonato TK; Olszewski AJ; Panagiotou OA; Puc M; Razavi P; Robilotti EV; Santos-Dutra M; Schmidt AL; Shah DP; Shah SA; Vieira K; Weissmann LB; Wise-Draper TM; Wu U; Wu JT; Choueiri TK; Mishra S; Warner JL; French B; Farmakiotis D
Open Forum Infect Dis; 2022 Mar; 9(3):ofac037. PubMed ID: 35198648
[TBL] [Abstract][Full Text] [Related]
16. Application of machine learning model in predicting the likelihood of blood transfusion after hip fracture surgery.
Chen X; Pan J; Li Y; Tang R
Aging Clin Exp Res; 2023 Nov; 35(11):2643-2656. PubMed ID: 37733228
[TBL] [Abstract][Full Text] [Related]
17. Prediction of hospital-onset COVID-19 infections using dynamic networks of patient contact: an international retrospective cohort study.
Myall A; Price JR; Peach RL; Abbas M; Mookerjee S; Zhu N; Ahmad I; Ming D; Ramzan F; Teixeira D; Graf C; Weiße AY; Harbarth S; Holmes A; Barahona M
Lancet Digit Health; 2022 Aug; 4(8):e573-e583. PubMed ID: 35868812
[TBL] [Abstract][Full Text] [Related]
18. Procalcitonin and C-reactive protein to rule out early bacterial coinfection in COVID-19 critically ill patients.
Galli F; Bindo F; Motos A; Fernández-Barat L; Barbeta E; Gabarrús A; Ceccato A; Bermejo-Martin JF; Ferrer R; Riera J; Peñuelas O; Lorente JÁ; de Gonzalo-Calvo D; Menéndez R; Gonzalez J; Misuraca S; Palomeque A; Amaya-Villar R; Añón JM; Balan Mariño A; Barberà C; Barberán J; Blandino Ortiz A; Bustamante-Munguira E; Caballero J; Cantón-Bulnes ML; Carbajales Pérez C; Carbonell N; Catalán-González M; de Frutos R; Franco N; Galbán C; Lopez Lago A; Gumucio-Sanguino VD; de la Torre MDC; Díaz E; Estella Á; Gallego Curto E; García-Garmendia JL; Gómez JM; Huerta A; Jorge García RN; Loza-Vázquez A; Marin-Corral J; Martin Delgado MC; Martínez de la Gándara A; Martínez Varela I; Lopez Messa J; M Albaiceta G; Nieto MT; Novo MA; Peñasco Y; Pérez-García F; Pozo-Laderas JC; Ricart P; Sagredo V; Sánchez-Miralles A; Sancho Chinesta S; Roche-Campo F; Socias L; Solé-Violan J; Suarez-Sipmann F; Tamayo Lomas L; Trenado J; Úbeda A; Valdivia LJ; Vidal P; Boado MV; Rodríguez A; Antonelli M; Blasi F; Barbé F; Torres A;
Intensive Care Med; 2023 Aug; 49(8):934-945. PubMed ID: 37507573
[TBL] [Abstract][Full Text] [Related]
19. The Development and Validation of Simplified Machine Learning Algorithms to Predict Prognosis of Hospitalized Patients With COVID-19: Multicenter, Retrospective Study.
He F; Page JH; Weinberg KR; Mishra A
J Med Internet Res; 2022 Jan; 24(1):e31549. PubMed ID: 34951865
[TBL] [Abstract][Full Text] [Related]
20. Machine learning-based prediction models for home discharge in patients with COVID-19: Development and evaluation using electronic health records.
Zapata RD; Huang S; Morris E; Wang C; Harle C; Magoc T; Mardini M; Loftus T; Modave F
PLoS One; 2023; 18(10):e0292888. PubMed ID: 37862334
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
