105 related articles for article (PubMed ID: 38154396)
1. Discovery of distinct cancer cachexia phenotypes using an unsupervised machine-learning algorithm.
Wu HF; Yan JP; Wu Q; Yu Z; Xu HX; Song CH; Guo ZQ; Li W; Xiang YJ; Xu Z; Luo J; Cheng SQ; Zhang FM; Shi HP; Zhuang CL;
Nutrition; 2024 Mar; 119():112317. PubMed ID: 38154396
[TBL] [Abstract][Full Text] [Related]
2. Identifying cancer cachexia in patients without weight loss information: machine learning approaches to address a real-world challenge.
Yin L; Cui J; Lin X; Li N; Fan Y; Zhang L; Liu J; Chong F; Wang C; Liang T; Liu X; Deng L; Yang M; Yu J; Wang X; Cong M; Li Z; Weng M; Yao Q; Jia P; Guo Z; Li W; Song C; Shi H; Xu H
Am J Clin Nutr; 2022 Nov; 116(5):1229-1239. PubMed ID: 36095136
[TBL] [Abstract][Full Text] [Related]
3. A fusion decision system to identify and grade malnutrition in cancer patients: Machine learning reveals feasible workflow from representative real-world data.
Yin L; Song C; Cui J; Lin X; Li N; Fan Y; Zhang L; Liu J; Chong F; Wang C; Liang T; Liu X; Deng L; Li W; Yang M; Yu J; Wang X; Liu X; Yang S; Zuo Z; Yuan K; Yu M; Cong M; Li Z; Jia P; Li S; Guo Z; Shi H; Xu H;
Clin Nutr; 2021 Aug; 40(8):4958-4970. PubMed ID: 34358843
[TBL] [Abstract][Full Text] [Related]
4. Discovery of Distinct Immune Phenotypes Using Machine Learning in Pulmonary Arterial Hypertension.
Sweatt AJ; Hedlin HK; Balasubramanian V; Hsi A; Blum LK; Robinson WH; Haddad F; Hickey PM; Condliffe R; Lawrie A; Nicolls MR; Rabinovitch M; Khatri P; Zamanian RT
Circ Res; 2019 Mar; 124(6):904-919. PubMed ID: 30661465
[TBL] [Abstract][Full Text] [Related]
5. Use of Machine Learning Consensus Clustering to Identify Distinct Subtypes of Black Kidney Transplant Recipients and Associated Outcomes.
Thongprayoon C; Vaitla P; Jadlowiec CC; Leeaphorn N; Mao SA; Mao MA; Pattharanitima P; Bruminhent J; Khoury NJ; Garovic VD; Cooper M; Cheungpasitporn W
JAMA Surg; 2022 Jul; 157(7):e221286. PubMed ID: 35507356
[TBL] [Abstract][Full Text] [Related]
6. Identification of distinct clinical phenotypes of cardiogenic shock using machine learning consensus clustering approach.
Wang L; Zhang Y; Yao R; Chen K; Xu Q; Huang R; Mao Z; Yu Y
BMC Cardiovasc Disord; 2023 Aug; 23(1):426. PubMed ID: 37644414
[TBL] [Abstract][Full Text] [Related]
7. Distinct phenotypes of hospitalized patients with hyperkalemia by machine learning consensus clustering and associated mortality risks.
Thongprayoon C; Kattah AG; Mao MA; Keddis MT; Pattharanitima P; Vallabhajosyula S; Nissaisorakarn V; Erickson SB; Dillon JJ; Garovic VD; Cheungpasitporn W
QJM; 2022 Jul; 115(7):442-449. PubMed ID: 34270780
[TBL] [Abstract][Full Text] [Related]
8. Machine learning-derived blood culture classification with both predictive and prognostic values in the intensive care unit: A retrospective cohort study.
Zhang J; Liu W; Xiao W; Liu Y; Hua T; Yang M
Intensive Crit Care Nurs; 2024 Feb; 80():103549. PubMed ID: 37804818
[TBL] [Abstract][Full Text] [Related]
9. Folic acid supplementation and malaria susceptibility and severity among people taking antifolate antimalarial drugs in endemic areas.
Crider K; Williams J; Qi YP; Gutman J; Yeung L; Mai C; Finkelstain J; Mehta S; Pons-Duran C; Menéndez C; Moraleda C; Rogers L; Daniels K; Green P
Cochrane Database Syst Rev; 2022 Feb; 2(2022):. PubMed ID: 36321557
[TBL] [Abstract][Full Text] [Related]
10. Differences between Very Highly Sensitized Kidney Transplant Recipients as Identified by Machine Learning Consensus Clustering.
Thongprayoon C; Miao J; Jadlowiec CC; Mao SA; Mao MA; Vaitla P; Leeaphorn N; Kaewput W; Pattharanitima P; Tangpanithandee S; Krisanapan P; Nissaisorakarn P; Cooper M; Cheungpasitporn W
Medicina (Kaunas); 2023 May; 59(5):. PubMed ID: 37241209
[No Abstract] [Full Text] [Related]
11. Subtyping Hyperchloremia among Hospitalized Patients by Machine Learning Consensus Clustering.
Thongprayoon C; Nissaisorakarn V; Pattharanitima P; Mao MA; Kattah AG; Keddis MT; Dumancas CY; Vallabhajosyula S; Petnak T; Erickson SB; Dillon JJ; Garovic VD; Kashani KB; Cheungpasitporn W
Medicina (Kaunas); 2021 Aug; 57(9):. PubMed ID: 34577826
[No Abstract] [Full Text] [Related]
12. Clinically Distinct Subtypes of Acute Kidney Injury on Hospital Admission Identified by Machine Learning Consensus Clustering.
Thongprayoon C; Vaitla P; Nissaisorakarn V; Mao MA; Genovez JLZ; Kattah AG; Pattharanitima P; Vallabhajosyula S; Keddis MT; Qureshi F; Dillon JJ; Garovic VD; Kashani KB; Cheungpasitporn W
Med Sci (Basel); 2021 Sep; 9(4):. PubMed ID: 34698185
[TBL] [Abstract][Full Text] [Related]
13. Model-Based and Model-Free Techniques for Amyotrophic Lateral Sclerosis Diagnostic Prediction and Patient Clustering.
Tang M; Gao C; Goutman SA; Kalinin A; Mukherjee B; Guan Y; Dinov ID
Neuroinformatics; 2019 Jul; 17(3):407-421. PubMed ID: 30460455
[TBL] [Abstract][Full Text] [Related]
14. Comparison of machine learning clustering algorithms for detecting heterogeneity of treatment effect in acute respiratory distress syndrome: A secondary analysis of three randomised controlled trials.
Sinha P; Spicer A; Delucchi KL; McAuley DF; Calfee CS; Churpek MM
EBioMedicine; 2021 Dec; 74():103697. PubMed ID: 34861492
[TBL] [Abstract][Full Text] [Related]
15. Machine Learning Methods Improve Prognostication, Identify Clinically Distinct Phenotypes, and Detect Heterogeneity in Response to Therapy in a Large Cohort of Heart Failure Patients.
Ahmad T; Lund LH; Rao P; Ghosh R; Warier P; Vaccaro B; Dahlström U; O'Connor CM; Felker GM; Desai NR
J Am Heart Assoc; 2018 Apr; 7(8):. PubMed ID: 29650709
[TBL] [Abstract][Full Text] [Related]
16. Discovering Pediatric Asthma Phenotypes on the Basis of Response to Controller Medication Using Machine Learning.
Ross MK; Yoon J; van der Schaar A; van der Schaar M
Ann Am Thorac Soc; 2018 Jan; 15(1):49-58. PubMed ID: 29048949
[TBL] [Abstract][Full Text] [Related]
17. Hand grip strength-based cachexia index as a predictor of cancer cachexia and prognosis in patients with cancer.
Xie H; Ruan G; Wei L; Zhang H; Ge Y; Zhang Q; Lin S; Song M; Zhang X; Liu X; Li X; Zhang K; Yang M; Tang M; Song CH; Gan J; Shi HP
J Cachexia Sarcopenia Muscle; 2023 Feb; 14(1):382-390. PubMed ID: 36447437
[TBL] [Abstract][Full Text] [Related]
18. Transcriptomic signature of cancer cachexia by integration of machine learning, literature mining and meta-analysis.
Zhao K; Ebrahimie E; Mohammadi-Dehcheshmeh M; Lewsey MG; Zheng L; Hoogenraad NJ
Comput Biol Med; 2024 Apr; 172():108233. PubMed ID: 38452471
[TBL] [Abstract][Full Text] [Related]
19. Machine Learning Consensus Clustering Approach for Hospitalized Patients with Dysmagnesemia.
Thongprayoon C; Sy-Go JPT; Nissaisorakarn V; Dumancas CY; Keddis MT; Kattah AG; Pattharanitima P; Vallabhajosyula S; Mao MA; Qureshi F; Garovic VD; Dillon JJ; Erickson SB; Cheungpasitporn W
Diagnostics (Basel); 2021 Nov; 11(11):. PubMed ID: 34829467
[TBL] [Abstract][Full Text] [Related]
20. Validation of the Consensus-Definition for Cancer Cachexia and evaluation of a classification model--a study based on data from an international multicentre project (EPCRC-CSA).
Blum D; Stene GB; Solheim TS; Fayers P; Hjermstad MJ; Baracos VE; Fearon K; Strasser F; Kaasa S;
Ann Oncol; 2014 Aug; 25(8):1635-42. PubMed ID: 24562443
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]