Posts Tagged ‘Machine Learning’
Higher Symptom Frequency and Severity After the Long Interdialytic Interval in Patients on Maintenance Intermittent Hemodialysis.
Link: https://doi.org/10.1016/j.ekir.2022.09.032 Authors: Chauhan, Kinsuk; Wen, Huei Hsun; Gupta, Neepa; Nadkarni, Girish; Coca, Steven; Chan, Lili Abstract: Patients on intermittent hemodialysis (HD) have a high symptom burden. Though studies report higher hospitalizations and mortality after the long interdialytic interval, whether symptoms vary based on the interdialytic interval is unclear. This is a prospective observational study…
Read MoreRelationship Between Preexisting Cardiovascular Disease and Death and Cardiovascular Outcomes in Critically Ill Patients With COVID-19.
Link: https://doi.org/10.1161/CIRCOUTCOMES.122.008942 Authors: Vasbinder, Alexi; Meloche, Chelsea; Azam, Tariq U; Anderson, Elizabeth; Catalan, Tonimarie; Shadid, Husam; Berlin, Hanna; Pan, Michael; O’Hayer, Patrick; Padalia, Kishan; Blakely, Pennelope; Khaleel, Ibrahim; Michaud, Erinleigh; Huang, Yiyuan; Zhao, Lili; Pop-Busui, Rodica; Gupta, Shruti; Eagle, Kim; Leaf, David E; Hayek, Salim S; , Abstract: Preexisting cardiovascular disease (CVD) is perceived as…
Read MoreThe dynamic changes and sex differences of 147 immune-related proteins during acute COVID-19 in 580 individuals.
Link: https://doi.org/10.1186/s12014-022-09371-z Authors: Butler-Laporte, Guillaume; Gonzalez-Kozlova, Edgar; Su, Chen-Yang; Zhou, Sirui; Nakanishi, Tomoko; Brunet-Ratnasingham, Elsa; Morrison, David; Laurent, Laetitia; Afilalo, Jonathan; Afilalo, Marc; Henry, Danielle; Chen, Yiheng; Carrasco-Zanini, Julia; Farjoun, Yossi; Pietzner, Maik; Kimchi, Nofar; Afrasiabi, Zaman; Rezk, Nardin; Bouab, Meriem; Petitjean, Louis; Guzman, Charlotte; Xue, Xiaoqing; Tselios, Chris; Vulesevic, Branka; Adeleye, Olumide; Abdullah, Tala;…
Read MorePredictors of outpatient follow-up care after adult emergency department asthma visits and association with 30-day outcomes.
Link: https://doi.org/10.1080/02770903.2022.2109166 Authors: Abbott, Ethan E; Vargas-Torres, Carmen; Karwoska Kligler, Sophie; Spadafore, Sophia; Lin, Michelle P Abstract:
Read MoreVisual Analytics to Leverage Anesthesia Electronic Health Record.
Link: https://doi.org/10.1213/ANE.0000000000006175 Authors: Kahn, Ronald A; Gal, Jonathan S; Hofer, Ira S; Wax, David B; Villar, Joshua I; Levin, Mathew A Abstract: Visual analytics is the science of analytical reasoning supported by interactive visual interfaces called dashboards. In this report, we describe our experience addressing the challenges in visual analytics of anesthesia electronic health record…
Read MoreHuman WDR5 promotes breast cancer growth and metastasis via KMT2-independent translation regulation.
Link: https://doi.org/10.7554/eLife.78163 Authors: Cai, Wesley L; Chen, Jocelyn Fang-Yi; Chen, Huacui; Wingrove, Emily; Kurley, Sarah J; Chan, Lok Hei; Zhang, Meiling; Arnal-Estape, Anna; Zhao, Minghui; Balabaki, Amer; Li, Wenxue; Yu, Xufen; Krop, Ethan D; Dou, Yali; Liu, Yansheng; Jin, Jian; Westbrook, Thomas F; Nguyen, Don X; Yan, Qin Abstract: Metastatic breast cancer remains a major…
Read MoreMachine learning for risk stratification in kidney disease.
Link: https://doi.org/10.1097/MNH.0000000000000832 Authors: Gulamali, Faris F; Sawant, Ashwin S; Nadkarni, Girish N Abstract: Risk stratification for chronic kidney is becoming increasingly important as a clinical tool for both treatment and prevention measures. The goal of this review is to identify how machine learning tools contribute and facilitate risk stratification in the clinical setting. The two…
Read MoreStudyU: A Platform for Designing and Conducting Innovative Digital N-of-1 Trials.
Link: https://doi.org/10.2196/35884 Authors: Konigorski, Stefan; Wernicke, Sarah; Slosarek, Tamara; Zenner, Alexander M; Strelow, Nils; Ruether, Darius F; Henschel, Florian; Manaswini, Manisha; Pottbäcker, Fabian; Edelman, Jonathan A; Owoyele, Babajide; Danieletto, Matteo; Golden, Eddye; Zweig, Micol; Nadkarni, Girish N; Böttinger, Erwin Abstract: N-of-1 trials are the gold standard study design to evaluate individual treatment effects and derive…
Read MoreClinical predictors of response to methotrexate in patients with rheumatoid arthritis: a machine learning approach using clinical trial data.
Link: https://doi.org/10.1186/s13075-022-02851-5 Authors: Duong, Stephanie Q; Crowson, Cynthia S; Athreya, Arjun; Atkinson, Elizabeth J; Davis, John M; Warrington, Kenneth J; Matteson, Eric L; Weinshilboum, Richard; Wang, Liewei; Myasoedova, Elena Abstract: Methotrexate is the preferred initial disease-modifying antirheumatic drug (DMARD) for rheumatoid arthritis (RA). However, clinically useful tools for individualized prediction of response to methotrexate treatment…
Read MoreIntegration of feature vectors from raw laboratory, medication and procedure names improves the precision and recall of models to predict postoperative mortality and acute kidney injury.
Link: https://doi.org/10.1038/s41598-022-13879-7 Authors: Hofer, Ira S; Kupina, Marina; Laddaran, Lori; Halperin, Eran Abstract: Manuscripts that have successfully used machine learning (ML) to predict a variety of perioperative outcomes often use only a limited number of features selected by a clinician. We hypothesized that techniques leveraging a broad set of features for patient laboratory results, medications,…
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