FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4381661530> ?p ?o ?g. }

• W4381661530 abstract "Patients with heart failure and reduced ejection fraction (HFrEF) are consistently underprescribed guideline-directed medications. Although many barriers to prescribing are known, identification of these barriers has relied on traditional a priori hypotheses or qualitative methods. Machine learning can overcome many limitations of traditional methods to capture complex relationships in data and lead to a more comprehensive understanding of the underpinnings driving underprescribing. Here, we used machine learning methods and routinely available electronic health record data to identify predictors of prescribing.We evaluated the predictive performance of machine learning algorithms to predict prescription of four types of medications for adults with HFrEF: angiotensin converting enzyme inhibitor/angiotensin receptor blocker (ACE/ARB), angiotensin receptor-neprilysin inhibitor (ARNI), evidence-based beta blocker (BB), or mineralocorticoid receptor antagonist (MRA). The models with the best predictive performance were used to identify the top 20 characteristics associated with prescribing each medication type. Shapley values were used to provide insight into the importance and direction of the predictor relationships with medication prescribing.For 3,832 patients meeting the inclusion criteria, 70% were prescribed an ACE/ARB, 8% an ARNI, 75% a BB, and 40% an MRA. The best-predicting model for each medication type was a random forest (area under the curve: 0.788-0.821; Brier score: 0.063-0.185). Across all medications, top predictors of prescribing included prescription of other evidence-based medications and younger age. Unique to prescribing an ARNI, the top predictors included lack of diagnoses of chronic kidney disease, chronic obstructive pulmonary disease, or hypotension, as well as being in a relationship, nontobacco use, and alcohol use.We identified multiple predictors of prescribing for HFrEF medications that are being used to strategically design interventions to address barriers to prescribing and to inform further investigations. The machine learning approach used in this study to identify predictors of suboptimal prescribing can also be used by other health systems to identify and address locally relevant gaps and solutions to prescribing." @default.
• W4381661530 created "2023-06-23" @default.
• W4381661530 creator A5001231969 @default.
• W4381661530 creator A5028982390 @default.
• W4381661530 creator A5033137571 @default.
• W4381661530 creator A5049847607 @default.
• W4381661530 creator A5067710734 @default.
• W4381661530 creator A5068692437 @default.
• W4381661530 creator A5069538684 @default.
• W4381661530 creator A5077029401 @default.
• W4381661530 creator A5085940278 @default.
• W4381661530 creator A5087791202 @default.
• W4381661530 date "2023-06-21" @default.
• W4381661530 modified "2023-10-17" @default.
• W4381661530 title "A machine learning evaluation of patient characteristics associated with prescribing of guideline-directed medical therapy for heart failure" @default.
• W4381661530 cites W167339880 @default.
• W4381661530 cites W1991181258 @default.
• W4381661530 cites W2044136105 @default.
• W4381661530 cites W2054674512 @default.
• W4381661530 cites W2064186732 @default.
• W4381661530 cites W2079518704 @default.
• W4381661530 cites W2091829146 @default.
• W4381661530 cites W2119910794 @default.
• W4381661530 cites W2148143831 @default.
• W4381661530 cites W2161111685 @default.
• W4381661530 cites W2164156792 @default.
• W4381661530 cites W2165369650 @default.
• W4381661530 cites W2315712189 @default.
• W4381661530 cites W2328176404 @default.
• W4381661530 cites W2338061068 @default.
• W4381661530 cites W2397616787 @default.
• W4381661530 cites W2511192823 @default.
• W4381661530 cites W2601058970 @default.
• W4381661530 cites W2626670312 @default.
• W4381661530 cites W2731253005 @default.
• W4381661530 cites W2744293479 @default.
• W4381661530 cites W2786847451 @default.
• W4381661530 cites W2793404518 @default.
• W4381661530 cites W2807076505 @default.
• W4381661530 cites W2883756867 @default.
• W4381661530 cites W2890936859 @default.
• W4381661530 cites W2924338030 @default.
• W4381661530 cites W2979638097 @default.
• W4381661530 cites W2979762289 @default.
• W4381661530 cites W2998790200 @default.
• W4381661530 cites W2999062607 @default.
• W4381661530 cites W3021644017 @default.
• W4381661530 cites W3043310174 @default.
• W4381661530 cites W3048948901 @default.
• W4381661530 cites W3082129774 @default.
• W4381661530 cites W3090402404 @default.
• W4381661530 cites W3103898380 @default.
• W4381661530 cites W3112316946 @default.
• W4381661530 cites W3122966625 @default.
• W4381661530 cites W3127655401 @default.
• W4381661530 cites W3135274079 @default.
• W4381661530 cites W3136075083 @default.
• W4381661530 cites W3147406063 @default.
• W4381661530 cites W3172720427 @default.
• W4381661530 cites W3216077891 @default.
• W4381661530 cites W4367307582 @default.
• W4381661530 doi "https://doi.org/10.3389/fcvm.2023.1169574" @default.
• W4381661530 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37416920" @default.
• W4381661530 hasPublicationYear "2023" @default.
• W4381661530 type Work @default.
• W4381661530 citedByCount "0" @default.
• W4381661530 crossrefType "journal-article" @default.
• W4381661530 hasAuthorship W4381661530A5001231969 @default.
• W4381661530 hasAuthorship W4381661530A5028982390 @default.
• W4381661530 hasAuthorship W4381661530A5033137571 @default.
• W4381661530 hasAuthorship W4381661530A5049847607 @default.
• W4381661530 hasAuthorship W4381661530A5067710734 @default.
• W4381661530 hasAuthorship W4381661530A5068692437 @default.
• W4381661530 hasAuthorship W4381661530A5069538684 @default.
• W4381661530 hasAuthorship W4381661530A5077029401 @default.
• W4381661530 hasAuthorship W4381661530A5085940278 @default.
• W4381661530 hasAuthorship W4381661530A5087791202 @default.
• W4381661530 hasBestOaLocation W43816615301 @default.
• W4381661530 hasConcept C119857082 @default.
• W4381661530 hasConcept C126322002 @default.
• W4381661530 hasConcept C142724271 @default.
• W4381661530 hasConcept C177713679 @default.
• W4381661530 hasConcept C2426938 @default.
• W4381661530 hasConcept C2778198053 @default.
• W4381661530 hasConcept C2778653478 @default.
• W4381661530 hasConcept C2780182762 @default.
• W4381661530 hasConcept C41008148 @default.
• W4381661530 hasConcept C71924100 @default.
• W4381661530 hasConcept C98274493 @default.
• W4381661530 hasConceptScore W4381661530C119857082 @default.
• W4381661530 hasConceptScore W4381661530C126322002 @default.
• W4381661530 hasConceptScore W4381661530C142724271 @default.
• W4381661530 hasConceptScore W4381661530C177713679 @default.
• W4381661530 hasConceptScore W4381661530C2426938 @default.
• W4381661530 hasConceptScore W4381661530C2778198053 @default.
• W4381661530 hasConceptScore W4381661530C2778653478 @default.
• W4381661530 hasConceptScore W4381661530C2780182762 @default.
• W4381661530 hasConceptScore W4381661530C41008148 @default.
• W4381661530 hasConceptScore W4381661530C71924100 @default.
• W4381661530 hasConceptScore W4381661530C98274493 @default.

• next