Impact of a Pharmacist-Led Intervention on 30-Day Readmission and Assessment of Factors Predictive of Readmission in African American Men With Heart Failure

McKinley, DeAngelo; Moye-Dickerson, Pamela M.; Davis, Shondria; Akil, Ayman

doi:10.1177/1557988318814295

Cited by 17 publications

(17 citation statements)

References 33 publications

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“…The second most popular algorithm was NN (14, 33%): many studies used multiple hidden layers based deep learning techniques [ 60 , 69 – 71 , 77 , 79 , 80 , 85 – 87 ] (e.g., recurrent NN, convolutional NN, deep NN, and ensemble of DL networks), while a few other studies either used one hidden layer [ 58 , 60 , 68 ] or did not specify the number of layers [ 49 , 66 ]. Regularized logistic regression (12, 28%), including Least Absolute Shrinkage and Selection Operator (LASSO) regression [ 53 , 64 , 65 , 67 , 70 , 71 , 78 – 80 ] (L1 regularization), ridge regression [ 64 , 70 , 71 , 80 ] (L2 regularization) and elastic-net [ 49 , 72 , 81 ]were third most used ML algorithm, followed by Support Vector Machine (SVM) [ 54 , 60 , 63 , 65 , 66 , 70 , 71 , 82 – 84 ] (10, 23%). The other less commonly used ML algorithms included naïve Bayes network [ 49 , 54 , 70 , 84 ], K-Nearest Neighbors (KNN) algorithm [ 54 , 65 ], ensemble of methods [ 50 , 67 , 84 ], ,and Bayesian Model averaging [ 49 ].…”

Section: Resultsmentioning

confidence: 99%

“…( N = 43 studies) Type of ML Algorithms Number of Studies f (Percent) Featuring Studies Tree-based methods 23 (53%) Decision Tree 9 [ 46 , 52 , 54 – 60 ] Random Forest 16 [ 48 – 50 , 59 – 71 ] Boosted tree methods a 18 [ 47 , 49 – 51 , 53 , 54 , 59 , 64 – 67 , 71 – 77 ] Regularized Logistic Regression (penalized method) 12 (28%) Lasso (L1 regularization) 9 [ 53 , 64 , 65 , 67 , 70 , 71 , 78 – 80 ] Ridge Regression (L2 regularization) 4 [ 64 , 70 , 71 , 80 ] Elastic-Net 3 [ 49 , 72 , 81 ] Support Vector Machine 10 (23%) [ 54 , 60 , …”

Section: Resultsmentioning

confidence: 99%

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Application of machine learning in predicting hospital readmissions: a scoping review of the literature

Huang

Talwar

Chatterjee

et al. 2021

BMC Med Res Methodol

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Background Advances in machine learning (ML) provide great opportunities in the prediction of hospital readmission. This review synthesizes the literature on ML methods and their performance for predicting hospital readmission in the US. Methods This review was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Extension for Scoping Reviews (PRISMA-ScR) Statement. The extraction of items was also guided by the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS). Electronic databases PUBMED, MEDLINE, and EMBASE were systematically searched from January 1, 2015, through December 10, 2019. The articles were imported into COVIDENCE online software for title/abstract screening and full-text eligibility. Observational studies using ML techniques for hospital readmissions among US patients were eligible for inclusion. Articles without a full text available in the English language were excluded. A qualitative synthesis included study characteristics, ML algorithms utilized, and model validation, and quantitative analysis assessed model performance. Model performances in terms of Area Under the Curve (AUC) were analyzed using R software. Quality in Prognosis Studies (QUIPS) tool was used to assess the quality of the reviewed studies. Results Of 522 citations reviewed, 43 studies met the inclusion criteria. A majority of the studies used electronic health records (24, 56%), followed by population-based data sources (15, 35%) and administrative claims data (4, 9%). The most common algorithms were tree-based methods (23, 53%), neural network (NN) (14, 33%), regularized logistic regression (12, 28%), and support vector machine (SVM) (10, 23%). Most of these studies (37, 85%) were of high quality. A majority of these studies (28, 65%) reported ML algorithms with an AUC above 0.70. There was a range of variability within AUC reported by these studies with a median of 0.68 (IQR: 0.64–0.76; range: 0.50–0.90). Conclusions The ML algorithms involving tree-based methods, NN, regularized logistic regression, and SVM are commonly used to predict hospital readmission in the US. Further research is needed to compare the performance of ML algorithms for hospital readmission prediction.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Application of machine learning in predicting hospital readmissions: a scoping review of the literature

Huang

Talwar

Chatterjee

et al. 2021

BMC Med Res Methodol

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“…Support vector machines were the second most utilized ML algorithm ( n = 9), followed by neural networks ( n = 7). The remaining studies presented combinations of these with other ML algorithms such as deep learning, 16,17 Bayesian techniques (including naïve Bayes classifiers and Bayesian networks), 18,19 and K ‐nearest neighbour 20 . Several studies employed multiple ML algorithms and compared them with one or more CSMs.…”

Section: Resultsmentioning

confidence: 99%

“…The remaining studies presented combinations of these with other ML algorithms such as deep learning, 16,17 Bayesian techniques (including naïve Bayes classifiers and Bayesian networks), 18,19 and K-nearest neighbour. 20 Several studies employed multiple ML algorithms and compared them with one or more CSMs. Many studies took advantage of ensemble learning algorithms, which are ML techniques that aggregate the outcomes of multiple-based trained models, producing a unified general result for each data sample (e.g.…”

Section: Machine Learning Methodsmentioning

confidence: 99%

Machine learning vs. conventional statistical models for predicting heart failure readmission and mortality

et al. 2020

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Aims This study aimed to review the performance of machine learning (ML) methods compared with conventional statistical models (CSMs) for predicting readmission and mortality in patients with heart failure (HF) and to present an approach to formally evaluate the quality of studies using ML algorithms for prediction modelling. Methods and results Following Preferred Reporting Items for Systematic Reviews and Meta‐Analyses guidelines, we performed a systematic literature search using MEDLINE, EPUB, Cochrane CENTRAL, EMBASE, INSPEC, ACM Library, and Web of Science. Eligible studies included primary research articles published between January 2000 and July 2020 comparing ML and CSMs in mortality and readmission prognosis of initially hospitalized HF patients. Data were extracted and analysed by two independent reviewers. A modified CHARMS checklist was developed in consultation with ML and biostatistics experts for quality assessment and was utilized to evaluate studies for risk of bias. Of 4322 articles identified and screened by two independent reviewers, 172 were deemed eligible for a full‐text review. The final set comprised 20 articles and 686 842 patients. ML methods included random forests (n = 11), decision trees (n = 5), regression trees (n = 3), support vector machines (n = 9), neural networks (n = 12), and Bayesian techniques (n = 3). CSMs included logistic regression (n = 16), Cox regression (n = 3), or Poisson regression (n = 3). In 15 studies, readmission was examined at multiple time points ranging from 30 to 180 day readmission, with the majority of studies (n = 12) presenting prediction models for 30 day readmission outcomes. Of a total of 21 time‐point comparisons, ML‐derived c‐indices were higher than CSM‐derived c‐indices in 16 of the 21 comparisons. In seven studies, mortality was examined at 9 time points ranging from in‐hospital mortality to 1 year survival; of these nine, seven reported higher c‐indices using ML. Two of these seven studies reported survival analyses utilizing random survival forests in their ML prediction models. Both reported higher c‐indices when using ML compared with CSMs. A limitation of studies using ML techniques was that the majority were not externally validated, and calibration was rarely assessed. In the only study that was externally validated in a separate dataset, ML was superior to CSMs (c‐indices 0.913 vs. 0.835). Conclusions ML algorithms had better discrimination than CSMs in most studies aiming to predict risk of readmission and mortality in HF patients. Based on our review, there is a need for external validation of ML‐based studies of prediction modelling. We suggest that ML‐based studies should also be evaluated using clinical quality standards for prognosis research. Registration: PROSPERO CRD42020134867

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“…In total, 30% of studies mentioned Black patients. Between 0.95% and 100% of the individuals were Black, with one study enrolling only African American males with heart failure [20] .…”

Section: Resultsmentioning

confidence: 99%

Predicting mortality and hospitalization in heart failure using machine learning: A systematic literature review

Mpanya

Çelik

Klug

et al. 2021

IJC Heart & Vasculature

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Impact of a Pharmacist-Led Intervention on 30-Day Readmission and Assessment of Factors Predictive of Readmission in African American Men With Heart Failure

Cited by 17 publications

References 33 publications

Application of machine learning in predicting hospital readmissions: a scoping review of the literature

Application of machine learning in predicting hospital readmissions: a scoping review of the literature

Machine learning vs. conventional statistical models for predicting heart failure readmission and mortality

Predicting mortality and hospitalization in heart failure using machine learning: A systematic literature review

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