Prediction of Chronic Disease-Related Inpatient Prolonged Length of Stay Using Machine Learning Algorithms

Symum, Hasan; Zayas‐Castro, José L.

doi:10.4258/hir.2020.26.1.20

Cited by 13 publications

(7 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Algorithms can identify at‐risk populations and individual patients and match them to appropriate and cost‐effective care coordination interventions 15 . Prediction of increasing risk of PLWMCCs may help reallocate resources and redesign health care to ameliorate risks 64,67 . CDS systems can recommend appropriate care, treatments, and best practice for the complex profile of a patient with multiple chronic conditions and facilitate proactive responses that would have profound impact on outcomes 51 .…”

Section: Methodsmentioning

confidence: 99%

“…15 Prediction of increasing risk of PLWMCCs may help reallocate resources and redesign health care to ameliorate risks. 64,67 CDS systems can recommend appropriate care, treatments, and best practice for the complex profile of a patient with multiple chronic conditions and facilitate proactive responses that would have profound impact on outcomes. 51 There is an additional benefit of pharmacology CDS with the ability to reduce physician errors.…”

Section: Using Analytics and Algorithms To Support Clinical Decision Makingmentioning

confidence: 99%

See 1 more Smart Citation

Health information technology to improve care for people with multiple chronic conditions

Samal

Camara

et al. 2021

Health Services Research

View full text Add to dashboard Cite

Objective To review evidence regarding the use of Health Information Technology (health IT) interventions aimed at improving care for people living with multiple chronic conditions (PLWMCC) in order to identify critical knowledge gaps. Data Sources We searched MEDLINE, CINAHL, PsycINFO, EMBASE, Compendex, and IEEE Xplore databases for studies published in English between 2010 and 2020. Study Design We identified studies of health IT interventions for PLWMCC across three domains as follows: self‐management support, care coordination, and algorithms to support clinical decision making. Data Collection/Extraction Methods Structured search queries were created and validated. Abstracts were reviewed iteratively to refine inclusion and exclusion criteria. The search was supplemented by manually searching the bibliographic sections of the included studies. The search included a forward citation search of studies nested within a clinical trial to identify the clinical trial protocol and published clinical trial results. Data were extracted independently by two reviewers. Principal Findings The search yielded 1907 articles; 44 were included. Nine randomized controlled trials (RCTs) and 35 other studies including quasi‐experimental, usability, feasibility, qualitative studies, or development/validation studies of analytic models were included. Five RCTs had positive results, and the remaining four RCTs showed that the interventions had no effect. The studies address individual patient engagement and assess patient‐centered outcomes such as quality of life. Few RCTs assess outcomes such as disability and none assess mortality. Conclusions Despite a growing body of literature on health IT interventions or multicomponent interventions including a health IT component for chronic disease management, current evidence for applying health IT solutions to improve care for PLWMCC is limited. The body of literature included in this review provides critical information on the state of the science as well as the many gaps that need to be filled for digital health to fulfill its promise in supporting care delivery that meets the needs of PLWMCC.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Using Analytics and Algorithms To Support Clinical Decision Makingmentioning

confidence: 99%

Health information technology to improve care for people with multiple chronic conditions

Samal

Camara

et al. 2021

Health Services Research

View full text Add to dashboard Cite

show abstract

“…To avoid overestimating the performance of the model, an imbalanced data set should be treated carefully when training a supervised classification machine learning model [ 18 , 19 ]. Along with accuracy, we wanted to interpret the performance of the model using indicators such as precision, recall, F1 score, AUPRC, and no information rate.…”

Section: Discussionmentioning

confidence: 99%

Predictability of Mortality in Patients With Myocardial Injury After Noncardiac Surgery Based on Perioperative Factors via Machine Learning: Retrospective Study

Shin¹,

Park²,

Lee³

et al. 2021

JMIR Med Inform

View full text Add to dashboard Cite

Background Myocardial injury after noncardiac surgery (MINS) is associated with increased postoperative mortality, but the relevant perioperative factors that contribute to the mortality of patients with MINS have not been fully evaluated. Objective To establish a comprehensive body of knowledge relating to patients with MINS, we researched the best performing predictive model based on machine learning algorithms. Methods Using clinical data from 7629 patients with MINS from the clinical data warehouse, we evaluated 8 machine learning algorithms for accuracy, precision, recall, F1 score, area under the receiver operating characteristic (AUROC) curve, and area under the precision-recall curve to investigate the best model for predicting mortality. Feature importance and Shapley Additive Explanations values were analyzed to explain the role of each clinical factor in patients with MINS. Results Extreme gradient boosting outperformed the other models. The model showed an AUROC of 0.923 (95% CI 0.916-0.930). The AUROC of the model did not decrease in the test data set (0.894, 95% CI 0.86-0.922; P=.06). Antiplatelet drugs prescription, elevated C-reactive protein level, and beta blocker prescription were associated with reduced 30-day mortality. Conclusions Predicting the mortality of patients with MINS was shown to be feasible using machine learning. By analyzing the impact of predictors, markers that should be cautiously monitored by clinicians may be identified.

show abstract

“…While statistical approaches have been applied to forecast hospital LOS but using ML algorithms is proven to have more optimal performance (44). Therefore, healthcare industries and clinicians worldwide adopted various ML algorithms to resolve some LOS prediction uncertainties (45).…”

Section: Discussionmentioning

confidence: 99%

Predictive modeling of hospital Length of Stay in COVID-19 patients using Artificial Neural Networks

Orooji

Shanbehzadeh

Kazemi-Arpanahi³

et al. 2021

Preprint

View full text Add to dashboard Cite

BackgroundThe current pandemic of coronavirus disease (COVID-19) causes unexpected economic burdens to worldwide health organizations with severe shortages in hospital bed capacity and other related medical resources. Therefore, predicting the length of stay (LOS) is essential to ensure optimal allocating scarce hospital resources and inform evidence-based decision-making. Thus, the purpose of this research is to construct a model for predicting COVID-19 patients' hospital LOS by multiple multilayer perceptron-artificial neural network (MLP-ANN) algorithms. Material and MethodsUsing a single-center registry, the records of 1225 laboratory-confirmed COVID-19 hospitalized cases from February 9, 2020, to December 20, 2020, were analyzed. The correlation coefficient technique was developed to determine the most significant variables as the input of the ANN models. Only variables with a correlation coefficient at the P-value< 0.2 were used in model construction. Ultimately the prediction models were developed based on 12 ANN techniques according to selected variables. ResultsAfter implementing feature selection, a total of 20 variables was determined as the most relevant predictors to build the models. The results indicated that the best performance belongs to a neural network with 20 and 10 neurons in the hidden layer of the Bayesian Regularization classifier for whole and selected features with RMSE of 1.6213 and 2.2332, respectively. ConclusionThe developed model in this study can help in the better calculation of LOS in COVID-19 patients. This model also can be leveraged in hospital bed management and optimized resource utilization.

show abstract

Prediction of Chronic Disease-Related Inpatient Prolonged Length of Stay Using Machine Learning Algorithms

Cited by 13 publications

References 22 publications

Health information technology to improve care for people with multiple chronic conditions

Health information technology to improve care for people with multiple chronic conditions

Predictability of Mortality in Patients With Myocardial Injury After Noncardiac Surgery Based on Perioperative Factors via Machine Learning: Retrospective Study

Predictive modeling of hospital Length of Stay in COVID-19 patients using Artificial Neural Networks

Contact Info

Product

Resources

About