Introduction to Predicting Readmission

New approaches to predicting readmissions are required to help mitigate risk and develop novel interventions.

CONCLUSION This preliminary study suggested that prolonged overlap time may have potential for predicting readmission and cardiac mortality risk assessment in HF.

Therefore, this study aimed to investigate the utility of type 2 biomarkers (eosinophils, periostin, and YKL-40) and a type 1 biomarker (CXCL9) in predicting readmission events in patients with AECOPD.

Few, if any, previous studies have investigated the factors predicting readmission rates over variable time periods.

Here, PRISMA was used to identify studies dealing with predicting readmissions for CHF and COPD patients that implemented machine learning techniques.

Factors predicting readmission after being DAMA were identified using logistic regression analysis.

While the Hospital Frailty Risk Score had a better discrimination for predicting readmissions, Johns Hopkins Claims-Based Frailty Index had a better discrimination for predicting mortality.

Conclusion: The number of selected features by PCA-based feature selection method improve the predictive performance based on accuracy of deep learning and most machine learning models for predicting readmission.

This study aimed to evaluate the role of frailty in predicting readmission, length of stay, and quality of life in the hospitalized older adults.

In this study, it was aimed to determine the rates of readmission and variables predicting readmission in patients with alcohol addiction.

To prevent hospital admissions, previous studies have focused on predicting readmissions relating to a defined index admission and specific condition, whereas generic models suited for community-dwelling persons are lacking.

9% as an optimal return to baseline percent threshold for predicting readmission.

Four models were identified for predicting readmission, with one external validation study.

Usually, predicting readmission is binary classification task, and people intuitively build a single model to solve the problem.

10, p-value CONCLUSIONS Machine learning algorithms are potentially valuable tools for predicting readmission after pediatric spine fusion surgery.

C-index for all three variables predicting readmission was 0.

Finally, using insights from sampling theory, we suggest a novel uncertainty-based perspective for predicting readmissions and non-readmissions.

Our REPRESS model performed better when compared with LACE for predicting readmission risk in a sepsis population.

Traditional markers of disease severity such as pulmonary function have limited utility in predicting readmission.

Results from random forest analyses revealed that TC strategies patients reported receiving were more important in predicting readmissions than TC strategies that hospitals reported delivering, and that other key co-variates, such as patient comorbidities, were the most important variables.

A more effective model of predicting readmission system can play an important role in helping hospitals and ICU doctors to find the patients who are going to be readmitted before discharge.

The derived score, which we call the Cancer READMIT score, had modest discriminatory performance in predicting readmissions (area under the curve for the model receiver operating characteristic curve = 0.

The deep learning model offered no improvement over traditional models (logistic regression and EasyEnsemble) for predicting readmission, whereas the other two algorithms led to much smaller differences between the training and testing datasets.

BACKGROUND To review the performance of machine learning (ML) methods compared to conventional statistical models (CSM) for predicting readmission and mortality in patients with myocardial infarction (MI).

300 patients with different risk levels (low, moderate and high complexity) to address the seven medical use cases of GATEKEEPER: •Lifestyle-related early detection and interventions •COPD (Chronic Obstructive Pulmonary Disease)  exacerbations management •Diabetes: predictive modelling of glycaemic status •Parkinson´s disease treatment DSS (Decision Support Systems) •Predicting readmissions and decompensations in heart  failure •Primary and secondary stroke prevention •Multi-chronic elderly patient management including polimedication To achieve this challenge the Basque Country will select and deploy from the GATEKEEPER platform a set of services and tools corresponding to the medical use cases to personalized early detection and interventions in order to test and demonstrate the benefits of GATEKEEPER solutions.

Research to further validate the accuracy of the AMI READMITS score in predicting readmissions may support adoption of the protocol in clinical practice.

RESULTS Our best performing model predicting readmission at discharge using clinical embeddings showed a testing area under receiver operating characteristic curve of 0.

The aim of this study was to examine treatment effects on predicting readmission to drug use treatment and being convicted for a criminal offence among youth.

Over the past decade, several data-driven solutions for predicting readmissions have been presented.

CONCLUSION  Feature extraction methods can improve the predictive performance of machine learning methods for predicting readmissions.

, Black, to have had prior Medicaid or hospitalization, and other comorbidities; significant interactions were seen between ADI quintile and Medicaid predicting readmission.

Conclusions: Adding the aggregate pharmacy score to the reference model significantly increased the c statistic but was out-performed by the comorbidity burden score model in predicting readmission.

Conclusions: This small set of data in a specific patient population found that the current risk prediction score was inaccurate in predicting readmission in the neuroscience intensive care unit population.

The possibility of episode-based bundled payments for spine surgery necessitates analysis of the factors predicting readmissions and postoperative complications.

1% with both Models 1 and 2 predicting readmission (OR: 1.

Recent developments in machine learning have been successful at predicting readmissions from the medical history of the diabetic patient.

On internal calibration, the tool was extremely accurate in predicting readmissions up to 25%.

Conclusions Dynamically predicting readmission and quantifying trends over patients’ hospital stay illuminated differing risk trajectory groups.

Machine-learning (ML) methods have shown improved predictive ability in various clinical contexts, but their utility in predicting readmission after hospitalization for AMI is unknown.

This study aimed to evaluate the model's performance in predicting readmissions 15 and 30 days postdischarge.

Therefore, we evaluated the performance of a widely utilized simplified index developed at the hospital level ‐ LACE (length of stay, acute admission, Charlson comorbidity index score, and emergency department visits) and developed and evaluated a novel index in predicting readmissions in this patient population.

The purpose of this paper is to explore whether a new screening tool for predicting readmissions and functional decline in medical patients>65 years of age could be implemented and its influence on cross-continuum collaborations between the primary and secondary sectors.

5 Decision Tree in predicting readmissions of diabetic patients, especially patients who have undergone HbA1c examination.

Predicting readmissions and long hospital stays in early stages allows extensive attention to patients identified with higher risks which leverages the community's healthcare and saves healthcare expenses and resources.

Pro-inflammatory cytokine IL-6 and anti-inflammatory cytokine IL-10 may be postoperative markers of cardiac injury, and IL-6, specifically, shows promise in predicting readmission and mortality following cardiac surgery.