3,678 research outputs found

    Predictive modelling of hospital readmissions in diabetic patients clusters

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    Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceDiabetes is a global public health problem with increasing incidence over the past 10 years. This disease's social and economic impacts are widely assessed worldwide, showing a direct and gradual decrease in the individual's ability to work, a gradual loss in the scale of quality of life and a burden on personal finances. The recurrence of hospitalisation is one of the most significant indexes in measuring the quality of care and the opportunity to optimise resources. Numerous techniques identify the patient who will need to be readmitted, such as LACE and HOSPITAL. The purpose of this study was to use a dataset related to the risk of hospital readmission in patients with Diabetes first to apply a clustering of subgroups by similarity. Then structures a predictive analysis with the main algorithms to identify the methodology of best performance. Numerous approaches were performed to prepare the dataset for these two interventions. The results found in the first phase were two clusters based on the total number of hospital recurrences and others on total administrative costs, with K=3. In the second phase, the best algorithm found was Neural Network 3, with a ROC of 0.68 and a misclassification rate of 0.37. When applied the same algorithm in the clusters, there were no gains in the confidence of the indexes, suggesting that there are no substantial gains in the division of subpopulations since the disease has the same behaviour and needs throughout its development

    A novel framework for predicting patients at risk of readmission

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    Uncertainty in decision-making for patients’ risk of re-admission arises due to non-uniform data and lack of knowledge in health system variables. The knowledge of the impact of risk factors will provide clinicians better decision-making and in reducing the number of patients admitted to the hospital. Traditional approaches are not capable to account for the uncertain nature of risk of hospital re-admissions. More problems arise due to large amount of uncertain information. Patients can be at high, medium or low risk of re-admission, and these strata have ill-defined boundaries. We believe that our model that adapts fuzzy regression method will start a novel approach to handle uncertain data, uncertain relationships between health system variables and the risk of re-admission. Because of nature of ill-defined boundaries of risk bands, this approach does allow the clinicians to target individuals at boundaries. Targeting individuals at boundaries and providing them proper care may provide some ability to move patients from high risk to low risk band. In developing this algorithm, we aimed to help potential users to assess the patients for various risk score thresholds and avoid readmission of high risk patients with proper interventions. A model for predicting patients at high risk of re-admission will enable interventions to be targeted before costs have been incurred and health status have deteriorated. A risk score cut off level would flag patients and result in net savings where intervention costs are much higher per patient. Preventing hospital re-admissions is important for patients, and our algorithm may also impact hospital income

    A theoretical framework for research on readmission risk prediction

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    On the one hand, predictive analytics is an important field of research in Information Systems (IS); however, research on predictive analytics in healthcare is still scarce in IS literature. One area where predictive analytics can be of great benefit is with regard to unplanned readmissions. While a number of studies on readmission prediction already exists in related research areas, there are few guidelines to date on how to conduct such analytics projects. To address this gap the paper presents the general process to develop empirical models by Shmueli and Koppius (2011) and extends this to the specific requirements of readmission risk prediction. Based on a systematic literature review, the resulting process defines important aspects of readmission prediction. It also structures relevant questions and tasks that need to be taken care of in this context. This extension of the guidelines by Shmueli and Koppius (2011) provides a best practice as well as a template that can be used in future studies on readmission risk prediction, thus allowing for more comparable results across various research fields

    A clustering approach for predicting readmissions in intensive medicine

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    Decision making assumes a critical role in the Intensive Medicine. Data Mining is emerging in the clinical area to provide processes and technologies for transforming data into useful knowledge to support clinical decision makers. Appling clustering techniques to the data available on the patients admitted into Intensive Care Units and knowing which ones correspond to readmissions, it is possible to create meaningful clusters that will represent the base characteristics of readmitted patients. Thus, exploring common characteristics it is possible to prevent discharges that will result into readmissions and then improve the patient outcome and reduce costs. Moreover, readmitted patients present greater difficulty to be recovered. In this work it was followed the Stability and Workload Index for Transfer (SWIFT). A subset of variables from SWIFT was combined with the results from laboratory exams, namely the Lactic Acid and the Leucocytes values, in order to create clusters to identify, in the moment of discharge, patients that probably will be readmitted

    Analyzing Patients’ EHR: Predicting and Explaining Admission Consequences for COPD and Liver Disease Patients

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    This study analyzed the admission outcomes in chronic patients (with COPD, and Liver disease) to demonstrate the feasibility of applying prediction methods on EHR records while incorporating an explainable AI technique. We predicted three target variables: 30-day readmission, Medium&Long Length of Stay and Single-day admission and analyzed the features using an explainable AI technique, the SHapley Additive exPlanations (SHAP). The results show that Readmission had higher prediction scores than all other dependent variables. Some features affected all target variables with either positive or negative influence including: Age, Charlson comorbidity index, Day-Shift, Gender, using EHR screens and Insurance cover level. These findings thus point to the value of using Machine-Learning combined with an explainable AI method to understand and assess the risks factors. The assessment of the potential factors leading to multiple complications can bolster prevention-oriented medical decisions to groups of patients but can also be tailored to the patient level

    Computational intelligence contributions to readmisision risk prediction in Healthcare systems

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    136 p.The Thesis tackles the problem of readmission risk prediction in healthcare systems from a machine learning and computational intelligence point of view. Readmission has been recognized as an indicator of healthcare quality with primary economic importance. We examine two specific instances of the problem, the emergency department (ED) admission and heart failure (HF) patient care using anonymized datasets from three institutions to carry real-life computational experiments validating the proposed approaches. The main difficulties posed by this kind of datasets is their high class imbalance ratio, and the lack of informative value of the recorded variables. This thesis reports the results of innovative class balancing approaches and new classification architectures

    Development and evaluation of ensemble-based classification models for predicting unplanned hospital readmissions after hysterectomy

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    Unplanned hospital readmissions are a key indicator of quality in healthcare and can lead to high unnecessary costs for the hospital due to additional required resources or reduced payments by insurers or governments. Predictive analytics can support the identification of patients at high-risk for readmission early on to enable timely interventions. In Australia, hysterectomies present the 2nd highest observed readmission rates of all surgical procedures in public hospitals. Prior research so far only focuses on developing explanatory models to identify associated risk factors for past patients. In this study, we develop and compare 24 prediction models using state-of-the-art sampling and ensemble methods to counter common problems in readmission prediction, such as imbalanced data and poor performance of individual classifiers. The application and evaluation of these models are presented, resulting in an excellent predictive power with under- and oversampling and an additional slight increase in performance when combined with ensemble methods
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