Predictive modelling of hospital readmissions in diabetic patients clusters

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

Different scenarios for the prediction of hospital readmission of diabetic patients

Hospitals generate large amounts of data on a daily basis, but most of the time that data is just an overwhelming amount of information which never transitions to knowledge. Through the application of Data Mining techniques it is possible to find hidden relations or patterns among the data and convert those into knowledge that can further be used to aid in the decision-making of hospital professionals. This study aims to use information about patients with diabetes, which is a chronic (long-term) condition that occurs when the body does not produce enough or any insulin. The main purpose is to help hospitals improve their care with diabetic patients and consequently reduce readmission costs. An hospital readmission is an episode in which a patient discharged from a hospital is admitted again within a specified period of time (usually a 30 day period). This period allows hospitals to verify that their services are being performed correctly and also to verify the costs of these re-admissions. The goal of the study is to predict if a patient who suffers from diabetes will be readmitted, after being discharged, using Machine Leaning algorithms. The final results revealed that the most efficient algorithm was Random Forest with 0.898 of accuracy.This work has been supported by FCT – Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020

Predicting diabetes-related hospitalizations based on electronic health records

OBJECTIVE: To derive a predictive model to identify patients likely to be hospitalized during the following year due to complications attributed to Type II diabetes. METHODS: A variety of supervised machine learning classification methods were tested and a new method that discovers hidden patient clusters in the positive class (hospitalized) was developed while, at the same time, sparse linear support vector machine classifiers were derived to separate positive samples from the negative ones (non-hospitalized). The convergence of the new method was established and theoretical guarantees were proved on how the classifiers it produces generalize to a test set not seen during training. RESULTS: The methods were tested on a large set of patients from the Boston Medical Center - the largest safety net hospital in New England. It is found that our new joint clustering/classification method achieves an accuracy of 89% (measured in terms of area under the ROC Curve) and yields informative clusters which can help interpret the classification results, thus increasing the trust of physicians to the algorithmic output and providing some guidance towards preventive measures. While it is possible to increase accuracy to 92% with other methods, this comes with increased computational cost and lack of interpretability. The analysis shows that even a modest probability of preventive actions being effective (more than 19%) suffices to generate significant hospital care savings. CONCLUSIONS: Predictive models are proposed that can help avert hospitalizations, improve health outcomes and drastically reduce hospital expenditures. The scope for savings is significant as it has been estimated that in the USA alone, about $5.8 billion are spent each year on diabetes-related hospitalizations that could be prevented.Accepted manuscrip

Predicting 30-day hospital readmission for diabetes patients using multilayer perceptron

Hospital readmission is considered a key metric in order to assess health center performances. Indeed, readmissions involve different consequences such as the patient's health condition, hospital operational efficiency but also cost burden from a wider perspective. Prediction of 30-day readmission for diabetes patients is therefore of prime importance. The existing models are characterized by their limited prediction power, generalizability and pre-processing. For instance, the benchmarked LACE (Length of stay, Acuity of admission, Charlson comorbidity index and Emergency visits) index traded prediction performance against ease of use for the end user. As such, this study propose a comprehensive pre-processing framework in order to improve the model's performance while exploring and selecting a prominent feature for 30-day unplanned readmission among diabetes patients. In order to deal with readmission prediction, this study will also propose a Multilayer Perceptron (MLP) model on data collected from 130 US hospitals. More specifically, the pre-processing technique includes comprehensive data cleaning, data reduction, and transformation. Random Forest algorithm for feature selection and SMOTE algorithm for data balancing are some example of methods used in the proposed pre-processing framework. The proposed combination of data engineering and MLP abilities was found to outperform existing research when implemented and tested on health center data. The performance of the designed model was found, in this regard, particularly balanced across different metrics of interest with accuracy and Area under the Curve (AUC) of 95% and close to the optimal recall of 99%

A guided neural network approach to predict early readmission of diabetic patients

Diabetes is a major chronic health problem affecting millions globally. Effective diabetes management can reduce the risk of hospital readmission and the associated financial losses for both the healthcare system and insurance companies. Hospital readmission is a high-priority healthcare quality measure that reflects the inadequacies in the healthcare system that also increase healthcare costs and negatively influence hospitals’ reputation. Predicting readmissions in the early stages prompts great attention to patients with a high risk of readmission. There has been some attempt in applying machine learning predictive models such as ensemble learning with Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM) and Artificial Neural Networks (ANN) to correctly identify if the readmission can happen within 30 days (< 30 days) or it may never happen or happens after 30 days ( ≥30 days). We are proposing a new method that is applied to ANN to guide it through its gradient descent optimizers by realizing consistent vs inconsistent data in every batch. Our results show that there are up to 1.5% improvement in classification accuracies in both 2-class and 3-class variations of the experimented benchmark dataset when using the guided optimizer to train the ANN as opposed to the standard optimizer. Guided ANN is also able to achieve better error convergence than standard ANN

Insulin Dependence Heralds Adverse Events After Hip And Knee Arthroplasty

ABSTRACT Background – Total hip arthroplasty (THA) is one of the most frequently performed orthopaedic procedures. As the prevalence of diabetes mellitus (DM) continues to increase the burden of its sequelae and associated surgical complications have also increased. For these reasons, it is important to understand the associations between DM and the rates of perioperative adverse events in patients with DM who will undergo THA. Methods – The American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) database records perioperative adverse events as well as patient factors including demographics and comorbidities. Patients who underwent THA between 2005 and 2014 were identified and characterized as having insulin dependent diabetes mellitus (IDDM), non-insulin dependent diabetes mellitus (NIDDM), or neither. Multivariate Poisson regression was used to assess the relative risk of multiple adverse events in the initial 30 postoperative days while controlling for demographic and comorbid factors. Results – A total of 71,733 patients who underwent THA were identified (1,920 IDDM, 6,305 NIDDM, and 63,508 without DM). Relative to patients without diabetes, patients with NIDDM were at an increased relative risk for 3 of 17 adverse events studied. These were sepsis or septic shock, readmission to hospital within 30 days, and extended postoperative length of stay (LOS) (greater than 5 days). Patients with IDDM were at an increased relative risk for 11 of 17 adverse events studied. These included death, sepsis or septic shock, myocardial infarction, wound-related infection, unplanned intubation, renal insufficiency, return to the operating room, readmission, pneumonia, urinary tract infection, and extended LOS. Patients with IDDM and NIDDM were both at greater risk for sepsis or septic shock, readmission, and extended LOS. Patients with IDDM were at greater risk for all of these adverse events (sepsis or septic shock: relative risk [RR] = 3.53 versus 1.90, for IDDM and NIDDM respectively, readmission: RR = 2.11 vs. 1.28, and extended LOS: RR = 2.26 vs. 1.35). Conclusions – Compared to patients with NIDDM, patients with IDDM are at greater risk for many more perioperative adverse events relative to patients without diabetes. These findings have important implications for patient selection, preoperative risk stratification, and postoperative expectations