Computational intelligence contributions to readmisision risk prediction in Healthcare systems

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

Contributions from computational intelligence to healthcare data processing

80 p.The increasing ability to gather, store and process health care information, through the electronic health records and improved communication methods opens the door for new applications intended to improve health care in many different ways. Crucial to this evolution is the development of new computational intelligence tools, related to machine learning and statistics. In this thesis we have dealt with two case studies involving health data. The first is the monitoring of children with respiratory diseases in the pediatric intensive care unit of a hospital. The alarm detection is stated as a classification problem predicting the triage selected by the nurse or medical doctor. The second is the prediction of readmissions leading to hospitalization in an emergency department of a hospital. Both problems have great impact in economic and personal well being. We have tackled them with a rigorous methodological approach, obtaining results that may lead to a real life implementation. We have taken special care in the treatment of the data imbalance. Finally we make propositions to bring these techniques to the clinical environment

Reduction of emergency department returns after discharge from hospital: Machine learning model to predict emergency department returns 30 days post hospital discharge for medical patients

Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Business AnalyticsPost-hospital discharge returns to emergency departments are associated with reducing the efficiency of the emergency department (ED) utilisation and the quality of healthcare. These returns are often related to the nature of the disease and/or inadequate care. This thesis aims to develop a machine-learning model that predicts ED returns within 30 days of inpatient discharge from Portuguese public hospitals. Different binary classification models were trained and evaluated with a particular focus on sensitivity (predictive power of the critical class of returning patients). The selected model was the Extreme gradient boost Classifier, which showed the best performance on recall and the other considered performance metrics. A cohort of 93 449 medical hospitalisations of adult patients discharged between January 1st, 2018, and December 31st, 2019, was assembled with diagnoses details to be used in this study. According to the problem's requirement, the recall was the performance metric to be maximised. Therefore, Performance optimisation methods were considered, and the final model resulted in a recall of 84.38%, precision of 84.35%, F1 score of 84.36% and accuracy of 84.10%. Future deployment and integration of this ED return predictive analytics into the inpatient care workflow may allow identifying patients that require targeted care interventions that reduce overall healthcare expense and improve health outcomes

Computational intelligence contributions to readmisision risk prediction in Healthcare systems

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

Ensemble Risk Model of Emergency Admissions (ERMER)

Introduction About half of hospital readmissions can be avoided with preventive interventions. Developing decision support tools for identification of patients’ emergency readmission risk is an important area of research. Because, it remains unclear how to design features and develop predictive models that can adjust continuously to a fast-changing healthcare system and population characteristics. The objective of this study was to develop a generic ensemble Bayesian risk model of emergency readmission. Methods We produced a decision support tool that predicts risk of emergency readmission using England's Hospital Episode Statistics inpatient database. Firstly, we used a framework to develop an optimal set of features. Then, a combination of Bayes Point Machine (BPM) models for different cohorts was considered to create an optimised ensemble model, which is stronger than the individual generative and non-linear classifications. The developed Ensemble Risk Model of Emergency Admissions (ERMER) was trained and tested using three time-frames: 1999-2004, 2000-05 and 2004-09, each of which includes about 20% of patients in England during the trigger year. Results Comparisons are made for different time-frames, sub-populations, risk cut-offs, risk bands and top risk segments. The precision was 71.6% to 73.9%, the specificity was 88.3% to 91.7% and the sensitivity was 42.1% to 49.2% across different time-frames. Moreover, the Area Under the Curve was 75.9% to 77.1%. Conclusions The decision support tool performed considerably better than the previous modelling approaches, and it was robust and stable with high precision. Moreover, the framework and the Bayesian model allow the model to continuously adjust it to new significant features, different population characteristics and changes in the system

Contributions from computational intelligence to healthcare data processing

80 p.The increasing ability to gather, store and process health care information, through the electronic health records and improved communication methods opens the door for new applications intended to improve health care in many different ways. Crucial to this evolution is the development of new computational intelligence tools, related to machine learning and statistics. In this thesis we have dealt with two case studies involving health data. The first is the monitoring of children with respiratory diseases in the pediatric intensive care unit of a hospital. The alarm detection is stated as a classification problem predicting the triage selected by the nurse or medical doctor. The second is the prediction of readmissions leading to hospitalization in an emergency department of a hospital. Both problems have great impact in economic and personal well being. We have tackled them with a rigorous methodological approach, obtaining results that may lead to a real life implementation. We have taken special care in the treatment of the data imbalance. Finally we make propositions to bring these techniques to the clinical environment