Development of an Architecture to Implement Machine Learning Based Risk Prediction in Clinical Routine: A Service-Oriented Approach.

BACKGROUND: Patients at risk of developing a disease have to be identified at an early stage to enable prevention. One way of early detection is the use of machine learning based prediction models trained on electronic health records. OBJECTIVES: The aim of this project was to develop a software solution to predict cardiovascular and nephrological events using machine learning models. In addition, a risk verification interface for health care professionals was established. METHODS: In order to meet the requirements, different tools were analysed. Based on this, a software architecture was created, which was designed to be as modular as possible. RESULTS: A software was realised that is able to automatically calculate and display risks using machine learning models. Furthermore, predictions can be verified via an interface adapted to the need of health care professionals, which shows data required for prediction. CONCLUSION: Due to the modularised software architecture and the status-based calculation process, different technologies could be applied. This facilitates the installation of the software at multiple health care providers, for which adjustments need to be carried out at one part of the software only.

Machine Learning Based Risk Prediction for Major Adverse Cardiovascular Events.

BACKGROUND: Patients with major adverse cardiovascular events (MACE) such as myocardial infarction or stroke suffer from frequent hospitalizations and have high mortality rates. By identifying patients at risk at an early stage, MACE can be prevented with the right interventions. OBJECTIVES: The aim of this study was to develop machine learning-based models for the 5-year risk prediction of MACE. METHODS: The data used for modelling included electronic medical records of more than 128,000 patients including 29,262 patients with MACE. A feature selection based on filter and embedded methods resulted in 826 features for modelling. Different machine learning methods were used for modelling on the training data. RESULTS: A random forest model achieved the best calibration and discriminative performance on a separate test data set with an AUROC of 0.88. CONCLUSION: The developed risk prediction models achieved an excellent performance in the test data. Future research is needed to determine the performance of these models and their clinical benefit in prospective settings.

Effect of Nursing Assessment on Predictive Delirium Models in Hospitalised Patients.

Delirium is an acute neuropsychiatric syndrome which is common in elderly patients during their hospitalisation and is associated with an increased mortality and morbidity. Since delirium is a) often underdiagnosed and b) preventable if early signs are detected,igh expectations are set in delirium risk assessment during hospital admission. In our latest studies, we showed that delirium prediction using machine learning algorithms is possible based on the patients' health history. The aim of this study is to compare the influence of nursing assessment data on prediction models with clinical and demographic data. We approached the problem by a) comparing the performance of predictive models including nursing data with models based on clinical and demographic data only and b) analysing the feature importance of all available features. From our results we concluded that nursing assessment data can improve the performance of delirium prediction models better than demographic, laboratory, diagnosis, procedures, and previous transfers' data alone.