Predictive analytics and deep learning techniques in electronic medical records : recent advancements and future direction

The demands on medical services are increasing rapidly in the global context. Therefore, handling beds availability, identifying and managing the length of stay (LOS) is creating persistent needs for the physicians, nurses, clinicians, hospital management, and caregivers in the public hospital admissions and the private hospital admissions. Health analytics provides unprecedented ways to predict trends, patients’ future outcomes, knowledge discovery, and improving the decision making in the clinical settings. This paper reviews the state-of-the-art machine learning, deep learning techniques and the related work in relation to the length of stay common hospital admissions. Research trends and future direction for the forecasting LOS in medical admissions are discussed in this paper

Benchmarking predictive models in electronic health records : sepsis length of stay prediction

Forecasting Sepsis length of stay is a challenge for hospitals worldwide. Although there are many attempts to improve sepsis length of stay prediction; however, there is still lack of baselining prediction metrics that can give better results for sepsis length of stay prediction in management hospital systems. This paper introduces a research architecture to predict and benchmark the Length of Stay (LOS) for Sepsis diagnoses from electronic medical records using the machine learning models. The architecture considered the time factor to identify the outperforming algorithms for Sepsis LOS prediction. This work contributes to the field of predictive modelling and information visualization for hospital management systems. Our results showed that the ensemble methods in particular the random forest (RF) outdo other classification models to predict the LOS for Sepsis from electronic medical records for Intensive Care Unit “ICU”-based hospitalizations