Predictive Analytics using Machine Learning for Electronic Health Records
2020 Southeast Decision Sciences Institute (SEDSI) Proceedings
Machine learning algorithms can play a vital role in different organizations such as healthcare. Using machine learning algorithms can impact the healthcare industry tremendously by predicting diseases outbreaks, mortality rates, monitoring risks, and many other factors. Being able to monitor and control these factors can help save the industry cost, and provide for better resource allocation. This paper aims to analyze different machine learning approaches for predicting hospital length of stay (LOS) using electronic health records (EHRs). The use of a machine learning model such as Logistic Regression(LR), Decision Trees and Neural Networks (NN) will be used along with python and SPSS to preprocess and prepare the data for prediction of hospital LOS. The results will show how the preprocessing phase of data can directly influence your results. Based on preprocessing of data, results could yield to a higher or lower accuracy of prediction models of patients’ length of stay in the hospital. Predicting LOS can better assist and provide patients with the proper resources and help reduce hospital cost and provide for a better treatment for the patient. In conclusion, machine learning algorithms can be a vital part for the healthcare industry. It can help save cost, reduce failures and delays in medical settings and impact the healthcare industry tremendously
Fatima, Arjumand, Hayden Wimmer, Cheryl L. Aasheim.
"Predictive Analytics using Machine Learning for Electronic Health Records."
2020 Southeast Decision Sciences Institute (SEDSI) Proceedings.