Abstract
In this article, the authors provide an overview of the potential and challenges of machine learning for healthcare decision support. They first discuss the healthcare decision support ecosystems, including (1) beneficiaries, (2) health data, and (3) models. They then introduce the three main challenges of the healthcare decision support systems: data complexity, decision criticality, and model explainability. From there, they use unplanned intensive care unit readmission predictions in tackling the three main challenges of machine learning-based healthcare decision support systems. They investigate the data complexity issue by adopting dimension reduction techniques on patients' medical records to integrate patients' chart events, demographics, and the ICD-9 code. To address the decision criticality issue, they perform an in-depth deep learning performance analysis, and they analyze each feature's contribution to the predictive model. To unpack the model explainability issue, they illustrate the importance of each input feature and its combinations in the predictive model.
Key Terms in this Chapter
Healthcare Data Analytics: A process of applying various analytical tools on historical healthcare data to identify patterns and get actionable insights to treat diseases.
Unplanned ICU Readmission: Unplanned ICU readmissions are defined as ICU patients who were rehospitalized within 30 days of hospital discharge.
Decision Support Systems: A decision support system is an information system used to support the courses of decisions in an organization or a business.
Machine Learning: Machine learning is a kind of algorithm that provides systems the ability to automatically learn and improve from data without human intervention.
Healthcare Decision Support Ecosystem: Healthcare decision support ecosystem include three major components: beneficiaries, data, and models.
Healthcare Decision Support: A kind of decision support system to improve healthcare delivery by integrating different healthcare data, including patient information, patient activities, healthcare knowledge, and other clinical information.
Deep Learning: Deep learning is a subset of machine learning, which is a neural network imitating the structure of a human brain.