Machine Learning for Administrative Health Records: A Systematic Review
  of Techniques and Applications
Adrian Caruana Madhushi Bandara Katarzyna Musial Daniel Catchpoole Paul J. Kennedy
Machine learning provides many powerful and effective techniques for
analysing heterogeneous electronic health records (EHR). Administrative Health
Records (AHR) are a subset of EHR collected for administrative purposes, and
the use of machine learning on AHRs is a growing subfield of EHR analytics.
Existing reviews of EHR analytics emphasise that the data-modality of the EHR
limits the breadth of suitable machine learning techniques, and pursuable
healthcare applications. Despite emphasising the importance of data modality,
the literature fails to analyse which techniques and applications are relevant
to AHRs. AHRs contain uniquely well-structured, categorically encoded records
which are distinct from other data-modalities captured by EHRs, and they can
provide valuable information pertaining to how patients interact with the
healthcare system.
  This paper systematically reviews AHR-based research, analysing 70 relevant
studies and spanning multiple databases. We identify and analyse which machine
learning techniques are applied to AHRs and which health informatics
applications are pursued in AHR-based research. We also analyse how these
techniques are applied in pursuit of each application, and identify the
limitations of these approaches. We find that while AHR-based studies are
disconnected from each other, the use of AHRs in health informatics research is
substantial and accelerating. Our synthesis of these studies highlights the
utility of AHRs for pursuing increasingly complex and diverse research
objectives despite a number of pervading data- and technique-based limitations.
Finally, through our findings, we propose a set of future research directions
that can enhance the utility of AHR data and machine learning techniques for
health informatics research.