Abstract
Recently, machine learning has become a hot research topic. Therefore, this study investigates the interaction between software engineering and machine learning within the context of health systems. We proposed a novel framework for health informatics: the framework and methodology of software engineering for machine learning in health informatics (SEMLHI). The SEMLHI framework includes four modules (software, machine learning, machine learning algorithms, and health informatics data) that organize the tasks in the framework using a SEMLHI methodology, thereby enabling researchers and developers to analyze health informatics software from an engineering perspective and providing developers with a new road map for designing health applications with system functions and software implementations. Our novel approach sheds light on its features and allows users to study and analyze the user requirements and determine both the function of objects related to the system and the machine learning algorithms that must be applied to the dataset. Our dataset used in this research consists of real data and was originally collected from a hospital run by the Palestine government covering the last three years. The SEMLHI methodology includes seven phases: designing, implementing, maintaining and defining workflows; structuring information; ensuring security and privacy; performance testing and evaluation; and releasing the software applications.
Highlights
The field of health informatics (HI) aims to provide a largescale linkage among disparate ideas
The SEMLHI framework was composed of four components that help developers observe the health application flow from the main module to submodules to run and validate specific tasks
Our methodology was applicable to current systems or in the development of new systems that use the Machine learning (ML) module for current systems, which can be used in regular updates to add data to the system, to perform irregular updates and to add new features such as new versions of ICD diagnosis codes, minor model improvements for bug fixes, new functionalities required by the client, and new hardware or architectural constraints
Summary
The field of health informatics (HI) aims to provide a largescale linkage among disparate ideas. For health-care data analytics, the widely known 3P tools [10] were used Many simple applications, such as WEKA, which provided a GUI for many machine learning algorithms [11], while Apache Spark was used for the cluster computing framework [12], are powerful. The SEMLHI framework was composed of four components that help developers observe the health application flow from the main module to submodules to run and validate specific tasks. It supports a structure that presents a common set of ML terminology to use, compare, measure, and design software systems in the area of health This creates a space whereby SE and ML experts can work on a specific methodological approach to enable health informatics software development teams to integrate the ML model lifecycle. Our methodology was applicable to current systems or in the development of new systems that use the ML module for current systems, which can be used in regular updates to add data to the system, to perform irregular updates and to add new features such as new versions of ICD diagnosis codes, minor model improvements for bug fixes, new functionalities required by the client, and new hardware or architectural constraints
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