An Interflow System Requirement Analysis in Health Informatics Field

Cheng, Po-Hsun; Chen, Ying-Pei; Lai, Jin-Shin

doi:10.1109/csie.2009.453

Cited by 2 publications

(2 citation statements)

References 4 publications

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“…ML algorithms in clinical genomics generally come in three main forms: supervised, unsupervised and semi-supervised [16]. Interflow system requirement analysis (ISRA) has been used to determine the system requirements [17].…”

Section: Introductionmentioning

confidence: 99%

A Novel Software Engineering Approach Toward Using Machine Learning for Improving the Efficiency of Health Systems

2020

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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. INDEX TERMS Health dataset analysis, machine learning, methodology, software development management, software engineering.

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Section: Introductionmentioning

confidence: 99%

A Novel Software Engineering Approach Toward Using Machine Learning for Improving the Efficiency of Health Systems

2020

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“…Based on software development, ML algorithms in clinical genomics generally come in three main forms: supervised, unsupervised and semi supervised [13]. Interflow system requirement analysis (ISRA) [14] is used to determine system requirements.…”

Section: Introductionmentioning

confidence: 99%

Software Engineering for Machine Learning in Health Informatics

Moreb

Ata

2019

Preprint

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Background We propose a novel framework for health Informatics: framework and methodology of Software Engineering for machine learning in Health Informatics (SEMLHI). This framework shed light on its features, that allow users to study and analyze the requirements, determine the function of objects related to the system and determine the machine learning algorithms that will be used for the dataset.Methods Based on original data that collected from the hospital in Palestine government in the past three years, first the data validated and all outlier removed, analyzed using develop framework in order to compare ML provide patients with real-time. Our proposed module comparison with three Systems Engineering Methods Vee, agile and SEMLHI. The result used by implement prototype system, which require machine learning algorithm, after development phase, questionnaire deliver to developer to indicate the result using three methodology. SEMLHI framework, is composed into four components: software, machine learning model, machine learning algorithms, and health informatics data, Machine learning Algorithm component used five algorithms use to evaluate the accuracy for machine learning models on component.Results we compare our approach with the previously published systems in terms of performance to evaluate the accuracy for machine learning models, the results of accuracy with different algorithms applied for 750 case, linear SVG have about 0.57 value compared with KNeighbors classifier, logistic regression, multinomial NB, random forest classifier. This research investigates the interaction between SE, and ML within the context of health informatics, our proposed framework define the methodology for developers to analyzing and developing software for the health informatic model, and create a space, in which software engineering, and ML experts could work on the ML model lifecycle, on the disease level and the subtype level.Conclusions This article is an ongoing effort towards defining and translating an existing research pipeline into four integrated modules, as framework system using the dataset from healthcare to reduce cost estimation by using a new suggested methodology. The framework is available as open source software, licensed under GNU General Public License Version 3 to encourage others to contribute to the future development of the SEMLHI framework.

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An Interflow System Requirement Analysis in Health Informatics Field

Cited by 2 publications

References 4 publications

A Novel Software Engineering Approach Toward Using Machine Learning for Improving the Efficiency of Health Systems

A Novel Software Engineering Approach Toward Using Machine Learning for Improving the Efficiency of Health Systems

Software Engineering for Machine Learning in Health Informatics

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