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Integrating Explainable Machine Learning in Clinical Decision Support Systems: Study Involving a Modified Design Thinking Approach

Integrating Explainable Machine Learning in Clinical Decision Support Systems: Study Involving a Modified Design Thinking Approach

Explainable machine learning (XML) is a field focused on developing techniques to help end users understand the predictions made by complex models [6]. Indeed, we followed Rudin [7] in adopting the definition of XML as the use of additional post-hoc models to explain a primary black-box model. Such black-box models are in contrast to interpretable models. This includes information concerning the underlying data and performance of the model [8].

Michael Shulha, Jordan Hovdebo, Vinita D’Souza, Francis Thibault, Rola Harmouche

JMIR Form Res 2024;8:e50475

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XML Data and Knowledge-Encoding Structure for a Web-Based and Mobile Antenatal Clinical Decision Support System: Development Study

XML Data and Knowledge-Encoding Structure for a Web-Based and Mobile Antenatal Clinical Decision Support System: Development Study

With these requirements in mind, we chose XML as a better language to represent CPG knowledge. For web applications and mobile apps, this is an ideal choice as a core component of the World Wide Web. XML is key to formatting content into HTML pages and is an industry standard for data communication among different computer systems [11].

Ever Augusto Torres Silva, Sebastian Uribe, Jack Smith, Ivan Felipe Luna Gomez, Jose Fernando Florez-Arango

JMIR Form Res 2020;4(10):e17512

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