Skip to content

Research at St Andrews

Bringing modern machine learning into clinical practice through the use of intuitive visualization and human-computer interaction

Research output: Contribution to journalArticle

Author(s)

Richard Osuala, Jieyi Li, Ognjen Arandelovic

School/Research organisations

Abstract

The increasing trend of systematic collection of medical data (diagnoses, hospital admission emergencies, blood test results, scans, etc) by healthcare providers offers an unprecedented opportunity for the application of modern data mining, pattern recognition, and machine learning algorithms. The ultimate aim is invariably that of improving outcomes, be it directly or indirectly. Notwithstanding the successes of recent research efforts in this realm, a major obstacle of making the developed models usable by medical professionals (rather than computer scientists or statisticians) remains largely unaddressed. Yet, a mounting amount of evidence shows that the ability to understand and easily use novel technologies is a major factor governing how widely adopted by the target users (doctors, nurses, and patients, amongst others) they are likely to be. In this work we address this technical gap. In particular, we describe a portable, web-based interface that allows healthcare professionals to interact with recently developed machine learning and data driven prognostic algorithms. Our application interfaces a statistical disease progression model and displays its predictions in an intuitive and readily understandable manner. Different types of geometric primitives and their visual properties (such as size or colour) are used to represent abstract quantities such as probability density functions, the rate of change of relative probabilities, and a series of other relevant statistics which the heathcare professional can use to explore patients’ risk factors or provide personalized, evidence and data driven incentivization to the patient.
Close

Details

Original languageEnglish
Article number3
Number of pages11
JournalAugmented Human Research
Volume4
Early online date19 Feb 2019
DOIs
Publication statusPublished - Apr 2019

    Research areas

  • Health care, Data, Visualization, Medicine, Patient, Interaction

Discover related content
Find related publications, people, projects and more using interactive charts.

View graph of relations

Related by author

  1. A more principled use of the p-value? Not so fast: a critique of Colquhoun's argument

    Arandelović, O., 15 May 2019, In : Royal Society Open Science. 6, 5, 5 p., 181519.

    Research output: Contribution to journalArticle

  2. Colorectal cancer outcome prediction from H&E whole slide images using machine learning and automatically inferred phenotype profiles

    Yue, X., Dimitriou, N., Caie, P., Harrison, D. & Arandjelovic, O., 18 Mar 2019, Proceedings of 11th International Conference on Bioinformatics and Computational Biology, BICOB 2019: Honolulu; United States; 18 March 2019 through 20 March 2019. Eulenstein, O., Al-Mubaid, H. & Ding, Q. (eds.). EasyChair, p. 139-127 11 p. (EPiC Series in Computing; vol. 60).

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

  3. Highly accurate and fully automatic 3D head pose estimation and eye gaze estimation using RGB-D sensors and 3D morphable models

    Ghiass, R. S., Arandjelovic, O. & Laurendeau, D., 5 Dec 2018, In : Sensors. 18, 12, 21 p., 4280.

    Research output: Contribution to journalArticle

  4. Reimagining the central challenge of face recognition: turning a problem into an advantage

    Arandelovic, O., Nov 2018, In : Pattern Recognition. 83, p. 388-400 13 p.

    Research output: Contribution to journalArticle

Related by journal

ID: 257572909