Affective Medical Estimation and Decision Making via Visualized Learning and Deep Learning
This addresses the challenge of making ML models more interpretable and useful for clinicians and patients in medical settings, though it appears incremental as it builds on existing visualization and ML techniques.
The paper tackles the problem of improving medical decision-making by introducing Visualized Learning for Machine Learning (VL4ML), a first-of-its-kind approach that uses visualization to assist physicians in understanding and communicating ML-based estimations, including uncertainty, with experiments across five case studies and a survey of over 100 users demonstrating its practical benefits.
With the advent of sophisticated machine learning (ML) techniques and the promising results they yield, especially in medical applications, where they have been investigated for different tasks to enhance the decision-making process. Since visualization is such an effective tool for human comprehension, memorization, and judgment, we have presented a first-of-its-kind estimation approach we refer to as Visualized Learning for Machine Learning (VL4ML) that not only can serve to assist physicians and clinicians in making reasoned medical decisions, but it also allows to appreciate the uncertainty visualization, which could raise incertitude in making the appropriate classification or prediction. For the proof of concept, and to demonstrate the generalized nature of this visualized estimation approach, five different case studies are examined for different types of tasks including classification, regression, and longitudinal prediction. A survey analysis with more than 100 individuals is also conducted to assess users' feedback on this visualized estimation method. The experiments and the survey demonstrate the practical merits of the VL4ML that include: (1) appreciating visually clinical/medical estimations; (2) getting closer to the patients' preferences; (3) improving doctor-patient communication, and (4) visualizing the uncertainty introduced through the black box effect of the deployed ML algorithm. All the source codes are shared via a GitHub repository.