Cooperation Between Data Analysts and Medical Experts: A Case Study

Rokošná, Judita; Babič, František; Majnarić, Ljiljana Trtica; Pusztová, Ľudmila

doi:10.1007/978-3-030-57321-8_10

Cited by 5 publications

(3 citation statements)

References 24 publications

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“…From the perspective of our own experience, we can state that close collaboration and a good mutual understanding between the medical expert and the data analyst is necessary throughout the entire process of data analysis, from data collection (selection), methods selection to the interpretation of the results, and in any case of problem-solving tasks [ 73 ]. We can also state with confidence that there is a need for using several analytical methods in most tasks, where data visualization techniques can substantially improve understanding of the results.…”

Section: The Machine Learning/big Data Approaches and Challenges Imentioning

confidence: 99%

AI and Big Data in Healthcare: Towards a More Comprehensive Research Framework for Multimorbidity

Majnarić

Babič

O’Sullivan

et al. 2021

JCM

Self Cite

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Multimorbidity refers to the coexistence of two or more chronic diseases in one person. Therefore, patients with multimorbidity have multiple and special care needs. However, in practice it is difficult to meet these needs because the organizational processes of current healthcare systems tend to be tailored to a single disease. To improve clinical decision making and patient care in multimorbidity, a radical change in the problem-solving approach to medical research and treatment is needed. In addition to the traditional reductionist approach, we propose interactive research supported by artificial intelligence (AI) and advanced big data analytics. Such research approach, when applied to data routinely collected in healthcare settings, provides an integrated platform for research tasks related to multimorbidity. This may include, for example, prediction, correlation, and classification problems based on multiple interaction factors. However, to realize the idea of this paradigm shift in multimorbidity research, the optimization, standardization, and most importantly, the integration of electronic health data into a common national and international research infrastructure is needed. Ultimately, there is a need for the integration and implementation of efficient AI approaches, particularly deep learning, into clinical routine directly within the workflows of the medical professionals.

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Section: The Machine Learning/big Data Approaches and Challenges Imentioning

confidence: 99%

AI and Big Data in Healthcare: Towards a More Comprehensive Research Framework for Multimorbidity

Majnarić

Babič

O’Sullivan

et al. 2021

JCM

Self Cite

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“…The game's limited complexity and ease of understanding make it a perfect playground for explainable AI by a developer. It does not require domain-specific knowledge like real-world applications like medicine (see [Rokosna et al, 2020], for instance). The limited search space of that game, especially in comparison with Go, would make this game more suitable for an AI-based on the Alpha-Beta family [Schaeffer, 1989].…”

Section: Introduction and Related Workmentioning

confidence: 99%

Learning Machine Learning with a Game

Lürig

2022

ECGBL

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AIs playing strategic games have always fascinated humans. Specifically, the reinforcement learning technique Alpha Zero (D.Silver, 2016) has gained much attention for its capability to play Go, which was hard to crack problem for AI for a long time. Additionally, we see the rise of explainable AI (xAI), which tries to address the problem that many modern AI decision techniques are black-box approaches and incomprehensible to humans. Combining a board game AI for the relatively simple game Connect-Four with explanation techniques offers the possibility of learning something about an AI's inner workings and the game itself. This paper explains how to combine an Alpha-Zero-based AI with known explanation techniques used in supervised learning. Additionally, we combine this with known visualization approaches for trees. Alpha-Zero combines a neuronal network and a Monte-Carlo-Search-Tree. The approach we present in this paper focuses on two explanations. The first explanation is a dynamic analysis of the evolving situation, primarily based on the tree aspect, and works with a radial tree representation (Yee et al., 2001). The second explanation is a static analysis that tries to identify the relevant situation elements using the Lime (Local Interpretable Model Agnostic Explanations) approach (Christoforos Anagnostopoulos, 2020). This technique focuses primarily on the neuronal network aspect. The straightforward application of Lime towards the Monte-Carlo-Search-Tree approach would be too compute-intensive for interactive applications. We suggest a modification to accommodate search trees and sacrifice the model agnosticism specifically. We use a weighted Lasso-based approach on the different board constellations analyzed in the search tree by the neuronal network to get a final static explanation of the situation. Finally, we visually interpret the resulting linear weights from the Lasso analysis on the game board. The implementation is done in Python using the PyGame library for visualization and interaction implementation. We implemented the neuronal networks with PyTorch and the Lasso analysis with Scikit Learn. This paper provides implementation details on an experimental approach to learning something about a game and how machines learn to play a game.

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“…The complex models must be used to solve the problems, but they are difficult for medical experts to interpret [6]. It may require intensive crosstalk between a data analyst and a medical expert to achieve a two-way understanding of the task [7]. Generated decision models and visualizations must be modified to be easier to read and interpret by users.…”

Section: Introductionmentioning

confidence: 99%

Decision Support System Improving the Interpretability of Generated Tree-Based Models

Klimonová,

Anderková,

Babič

et al. 2022

Acta Electrotechnica Et Informatica

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A decision tree represents one of the most used data analysis methods for classification tasks. The generated decision models can be visualized as a graph, but this visualization is quite complicated for a domain expert to understand in large or heterogeneous data. Our previous experience with medical data analytics related to the classification of patients with Metabolic Syndrome, Mild Cognitive Impairment, heart disease, or Frailty motivated us to evaluate the potential of new visualizations for this decision model in the medical domain. We managed a user study to design and implement a decision support system containing selected methods to improve the interpretability of the generated tree-based decision model. We hypothesized that this approach would result in more effective communication between data analysts and medical experts, reduce necessary time and energy and bring more comprehensive results. For this purpose, we selected two model-agnostic methods, LIME and SHAP, and one new interactive visualization called Sunburst. We used two data samples for design and evaluation: the publicly available heart disease dataset and the Metabolic Syndrome dataset the participating medical expert provided. We will use the collected feedback and experience for further improvements, like more evaluation metrics related to the usability of the decision models.

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Cooperation Between Data Analysts and Medical Experts: A Case Study

Cited by 5 publications

References 24 publications

AI and Big Data in Healthcare: Towards a More Comprehensive Research Framework for Multimorbidity

AI and Big Data in Healthcare: Towards a More Comprehensive Research Framework for Multimorbidity

Learning Machine Learning with a Game

Decision Support System Improving the Interpretability of Generated Tree-Based Models

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