Visual Analytics Tools for the Study of Complex
Problems in Engineering and Biomedicine

Ignacio Díaz, José María Enguita, Abel Alberto Cuadrado, Diego García, Ana González

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To cite this version:

Ignacio Díaz, José María Enguita, Abel Alberto Cuadrado, Diego García, Ana González. Visual Analytics Tools for the Study of Complex Problems in Engineering and Biomedicine. 19th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Jun 2023, León, Spain. pp.446-457, 10.1007/978-3-031-34171-7_36 . hal-05419414

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Visual Analytics Tools for the Study
of Complex Problems in Engineering
and Biomedicine

Ignacio Díaz✉, José M. Enguita✉, Abel A. Cuadrado✉, Diego García✉, and Ana González✉

Department of Electrical Engineering, University of Oviedo, Edificio Torres Quevedo, módulo 2, Campus de Gijón, Oviedo 33204, Spain
{idiaz,jmenguita,cuadradoabel,garciaperdiego,gonzalezmunana}@uniovi.es

**Abstract.** In this article, we present the main lines of an ongoing research project funded by the Spanish government. The project proposes research on visual analytics techniques for solving complex problems in engineering and biomedicine. We outline the characteristics of complex problems that make it difficult for machine learning approaches to tackle them. Next, we present the benefits of solutions that exploit the synergy between machine learning and data visualization through interactive mechanisms for solving such problems. Finally, we briefly present the approaches being worked on in this project to achieve the objectives and the results achieved so far. We hope that these ideas and approaches will serve as inspiration for other projects or applications in the field.

**Keywords:** Visual analytics · engineering applications · biomedicine

# 1 Introduction

Many problems today in the fields of biomedicine (genomics [1,26], epidemiological studies, analysis of clinical data [25], etc.) and engineering (complex systems with many sensors and data sources [2,40]) involve huge amounts of data, a large number of variables and a high complexity, with many factors influencing their behavior. Unlike classical approaches, based on first-principles models, data carry detailed and updated information and can be processed to produce models and useful knowledge. Machine learning (ML) algorithms allow the analysis of massive data, being able to surpass humans in well-defined tasks, such as the analysis of a tumor in an X-ray image or making a fault diagnosis of a bearing from vibration data. However, ML algorithms have significant limitations in many data analysis problems today. They are prone to error in the presence of minimal changes in context or the definition of the problem. Also, they are often

<footnote>This work was supported by the Ministerio de Ciencia e Innovación / Agencia Estatal de Investigación (MCIN/AEI/ 10.13039/501100011033) grant [PID2020-115401GB-I00].</footnote>
“black box” models, making their integration with related domain knowledge dif-
ficult and being, in consequence, less reliable [42]. However, humans, although
less precise, can work with poorly posed problems, perform well on a wide range
of tasks, and are able to find connections and improve responses through an
iterative and exploratory process. This suggests a “third way”, based on the
efficient use of interaction mechanisms between humans and ML algorithms to
create hybrid “ML-human” problem-solving processes that can potentially allow
a great improvement over “human” or “ML” only approaches.

Within this context, we need data analysis tools that allow a quick, effective
and intuitive analysis about the behavior of the system under study (e.g., a
disease, an epidemic, or an industrial process) in different scenarios, such as
different medical treatments, clinical conditions, patient groups, or for different
process parameters or setpoint values in the case of an industrial process, as well
as to support decisions and the development of protocols. The tools developed
must be easily reproducible, able to be implemented in a short time, applicable
to new scenarios, and must be able to carry out a multilevel analysis which
allows to move smoothly from a “big picture” of the problem, towards the detail
and the obtaining of quantitative information. Finally, they should allow to be
powered by ML algorithms, which can reveal hidden patterns in the data and
be able to integrate that information in a visual and intuitive way.

1.1 Hypotheses

*Complex Nature of Today's Problems.* Complex problems arising in the analysis of biomedical data, including gene expression and clinical or epidemiological data, as well as in engineering fields such as industrial process supervision and energy efficiency management, are highly multifactorial and multidomain in nature, for which commonly we do not have a comprehensive understanding. This makes them problems that most often cannot be posed in terms of canned operations achievable by machine learning algorithms or other automated approaches alone, since most of the times a precise formulation is not available. The question, then, is how to pose them as closed problems since, once this is achieved, we can deploy powerful numerical methods to get precise answers.

*Need of Human-ML Hybrid Approaches for Today's Challenges.* As the complexity, size and dimensionality grows, the problem definition becomes a nontrivial task, requiring the combination of the user's expert knowledge with data analytics methods. Despite the fact that ML algorithms allow analysis of massive data, outperforming humans in well-defined tasks, they are prone to failure under changes in the context or the problem statement. At the same time, humans can perform well under ill-posed problems and in a vast range of tasks, find connections and improve answers by means of an iterative and exploratory cognitive process. So, data visualization and well-designed interaction mechanisms between humans and ML algorithms leading to an “ML-human” hybrid problem-solving process can, in consequence, result in a vast improvement over “human” or “ML” approaches alone.
# 2 Methods and Techniques

## 2.1 Main Architectural Approach

We propose to apply the above idea, centered on the user, emphasizing the iterative and exploratory character, innate to human learning, and exploiting the efficiency of the visual and motor system in pattern analysis. The solutions and approaches we will work on will be based on the search for synergies between data visualization techniques, ML algorithms and interaction mechanisms, allowing the user to steer the analysis process and formulate new questions based on the answers he gets on the way.

Inspired by [39], the workflow of the visual cognition process aimed at in our proposal which takes place during a problem analysis task is described in Fig 1.

<figure><img src="image_4.png" /><figcaption>Fig. 1. Analysis workflow</figcaption></figure>

During the analysis, the increment $\frac{dK}{dt}$ of the user's knowledge depends both on the visual information *I* that he perceives through the perception *P*, and the user's current knowledge *K* itself, which modulates the perception, that is, $\frac{dK}{dt} = P(I, K)$. Based on her knowledge, the user modifies the view through interactive exploration, *E*, which involves interaction (zoom, selection or pan), or more advanced methods, such as the reconfiguration of ML algorithms steering parameters *p*₁, *p*₂, ..., *p*ₙ. Note that the interaction takes place at two levels, visualization and ML, implying that the user has control on what he sees but also on the formulated questions and their answers provided by ML. We hypothesize that particularly this fact is key to speed up the quest for a proper problem formulation under weakly posed scenarios.
## 2.2 Interactive Data Cubes

Interactive data cubes (iHistograms) [9] allow the user to analyze datasets with several attributes (columns), organizing samples (rows) internally as a hyper-cube, where each side is an attribute and each cell contains samples with a combination of attribute values. By defining filters for a set of attributes, the histogram of the remaining samples, conditioned to the filtering operation, is shown in real time, allowing the user to explore different scenarios interactively. Aggregated values can also be represented, such as the mean or the sum, which allow the generation of statistics of the type “average of deaths by age for COVID-19 patients treated with dexamethasone”, and instantly, “on the fly”, modify the filters (e.g., change dexamethasone by remdesivir, or consider both drugs administered simultaneously) through drag and drop gestures. Two working demos and videos can be checked at http://isa.uniovi.es/GSDPI/dpi2015.html.

## 2.3 Morphing Projections

Morphing Projections (MP) [11,13], are based on the use of continuous transitions (*morphing* operations) between several basis views, each of which represents items (e.g., patients) spatially organized according to different criteria, such as age, sex, value of a clinical variable, or administered dose of a drug. Transitions let the user keep a mental model of the data between views, which allows different aspects of the data to be connected. In addition, it makes possible to define new views by “mixing” two or more basis views, resulting in a spatial rearrangement of the data according to a mixed criterion (e.g., men-old, women-young...). The combination of morphing and other interaction elements such as zoom, detail and multiple selection results in an immersive interface that enables the user to link and combine information in unprecedented ways. Videos and a playable demo app can be checked at http://isa.uniovi.es/GSDPI/morphingProjections.html.

## 2.4 Dimensionality Reduction Techniques

Dimensionality reduction (DR) techniques consist in obtaining a mapping between a large number of variables that describe the state of a system and a small set of “latent” variables or factors that explain the possible modes of variation of the system’s states. In biomedical applications, latent factors could summarize the evolution of a disease from numerous clinical data (sensors, lab values, medication, etc.), opening ways to determine biomarkers, health indicators, and treatments, as well as elaborating a taxonomy of variants of this disease using the factors to generate insightful data visualizations. In industrial engineering problems the factors could summarize the process states from multiple sensor measurements, allowing, for instance, to build visual maps of its operating conditions useful to improve process efficiency or to build indices showing degradation. This idea can be taken further by integrating latent factors into visual analytics tools (morphing projections and interactive data cubes),
making it possible, for instance, to represent maps of the patients' conditions on a disease and allowing exploratory analysis in combination with other data such as treatments or other relevant information. Interactive exploration could also help in finding combinations of factors that allow to determine potential biomarkers of medically relevant properties about the patient's condition that serve as a basis for generating diagnostic or medication protocols. In engineering processes, the latent variables could allow to develop virtual sensors to estimate non-measured variables or more abstract quantities such as efficiency or quality. These are unexplored topics that will be subject of research in this project.

# 3 Proposal

Our proposal will seek for approaches that exploit the synergies between the three techniques mentioned above and novel ML-based methods, resulting in visual analytics models pursuing the following abilities: 1) the interpretability of a complex data set from the ML output; 2) interactively exploring latent spaces obtained from modern DR techniques; and 3) novelty detection.

## 3.1 Interpretability from Machine Learning

An important weakness of ML methods is their black-box nature. ML algorithms, in their basic philosophy, learn a model from input/output data sets and later, in the inference phase, take new inputs to predict their outcomes (e.g., a classification result or an estimation). This inference is done without any user intervention, and no explanation is provided. This produces a lack of confidence in the results, which is a major drawback, for instance, in medicine as a paradigmatic example, but also in any industrial area that involves economical or personal consequences. Explainable ML — and the closed topic of interpretable ML — [24, 35, 37] has arisen in last years as a branch that looks for designing ML algorithms having the user in mind, providing her with complementary information that “explains” or suggests the pathway taken for the inference results and also providing ways for the user to “tune” the ML behavior according to interpretable parameters. We outline below three ways —see Fig. 2—that will inspire our research in this topic:

- *Visualization of the ML output.* The most naive (yet effective) way to get meaning from ML is probably to visualize its outcome for a number of samples. Its effectiveness can be potentiated with a good visualization design, using a meaningful layout of the samples (e.g., according to a context variable, such as sex, age, or a similarity-based arrangement using DR) and proper channels to convey the information of the ML output (e.g., color scales, size, etc.).

- *Introspection in ML models*, that is, “reverse engineering” the model by getting out information stored in its parameters and transforming it into a human readable format by means of visualization or natural text generation. This
<figure><img src="image_5.png" /><figcaption>**Fig. 2.** Different approaches for interpretability in ML. V=visualization, x = input, y = output, w = internal model weights, c = conditioning parameters.</figcaption></figure>

approach is not new. An example are SOM component planes ( [29] and many
other later related works). A recent work about visualizing deep convolutional
networks has been presented by the researchers of this group [22]. The pos-
sibilities of this approach in industrial processes and biomedical applications
are unexplored as far as we know and will be object of research in this project.
– *Steering ML models*. In the typical workflow of DR visualization, interac-
tion often takes place *after* DR computation. The user typically sets an
initial configuration, runs it until convergence and, after *N* iterations, the
results are visualized. The user can later reconfigure this visualization or run
the DR algorithm again with a different parameterization. However, inter-
action can go far beyond if we allow the user to take complete control of
the DR algorithm during convergence and visualize the intermediate projec-
tions, resulting in interactive dimensionality reduction, iDR [10] [16], which
suits the concept outlined in Fig. 1. Using DR algorithms based on itera-
tive approximation, such as the SNE (stochastic neighbor embedding), the
visualization of intermediate projections produces a smoothly varying layout
that reveals the changes in the relationships between the samples in terms of
the chosen parameters (e.g., redefining the weights of the variables used in
computing the similarities). The result is a visualization that changes dynam-
ically - an animated transition- that allows the user to track the changes in
the projection that results from changes in the formulation of the problem,
such as changes in the input data samples, or in the weights of the input
attributes. Working demos of this approach, developed in a former Spanish
research-funded project (DPI2015-69891-C2-2-R grant), can be seen in iDR
visualization: map of vibration states and other demos in the project page
http://isa.uniovi.es/GSDPI/dpi2015.html.

## 3.2 Explanatory Power of Latent Spaces

Many high-dimensional datasets, such as images of human faces, gene expres-
sion patterns, or power demand profiles, can be approximately explained by a
reduced number of latent factors by means of DR methods. Latent spaces sum-
marize the modes of data variability and can provide human-tractable ways for visualization and interaction of high-dimensional data. As suggested in [3] certain directions in the latent spaces, called *attribute vectors*, can be associated to meaningful abstract concepts (such as a “smiling” or a “gender” direction in a map of faces). Using manifold learning methods to obtain continuous latent spaces (*t*-SNE [31], UMAP [32], deep autoencoders [23], etc.) this idea can be transferred to the genomics data analysis domain to discover explanatory directions with biomedical relevance [28,30]. For instance, looking for directions revealing the stages of tumors could reveal what genes are involved in the cancer progression. Interestingly, we expect to find parallelisms of this idea in industrial process analysis to design health indicators or understand the causes of process degradation by finding directions of faults in the latent space.

## 3.3 Novelty Detection

Anomaly detection consists in finding those patterns in data that do not conform to expected normal behavior. Such patterns, commonly known as anomalies or outliers, represent deviations from the normal behavior, so their detection is not only of great value, but is often critical in a wide variety of applications, such as intrusion detection, fraud detection, medical anomaly detection or industrial damage detection [5]. In the context of industrial processes, anomaly detection is a topic of great importance, since both detection and diagnosis of faults are crucial to optimize and guarantee safety in the operation of machines, leading to higher productivity and process efficiency, with benefits such as reduced operating costs, longer operating life or improved process uptime [34]. In biomedical data analysis, novelty scores have been used to develop test of pluripotency in human cells [33]. In [38] novelty detection was used to perform high-content screening, allowing the discovery of rare phenotypes without user training. In [41], the authors propose a confidence-aware anomaly detection (CAAD) model able to achieve a rapid and accurate detection of viral pneumonia (reporting also radiologist-level results in COVID-19 cases) from chest X-rays, combining deep learning (DL) feature extraction stage, anomaly detection based on one-class SVM, and a confidence prediction score.

A common approach in the literature is to detect anomalies by means of residual analysis (instances with large residual errors are more likely to be anomalies) [36], using dimensionality reduction techniques to generate them, such as SOM, which has been studied by our research group in former projects and published in several papers [6–8, 18, 21]. In recent years, new techniques have emerged, but their potential has yet to be explored. This is the case of the *variational autoencoder* (VAE), which has become one of the most successful unsupervised learning algorithms, showing promising results in encoding and reconstructing data in the field of computer vision [27]. Also, regression models aiming to replicate the system dynamics (e.g., virtual sensors providing a redundant analytic estimation of a real sensor measurement) can be used for this purpose as a source of analytical redundancy.
# 4 Methodology

The planned project work packages (WPx) to achieve the objectives are summarized as follows:

**WP1 Interaction and Data Visualization.** This task aims at integrating the principles of MP and interactive data cubes, carrying out research on potential ways to extend the idea with the ML techniques studied in the other tasks. We propose to extend the MP approach described in [13] with full **Filter**, **Group**-by and **Aggregation** (FGA) operations found in the interactive data cubes presented in [9]. Attention will be given to the individual possibilities of these operations. For example, filters can be applied to a MP view using a 2D lasso and grouping can be done using ML clustering algorithms. Aggregation operations involve computing a summary value from the elements of a group and basic aggregations include count, average, sum, and max. However, ML algorithms can be used to compute more elaborate outputs such as classification, novelty or saliency of items within a group, or making a prediction or prognosis.

**WP2 Dimensionality Reduction Approaches.** This task will aim to evaluate potential advantages of recent developments in DR, with a special focus on the explanatory power of latent variables and their efficient integration within interactive data visualization tools to contribute to problem understanding. It will focus on novel methods (e.g. deep autoencoders, manifold learning algorithms), exploring their ability to disentangle complex data; interpretability by studying the explanatory power of latent variables; and in the possibilities of integration in interactive data visualization interfaces.

**WP3 Novelty Detection.** This task will involve research in ML methods to detect salient or abnormal data suitable for engineering and biomedical data analysis, and how they can be integrated in interactive data visualization approaches to boost discovery of relevant knowledge. We will research on methods based on the principle of *analytical redundancy*, building first a model of the data, and then comparing the actual sample with its reconstruction by the model, resulting in *residuals*, which convey information highly related to the degree of novelty or saliency of the data. The visualization of residuals within an interactive tool, allowing the user to confront them with other problem attributes providing context information, is expected to be a powerful way to generate valuable knowledge.

**WP4 Other ML Approaches.** This task will involve research in ways of integrating other ML methods (mainly regression and classification algorithms) in the visual analytics tools to improve interpretation and quantitative assessment of data.

**WP5 Applications.** Finally, this task will evaluate, by means of data analysis, case studies and demo applications, the suitability and potential of the methods and techniques proposed in the project on real problems from the process engineering and biomedical domains.
## 4.1 Project Achievements up to Date

*Development of Health Indicators* (WP3, WP5). A novel method harnessing the disentangling capability of variational autoencoders (VAE) was proposed in [19] to generate effective health indicators. The core idea implies using the latent reconstruction error, which is the error in reconstructing data from the latent space of an autoencoder, as a health indicator. The approach was tested on three different datasets and was compared to other state-of-the-art approaches. It was found that the proposed approach outperformed conventional approaches in terms of quality metrics, and that it is suitable for VA-based monitoring of the condition of machinery.

*Anomaly Detection and Componentwise Characterization* (WP3, WP5). A variational autoencoder (VAE) and a classifier were used in [20] to detect anomalies in engineering systems at two levels: a global one, that indicates the nature (normal/faulty) of the samples, and a detailed analysis showing anomalies of individual components in each sample. The ability to visualize anomalies for individual process features (components) provides an explainable diagnostic of the anomaly decision.

*Visualization of Time Series Using ESN (WP2, WP4, WP5).* *Echo State Networks* (ESN) are used in [14] to characterize time series of processes using an autoregressive model of the reservoir states, and proposes a visualization method based on a principal component analysis (PCA) projection of the regression matrix, that can be used for the exploratory analysis of processes with dynamics as well as for monitoring their condition.

*Morphing Projections for Cancer Genomics Research* (WP1, WP5). Visual analytics techniques helped also in gene expression analysis as part of a study of hypoxia mechanisms in certain cancer types [4]. Several t-SNE projections of cancer samples using different sets of genes were used to visualize the relative positions in a similarity map of samples with three different mutations of pheochromocytoma-paraganglioma. Additionally, a sorted visualization of a gene expression matrix (samples, genes) was produced by using 1D-DR projections, which facilitated the visual identification of relevant patterns.

*Dual Interactive Visualization Gene Expression Data* (WP2, WP5). We presented two applications of interactive DR for exploratory analysis of gene expression data. One of them produces two lively-updated projections, a sample map and a gene map, by rendering intermediate results of a t-SNE, [15]. The other combines the sample and gene view with a heatmap visualization of the gene expression map, [12]. In both applications the user can condition the projections “on the fly” by subsets of genes or samples, so updated views reveal co-expression patterns for different cancer types or gene groups.
*Visual Analytics of COVID-19 Clinical Data During the First Wave in Spain* (WP1, WP5). We presented in [17] a visual analytics approach that uses the morphing projections technique to combine the visualization of a t-SNE projection of clinical time series, with views of other clinical or patient's information. The proposed approach is demonstrated on an application case study of COVID-19 clinical information taken during the first wave.

*Interactive Visualization of Cell Movement in Cancer Samples* (WP2). A 2D map has been used as a tool for the analysis of cellular motility in cancer processes, using a deep autoencoder preceded by a manual feature extraction stage based on the extraction of the velocity field and the *Histogram of Oriented Optical Flow* (HOOF) of the working videos. The exploration of the obtained map has allowed to identify different patterns of movement in the cells, providing therefore a preliminary evaluation of the working samples, which represents a valuable starting point for further analysis. Videos showing the proposed approach can be found in the project page http://isa.uniovi.es/GSDPI/pid2020-es.html.

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