The importance of interpretability and visualization in ML for medical applications

Many areas of science have made a sharp transition towards data-dependent methods, enabled by simultaneous advances in data acquisition and the development of networked system technologies. This is particularly clear in the life sciences, which can be seen as a perfect scenario for the use of machine learning to address problems in which more traditional data analysis approaches might struggle. But this scenario also poses some serious challenges. One of them is the lack interpretability and explainability for complex nonlinear models. In medicine and health care, not addressing such challenge might seriously limit the chances of adoption of these methods. In this summary paper, we pay specific attention to one of the ways in which interpretability and explainability can be addressed in this context: data and model visualizationPeer ReviewedPostprint (published version

Societal issues concerning the application of artificial intelligence in medicine

Medicine is becoming an increasingly data-centred discipline and, beyond classical statistical approaches, artificial intelligence (AI) and, in particular, machine learning (ML) are attracting much interest for the analysis of medical data. It has been argued that AI is experiencing a fast process of commodification. This characterization correctly reflects the current process of industrialization of AI and its reach into society. Therefore, societal issues related to the use of AI and ML should not be ignored any longer and certainly not in the medical domain. These societal issues may take many forms, but they all entail the design of models from a human-centred perspective, incorporating human-relevant requirements and constraints. In this brief paper, we discuss a number of specific issues affecting the use of AI and ML in medicine, such as fairness, privacy and anonymity, explainability and interpretability, but also some broader societal issues, such as ethics and legislation. We reckon that all of these are relevant aspects to consider in order to achieve the objective of fostering acceptance of AI- and ML-based technologies, as well as to comply with an evolving legislation concerning the impact of digital technologies on ethically and privacy sensitive matters. Our specific goal here is to reflect on how all these topics affect medical applications of AI and ML. This paper includes some of the contents of the “2nd Meeting of Science and Dialysis: Artificial Intelligence,” organized in the Bellvitge University Hospital, Barcelona, Spain.Peer ReviewedPostprint (author's final draft

Using random forests for assistance in the curation of G-protein coupled receptor databases

Background: Biology is experiencing a gradual but fast transformation from a laboratory-centred science towards a data-centred one. As such, it requires robust data engineering and the use of quantitative data analysis methods as part of database curation. This paper focuses on G protein-coupled receptors, a large and heterogeneous super-family of cell membrane proteins of interest to biology in general. One of its families, Class C, is of particular interest to pharmacology and drug design. This family is quite heterogeneous on its own, and the discrimination of its several sub-families is a challenging problem. In the absence of known crystal structure, such discrimination must rely on their primary amino acid sequences. Methods: We are interested not as much in achieving maximum sub-family discrimination accuracy using quantitative methods, but in exploring sequence misclassification behavior. Specifically, we are interested in isolating those sequences showing consistent misclassification, that is, sequences that are very often misclassified and almost always to the same wrong sub-family. Random forests are used for this analysis due to their ensemble nature, which makes them naturally suited to gauge the consistency of misclassification. This consistency is here defined through the voting scheme of their base tree classifiers. Results: Detailed consistency results for the random forest ensemble classification were obtained for all receptors and for all data transformations of their unaligned primary sequences. Shortlists of the most consistently misclassified receptors for each subfamily and transformation, as well as an overall shortlist including those cases that were consistently misclassified across transformations, were obtained. The latter should be referred to experts for further investigation as a data curation task. Conclusion: The automatic discrimination of the Class C sub-families of G protein-coupled receptors from their unaligned primary sequences shows clear limits. This study has investigated in some detail the consistency of their misclassification using random forest ensemble classifiers. Different sub-families have been shown to display very different discrimination consistency behaviors. The individual identification of consistently misclassified sequences should provide a tool for quality control to GPCR database curators.Peer ReviewedPostprint (published version

The effect of noise and sample size on an unsupervised feature selection method for manifold learning

The research on unsupervised feature selection is scarce in comparison to that for supervised models, despite the fact that this is an important issue for many clustering problems. An unsupervised feature selection method for general Finite Mixture Models was recently proposed and subsequently extended to Generative Topographic Mapping (GTM), a manifold learning constrained mixture model that provides data visualization. Some of the results of a previous partial assessment of this unsupervised feature selection method for GTM suggested that its performance may be affected by insufficient sample size and by noisy data. In this brief study, we test in some detail such limitations of the method.Postprint (published version

Artificial Intelligence and Dialysis

Contemporary medical science heavily relies on the use of technology. Part of this technology strives to im- prove examination and measurement of the human body, with some of the most impressive technical breakthroughs to be found in the development of non-invasive proce- dures. Another part focuses on the development of de- vices that support therapies for specific pathologies. An example of this is the artificial kidney, which has become the target of intensive research from many directions and generates great expectations for dialysis patients. Re- search on the artificial kidney is still incipient though, and there are many challenges that must be overcome before it will become a reality and part of clinical practice in ne- phrology. One of these non-trivial challenges concerns the safety of users of these new dialysis devices. Safety risks make effective monitoring systems mandatory

Bayesian semi non-negative matrix factorisation

Non-negative Matrix Factorisation (NMF) has become a standard method for source identification when data, sources and mixing coefficients are constrained to be positive-valued. The method has recently been extended to allow for negative-valued data and sources in the form of Semi-and Convex-NMF. In this paper, we re-elaborate Semi-NMF within a full Bayesian framework. This provides solid foundations for parameter estimation and, importantly, a principled method to address the problem of choosing the most adequate number of sources to describe the observed data. The proposed Bayesian Semi-NMF is preliminarily evaluated here in a real neuro-oncology problem.Peer ReviewedPostprint (published version

Robust cartogram visualization of outliers in manifold learning

Most real data sets contain atypical observations, often referred to as outliers. Their presence may have a negative impact in data modeling using machine learning. This is particularly the case in data density estimation approaches. Manifold learning techniques provide low-dimensional data representations, often oriented towards visualization. The visualization provided by density estimation manifold learning methods can be compromised by the presence of outliers. Recently, a cartogram-based representation of model-generated distortion was presented for nonlinear dimensionality reduction. Here, we investigate the impact of outliers on this visualization when using manifold learning techniques that behave robustly in their presence.Postprint (published version

Intelligent data analysis approaches to churn as a business problem: a survey

Globalization processes and market deregulation policies are rapidly changing the competitive environments of many economic sectors. The appearance of new competitors and technologies leads to an increase in competition and, with it, a growing preoccupation among service-providing companies with creating stronger customer bonds. In this context, anticipating the customer’s intention to abandon the provider, a phenomenon known as churn, becomes a competitive advantage. Such anticipation can be the result of the correct application of information-based knowledge extraction in the form of business analytics. In particular, the use of intelligent data analysis, or data mining, for the analysis of market surveyed information can be of great assistance to churn management. In this paper, we provide a detailed survey of recent applications of business analytics to churn, with a focus on computational intelligence methods. This is preceded by an in-depth discussion of churn within the context of customer continuity management. The survey is structured according to the stages identified as basic for the building of the predictive models of churn, as well as according to the different types of predictive methods employed and the business areas of their application.Peer ReviewedPostprint (author's final draft

Systematic analysis of primary sequence domain segments for the discrimination between class C GPCR subtypes

G-protein-coupled receptors (GPCRs) are a large and diverse super-family of eukaryotic cell membrane proteins that play an important physiological role as transmitters of extracellular signal. In this paper, we investigate Class C, a member of this super-family that has attracted much attention in pharmacology. The limited knowledge about the complete 3D crystal structure of Class C receptors makes necessary the use of their primary amino acid sequences for analytical purposes. Here, we provide a systematic analysis of distinct receptor sequence segments with regard to their ability to differentiate between seven class C GPCR subtypes according to their topological location in the extracellular, transmembrane, or intracellular domains. We build on the results from the previous research that provided preliminary evidence of the potential use of separated domains of complete class C GPCR sequences as the basis for subtype classification. The use of the extracellular N-terminus domain alone was shown to result in a minor decrease in subtype discrimination in comparison with the complete sequence, despite discarding much of the sequence information. In this paper, we describe the use of Support Vector Machine-based classification models to evaluate the subtype-discriminating capacity of the specific topological sequence segments.Peer ReviewedPostprint (author's final draft

Blood pressure assessment with differential pulse transit time and deep learning: a proof of concept

Modern clinical environments are laden with technology devices continuously gathering physiological data from patients. This is especially true in critical care environments, where life-saving decisions may have to be made on the basis of signals from monitoring devices. Hemodynamic monitoring is essential in dialysis, surgery, and in critically ill patients. For the most severe patients, blood pressure is normally assessed through a catheter, which is an invasive procedure that may result in adverse effects. Blood pressure can also be monitored noninvasively through different methods and these data can be used for the continuous assessment of pressure using machine learning methods. Previous studies have found pulse transit time to be related to blood pressure. In this short paper, we propose to study the feasibility of implementing a data-driven model based on restricted Boltzmann machine artificial neural networks, delivering a first proof of concept for the validity and viability of a method for blood pressure prediction based on these models.Peer ReviewedPostprint (author's final draft