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Deep learning models are receiving increasing attention in clinical decision-making, however the lack of interpretability and explainability impedes their deployment in day-to-day clinical practice. We propose REM, an interpretable and explainable methodology for extracting rules from deep neural networks and combining them with other data-driven and knowledge-driven rules. This allows integrating ma- chine learning and reasoning for investigating applied and basic biological research questions. We evaluate the utility of REM on the predictive tasks of classifying histological and immunohistochemical breast cancer subtypes from genotype and phenotype data. We demonstrate that REM efficiently extracts accurate, compre- hensible and, biologically relevant rulesets from deep neural networks that can be readily integrated with rulesets obtained from tree-based approaches. REM provides explanation facilities for predictions and enables the clinicians to vali- date and calibrate the extracted rulesets with their domain knowledge. With these functionalities, REM caters for a novel and direct human-in-the-loop approach in clinical decision making.

Artificial intelligence (AI) has boundless potential in healthcare. In this chapter, we consider how to facilitate interactions between healthcare practitioners and AI systems. We discuss how AI can extend a practitioner’s expertise by leveraging the predictive power of AI systems. When AI systems are aligned with human values, practitioners can place appropriate trust in AI systems. Current applications of AI systems in clinical settings are limited to model-driven systems, some of which explicitly encode practitioner preferences as rules. We explore how AI systems can scrutinize datasets for insights, find new drugs or treatments, or provide clinical decision support to practitioners. Moreover, AI systems can show their trustworthiness to practitioners, by displaying predictability, procedural transparency, algorithmic transparency, and robustness. Practitioners are essential to the successful deployment of AI systems. The merit of AI in healthcare will come from AI deployed responsibly with practitioners in mind.

Emotion regulation is among the most important skills that children and adolescents need to be successful in their social lives. Individuals with autism spectrum disorder have serious deficit in regulating their emotions. Various treatments have been used to improve emotion regulation in children with autism including using video games designed to improve emotion regulation, has attracted many researchers, treating autism or other groups of children with special needs. These games can be used with or without a therapist presence which would save money and energy. This study aimed to evaluate the effects of EmoGalaxy, a video game designed to improve emotion regulation of children with autism spectrum disorder, compared to a card games with emotion regulation approach. Effectiveness of both interventions were evaluated by a quasi-experimental research with pretest-posttest and a control group. 10 students in the first experimental group played with EmoGalaxy, while the second experimental group, with 10 students, played regular cards games. The control group, consisted of 10 students did not receive any treatment and waited to receive the intervention after the experimental group intervention period. The instrument used in this study to evaluate the emotion regulation of the participants was the emotion regulation checklist (Shields & Cicchetti, 2004). The obtained data was analyzed by Multiple Analysis of the Covariance (MANCOVA). The results of the study showed that computer cognitive games led to significant changes compared to the other experimental group and the control group in emotion regulation (p

In a normative environment, an agent’s actions are directed not only by its goals but also by the norms activated by its actions and those of other actors. The potential for conflict between agent goals and norms makes decision making challenging, in that it requires looking ahead to consider the longer-term consequences of which goal to satisfy or which norm to comply with in face of conflict. We therefore seek to determine the actions an agent should select at each point in time, taking account of its temporal goals, norms, and their conflicts. We propose a solution in which a normative planning problem is the basis for practical reasoning based on argumentation. Various types of conflict within goals, within norms, and between goals and norms are identified based on temporal properties of these entities. The properties of the best plan(s) with respect to goal achievement and norm compliance are mapped to arguments, followed by mapping their conflicts to attack between arguments, all of which are used to identify why a plan is justified.

International initiatives such as the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC), Cancer Genome Atlas (TCGA), and the International Cancer Genome Consortium (ICGC) are collecting multiple data sets at different genome-scales with the aim to identify novel cancer bio-markers and predict patient survival. To analyse such data, several machine learning, bioinformatics and statistical methods have been applied, among them neural networks such as autoencoders. Although these models provide a good statistical learning framework to analyse multi-omic and/or clinical data, there is a distinct lack of work on how to integrate diverse patient data and identify the optimal design best suited to the available data. In this paper, we investigate several autoencoder architectures that integrate a variety of cancer patient data types (e.g., multi-omics and clinical data). We perform extensive analyses of these approaches and provide a clear methodological and computational framework for designing systems that enable clinicians to investigate cancer traits and translate the results into clinical applications. We demonstrate how these networks can be designed, built and, in particular, applied to tasks of integrative analyses of heterogeneous breast cancer data. The results show that these approaches yield relevant data representations that, in turn, lead to accurate and stable diagnosis.

This study deals with the application of the sequential function specification method for estimating the unknown time-dependent base temperature in a porous fin based on the temperature measurement...

The aim of this study is to investigate the effects of nanoparticles type, nanoparticles size, and droplet size on the ignition probability of the diesel fuel. Also, the effect of metal oxide nanoparticles on the ignition delay of diesel fuel droplets is studied. A series of hot plate ignition tests are conducted for diesel fuel with and without Al 2 O 3 , TiO 2 and Fe 3 O 4 nanoparticles at different concentrations (up to 0.05 wt.%). The experimental results show that the ignition probability of the diesel fuel significantly increases in the presence of metal oxide nanoparticles. The ignition probabilities of diesel/Al 2 O 3 and diesel/Fe 3 O 4 nanofuels are almost similar and higher than that of diesel/TiO 2 nanofuel. Results for diesel/Al 2 O 3 show that the nanoparticle size does not affect the ignition probability of nanofuel. Moreover, results indicate that the ignition probability decreases by increasing the droplet size. Investigations show that the ignition delay for diesel fuel droplets decreases in the presence of Al 2 O 3 nanoparticles.

Autonomous software agents operating in dynamic environments need toconstantly reason about actions in pursuit of their goals, while taking intoconsideration norms which might be imposed on those actions. Normativepractical reasoning supports agents making decisions about what is best forthem to (not) do in a given situation. What makes practical reasoning chal-lenging is the interplay between goals that agents are pursuing and the normsthat the agents are trying to uphold. We offer a formalisation to allow agentsto plan for multiple goals and norms in the presence of durative actions thatcan be executed concurrently. We compare plans based on decision-theoreticnotions (i.e. utility) such that the utility gain of goals and utility loss of normviolations are the basis for this comparison. The set of optimal plans consistsof plans that maximise the overall utility, each of which can be chosen by theagent to execute. We provide an implementation of our proposal in AnswerSet Programming, thus allowing us to state the original problem in terms ofa logic program that can be queried for solutions with specific properties.

Research in psychology about reasoning has often been restricted to relatively inexpressive statements involving quantifiers (e.g. syllogisms). This is limited to situations that typically do not arise in practical settings, like ontology engineering. In order to provide an analysis of inference, we focus on reasoning tasks presented in external graphic representations where statements correspond to those involving multiple quantifiers and unary and binary relations. Our experiment measured participants’ performance when reasoning with two notations. The first notation used topological constraints to convey information via node-link diagrams (i.e. graphs). The second used topological and spatial constraints to convey information (Euler diagrams with additional graph-like syntax). We found that topo-spatial representations were more effective for inferences than topological representations alone. Reasoning with statements involving multiple quantifiers was harder than reasoning with single quantifiers in topological representations, but not in topo-spatial representations. These findings are compared to those in sentential reasoning tasks.

Ontologies are notoriously hard to define, express and reason about. Many tools have been developed to ease the debugging and the reasoning process with ontologies, however they often lack accessibility and formalisation. A visual representation language, concept diagrams, was developed for expressing and reasoning about ontologies in an accessible way. Indeed, empirical studies show that concept diagrams are cognitively more accessible to users in ontology debugging tasks. In this paper we answer the question of “ How can concept diagrams be used to reason about inconsistencies and incoherence of ontologies?”. We do so by formalising a set of inference rules for concept diagrams that enables stepwise verification of the inconsistency and/or incoherence of a set of ontology axioms. The design of inference rules is driven by empirical evidence that concise (merged) diagrams are easier to comprehend for users than a set of lower level diagrams that offer a one-to-one translation of OWL ontology axioms into concept diagrams. We prove that our inference rules are sound, and exemplify how they can be used to reason about inconsistencies and incoherence. Finally, we indicate how our rules can serve as a foundation for new rules required when representing ontologies in diverse new domains.

© Springer International Publishing AG 2017. Ontologies are notoriously hard to define, express and reason about. Many tools have been developed to ease the ontology debugging and reasoning, however they often lack accessibility and formalisation. A visual representation language, concept diagrams, was developed for expressing ontologies, which has been empirically proven to be cognitively more accessible to ontology users. In this paper we answer the question of “How can concept diagrams be used to reason about inconsistencies and incoherence of ontologies?". We do so by formalising a set of inference rules for concept diagrams that enables stepwise verification of the inconsistency and incoherence of a set of ontology axioms. The design of inference rules is driven by empirical evidence that concise (merged) diagrams are easier to comprehend for users than a set of lower level diagrams that are a one-to-one translation from OWL ontology axioms. We prove that our inference rules are sound, and exemplify how they can be used to reason about inconsistencies and incoherence.