Adopting cognitive computing solutions in healthcare

This paper discusses possible motivations to adopt cognitive computing-based solutions in the field of healthcare and surveys some recent experiences. From a very practical point of view, the use of cognitive computing techniques can provide machines with human-like reasoning capabilities, thus allowing them to face heavy uncertainties and to cope with problems whose solution may require computing intensive tasks. Moreover, empowered by reliable networking infrastructures and cloud environments, cognitive computing enables effective machine-learning techniques, resulting in the ability to find solutions on the basis of past experience, taking advantage from both errors and successful findings. Owing to these special features, it is perceptible that healthcare can greatly benefit from such a powerful technology. In fact, clinical diagnoses are frequently based on statistics and significant research advancements were accomplished through the recursive analysis of huge quantity of unstructured data such as in the case of X-ray images or computerized axial tomography scans. As another example, let us consider the problem of DNA sequence classification with the uncountable combinations that derive from such a complex structure

Focus on: New trends, challenges and perspectives on healthcare cognitive computing: from information extraction to healthcare analytics

The focus of this special issue is cognitive computing in healthcare, due to the ever-increasing interest it is gaining for both research purposes and clinical applications. Indeed, cognitive computing is a challenging technology in many fields of application (Banavar, 2016) such as, e.g., medicine, education or eco- nomics (Coccoli et al., 2016) especially for the management of huge quantities of information where cognitive computing techniques push applications based on the use of big data (Coccoli et al., 2017). An unprecedented amount of data is made available from a heterogeneous variety of sources and this is true also in the case of health data, which can be exploited in many ways by means of sophisticated cognitive computing solutions and related technologies, such as, e.g., information extraction, natural language processing, and analytics. Also, from the point of view of programming they set challenging issues (see, e.g., Coccoli et al., 2015). In fact, the amount of healthcare that is now available and, potentially useful to care teams, reached 150 Exabytes worldwide and about 80% of this huge volume of data is in an unstructured form, being thus somehow invisible to systems. Hence, it is clear that cognitive computing and data analytics are the two key factors we have for make use – at least partially – of such a big volume of data. This can lead to personalized health solutions and healthcare systems that are more reliable, effective and efficient also re- ducing their expenditures. Healthcare will have a big impact on industry and research. However, this field, which seems to be a new era for our society, requires many scientific endeavours. Just to name a few, you need to create a hybrid and secure cloud to guarantee the security and confidentiality of health data, especially when smartphones or similar devices are used with specific app (see, e.g., Mazurczyk & Caviglione, 2015). Beside the cloud, you also need to consider novel ar- chitectures and data platforms that shall be different from the existing ones,because 90% of health and biomedical data are images and also because 80% of health data in the world is not available on the Web. This special issue wants to review state-of-the-art of issues and solutions of cognitive computing, focusing also on the current challenges and perspecti- ves and includes a heterogeneous collection of papers covering the following topics: information extraction in healthcare applications, semantic analysis in medicine, data analytics in healthcare, machine learning and cognitive com- puting, data architecture for healthcare, data platform for healthcare, hybrid cloud for healthcare

Big Data, Cognitive Computing and the future of learning managements Systems

Since the early years, when they started to enter the market, Learning Management Systems (LMSs) demonstrated their utility inside learning environments, contributing to the diffusion of e-learning especially in those Institutions with a low budget or no internal knowledge for developing e-learning initiatives. Today, they have reached a high maturity level, providing professional solutions to almost any educational need referring to distance learning. However, in our opinion, there are two important evolutions that should profoundly change the architecture of these pillar software tools. First, the acquisition of an enormous amount of data related to educational tasks will be very interesting for all the actors involved in educational processes (teachers, students, researchers, administrative personnel), and this will be particularly evident when standards like Experience-API (xAPI) will help to provide a more pervasive experience for learners. Second, we are observing the rise of new era for software platforms, characterized by machine learning, deep learning, cognitive computing and many other technologies that substantially give the computer a much more active role in the respective processes. We believe that this new paradigm will apply to education too. What this will entail is mainly related to exponential learning, a process of exponential growth of training demand because new knowledge and skills must be delivered at a speed never seen before, and where big data contexts are fundamental. In this paper, we present an analysis of how LMSs should evolve in the future, in our opinion and according to our experience, in terms of functionalities and services provided to users. We believe that current LMSs and their software architectures, mainly based on traditional multi-tier, relational database-oriented architectures will not be enough to stand the impact of these two new paradigms for modern learning environments. We are in the process of re-designing a virtual community platform that we have created and developed along the years, used in our universities and in several public and private organizations. The platform is oriented towards the support of collaborative processes, where of course e-learning is one of the most important, but not the only one, and where we are adding new services supporting collaboration in different ways. In this paper we will present the software architectural changes and evolution according to the advent of big data and cognitive computing

Teaching Computer Programming Through Hands-on Labs on Cognitive Computing

In this work we report the experience of a long-lasting educational project that we have been carrying since a couple of years. In particular, we summarize the results achieved by students in the last year, when they were put to work on the collaborative development of small, yet full featured, software projects. At the same time, based on more recent findings, we seek to lay the foundations to build a pragmatic model to teach cognitive computing programming. The experience was carried on in a Programming course at the Universities of Naples “Federico II” and Genoa, in Italy, and fostered the use of a PaaS (Platform as a Service) environment for a cooperative learning activity, used to disseminate theoretical concepts acquired within the course, also by means of cognitive computing tools. The project, from its inception, has involved a relevant number of students. Initially, the experiment had to be concluded in one year but, instead, has continued evolving with new projects, as new tools and services were made available, carrying new opportunities. The evolution has led, in the most recent release, to using the IBM Bluemix platform with its wide range of components, including Watson. This work goes in the direction of developing the smart university model, by using innovative and intelligent services to help develop a new generation of applications, but also to promote and disseminate a new way for designing and building them

Cognitive computing in education

Cognitive computing is the new wave of Artificial Intelligence (AI), relying on traditional techniques based on expert systems and also exploiting statistics and mathematical model. In particular, cognitive computing systems can be regarded as a "more human" artificial intelligence. In fact, they mimic human reasoning methodologies, showing special capabilities in dealing with uncertainties and in solving problems that typically entail computation consuming processes. Moreover, they can evolve, exploiting the accumulated experience to learn from the past, both from errors and from successful findings. From a theoretical point of view, cognitive computing could replace existing calculators in many fields of application but hardware requirements are still high, even if the cloud infrastructure, which is expected to uphold its rapid growth in the very next future, can support their diffusion and ease the penetration of such a novel variety of systems, fostering new services as well as changes in many settled paradigms. In this paper, we focus on benefits that this technology can bring when applied in the education field and we make a short review of relevant experiences

An experience of collaboration using a PaaS for the smarter university model

In this paper we continue our previous research on the development of the current model of higher education, which pointed out that the labor market is looking for people with competencies and skills reflecting a T-shape model. As a consequence, universities should include a wider mix of disciplines in the curricula of their courses. Hence, to overcome existing criticisms and to provide some suggestions on how to enhance universities' performances, we thought of education as a process with inputs, outputs, and relevant dependencies. We called such a university a “smarter university” in which knowledge is a common heritage of teachers and students. In our research the smarter university model is based on a smart-city-like model, due to the fact that next generation networks and relevant services are going to be more and more integrated with existing infrastructure and information management systems. Thus, it is mandatory that smart solutions are the most prominent assets of modern university environments to improve the effectiveness of higher education. In this paper, we report the experimental results from a specific case study of collaboration between industry and university, which could be used as a refer- ence for the definition of patterns to be applied in the redesign of the current education systems, even though the experiment refers to a technological application scenario

Dietary Intake as a Link between Obesity, Systemic Inflammation, and the Assumption of Multiple Cardiovascular and Antidiabetic Drugs in Renal Transplant Recipients.

Abstract We evaluated dietary intake and nutritional-inflammation status in ninety-six renal transplant recipients, 7.2±5.0 years after transplantation. Patients were classified as normoweight (NW), overweight (OW), and obese (OB), if their body mass index was between 18.5 and 24.9, 25.0 and 29.9, and ≥30 kg/m2, respectively. Food composition tables were used to estimate nutrient intakes. The values obtained were compared with those recommended in current nutritional guidelines. 52% of the patients were NW, 29% were OW, and 19% were OB. Total energy, fat, and dietary n-6 PUFAs intake was higher in OB than in NW. IL-6 and hs-CRP were higher in OB than in NW. The prevalence of multidrug regimen was higher in OB. In all patients, total energy, protein, saturated fatty acids, and sodium intake were higher than guideline recommendations. On the contrary, the intake of unsaturated and n-6 and n-3 polyunsaturated fatty acids and fiber was lower than recommended. In conclusion, the prevalence of obesity was high in our patients, and it was associated with inflammation and the assumption of multiple cardiovascular and antidiabetic drugs. Dietary intake did not meet nutritional recommendations in all patients, especially in obese ones, highlighting the need of a long-term nutritional support in renal transplant recipients

Human Papillomavirus-16 E7 Interacts with Glutathione S-Transferase P1 and Enhances Its Role in Cell Survival

Background:Human Papillomavirus (HPV)-16 is a paradigm for "high-risk" HPVs, the causative agents of virtually all cervical carcinomas. HPV E6 and E7 viral genes are usually expressed in these tumors, suggesting key roles for their gene products, the E6 and E7 oncoproteins, in inducing malignant transformation.Methodology/Principal Findings:By protein-protein interaction analysis, using mass spectrometry, we identified glutathione S-transferase P1-1 (GSTP1) as a novel cellular partner of the HPV-16 E7 oncoprotein. Following mapping of the region in the HPV-16 E7 sequence that is involved in the interaction, we generated a three-dimensional molecular model of the complex between HPV-16 E7 and GSTP1, and used this to engineer a mutant molecule of HPV-16 E7 with strongly reduced affinity for GSTP1.When expressed in HaCaT human keratinocytes, HPV-16 E7 modified the equilibrium between the oxidized and reduced forms of GSTP1, thereby inhibiting JNK phosphorylation and its ability to induce apoptosis. Using GSTP1-deficient MCF-7 cancer cells and siRNA interference targeting GSTP1 in HaCaT keratinocytes expressing either wild-type or mutant HPV-16 E7, we uncovered a pivotal role for GSTP1 in the pro-survival program elicited by its binding with HPV-16 E7.Conclusions/Significance:This study provides further evidence of the transforming abilities of this oncoprotein, setting the groundwork for devising unique molecular tools that can both interfere with the interaction between HPV-16 E7 and GSTP1 and minimize the survival of HPV-16 E7-expressing cancer cells. © 2009 Mileo et al