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

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

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

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

Lopinavir/Ritonavir and Darunavir/Cobicistat in Hospitalized COVID-19 Patients: Findings From the Multicenter Italian CORIST Study

Background: Protease inhibitors have been considered as possible therapeutic agents for COVID-19 patients. Objectives: To describe the association between lopinavir/ritonavir (LPV/r) or darunavir/cobicistat (DRV/c) use and in-hospital mortality in COVID-19 patients. Study Design: Multicenter observational study of COVID-19 patients admitted in 33 Italian hospitals. Medications, preexisting conditions, clinical measures, and outcomes were extracted from medical records. Patients were retrospectively divided in three groups, according to use of LPV/r, DRV/c or none of them. Primary outcome in a time-to event analysis was death. We used Cox proportional-hazards models with inverse probability of treatment weighting by multinomial propensity scores. Results: Out of 3,451 patients, 33.3% LPV/r and 13.9% received DRV/c. Patients receiving LPV/r or DRV/c were more likely younger, men, had higher C-reactive protein levels while less likely had hypertension, cardiovascular, pulmonary or kidney disease. After adjustment for propensity scores, LPV/r use was not associated with mortality (HR = 0.94, 95% CI 0.78 to 1.13), whereas treatment with DRV/c was associated with a higher death risk (HR = 1.89, 1.53 to 2.34, E-value = 2.43). This increased risk was more marked in women, in elderly, in patients with higher severity of COVID-19 and in patients receiving other COVID-19 drugs. Conclusions: In a large cohort of Italian patients hospitalized for COVID-19 in a real-life setting, the use of LPV/r treatment did not change death rate, while DRV/c was associated with increased mortality. Within the limits of an observational study, these data do not support the use of LPV/r or DRV/c in COVID-19 patients

Prescription appropriateness of anti-diabetes drugs in elderly patients hospitalized in a clinical setting: evidence from the REPOSI Register

Diabetes is an increasing global health burden with the highest prevalence (24.0%) observed in elderly people. Older diabetic adults have a greater risk of hospitalization and several geriatric syndromes than older nondiabetic adults. For these conditions, special care is required in prescribing therapies including anti- diabetes drugs. Aim of this study was to evaluate the appropriateness and the adherence to safety recommendations in the prescriptions of glucose-lowering drugs in hospitalized elderly patients with diabetes. Data for this cross-sectional study were obtained from the REgistro POliterapie-Società Italiana Medicina Interna (REPOSI) that collected clinical information on patients aged ≥ 65 years acutely admitted to Italian internal medicine and geriatric non-intensive care units (ICU) from 2010 up to 2019. Prescription appropriateness was assessed according to the 2019 AGS Beers Criteria and anti-diabetes drug data sheets.Among 5349 patients, 1624 (30.3%) had diagnosis of type 2 diabetes. At admission, 37.7% of diabetic patients received treatment with metformin, 37.3% insulin therapy, 16.4% sulfonylureas, and 11.4% glinides. Surprisingly, only 3.1% of diabetic patients were treated with new classes of anti- diabetes drugs. According to prescription criteria, at admission 15.4% of patients treated with metformin and 2.6% with sulfonylureas received inappropriately these treatments. At discharge, the inappropriateness of metformin therapy decreased (10.2%, P < 0.0001). According to Beers criteria, the inappropriate prescriptions of sulfonylureas raised to 29% both at admission and at discharge. This study shows a poor adherence to current guidelines on diabetes management in hospitalized elderly people with a high prevalence of inappropriate use of sulfonylureas according to the Beers criteria