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 ndings. Owing to these special features, it is perceptible that healthcare can greatly bene t from such a powerful technology. In fact, clinical diagnoses are frequently based on statistics and signi cant 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 classi cation with the uncountable combinations that derive from such a complex structure

Cognitive Computing supported Medical Decision Support System for Patient’s Driving Assessment

To smartly utilize a huge and constantly growing volume of data, improve productivity and increase competitiveness in various fields of life; human requires decision making support systems that efficiently process and analyze the data, and, as a result, significantly speed up the process. Similarly to all other areas of human life, healthcare domain also is lacking Artificial Intelligence (AI) based solution. A number of supervised and unsupervised Machine Learning and Data Mining techniques exist to help us to deal with structured data. However, in a real life, we pretty much deal with unstructured data that hides useful knowledge and valuable information inside human-readable plain texts, images, audio and video. Therefore, such IT giants as IBM, Google, Microsoft, Intel, Facebook, etc., as well as variety of SMEs are actively elaborating different Cognitive Computing services and tools to get a value from unstructured data. Thus, the paper presents feasibility study of IBM Watson cognitive computing services and tools to address the issue of automated health records processing to support doctor’s decision for patient’s driving assessment

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

Do Healthcare Workers Need Cognitive Computing Technologies? A Qualitative Study Involving IBM Watson and Dutch Professionals

The healthcare ecosystem continually produces huge volumes of structured and unstructured data. Cognitive computing, a new computing paradigm, promises to effectively help healthcare researchers and practitioners to derive precious information from data. Arguably, the most famous cognitive computing system is called IBM Watson, which has been adapted to different domains, including healthcare. In this paper, we investigate whether there is a natural demand for cognitive computing systems coming from healthcare workers. Specifically, using the technology acceptance model to guide our efforts, we study different perceptions from healthcare professionals from the Netherlands regarding IBM Watson. The results from our interviews show that virtually all the perceptions are very negative. We list several reasons underlying these perceptions alongside potential ways of changing them. We believe our results are of great value to health information technology professionals trying to introduce a potentially groundbreaking product and to organizations that are contemplating investing in those technologies

When do we eat? An evaluation of food items input into an electronic monitoring application

We present a formative study that examines what, when, and how participants in a chronic kidney disease (stage 5) population input food items into an electronic intake monitoring application. Participants scanned food item barcodes or voice recorded food items they consumed during a three week period. The results indicated that a learning curve was associated with barcode scanning; participants with low literacy skills had difficulty describing food items in voice recordings; and participants input food items depending on when they had dialysis treatment. Participants thought this electronic self monitoring application would be helpful for chronically ill populations in their first year of treatmen