Association of Academicians, Scholars, Scientists and Engineers (IAASSE), USA; Institute of Doctors Engineers and Scientists (IDES), India; The International Society of Service Innovation Professionals (ISSIP); and The Society of Digital Information and Wireless Communications (SDIWC). He is also a certified Chartered Engineer of the IEI and is on the Board of Directors of IETI, Hong Kong.
Dr. Ujjwal Maulik has been a Professor in the Department of Computer Science and Engineering, Jadavpur University since 2004. He formerly acted as the Head of the same department. He held the position of the Principal and Head of the Department of Computer Science and Engineering. Dr. Maulik has worked in many universities and research laboratories around the world as a visiting Professor/Scientist including the Los Alamos National Lab, USA in 1997; University of New South Wales, Australia in 1999; University of Texas at Arlington, USA in 2001; University of Maryland at Baltimore County, USA in 2004; Fraunhofer Institute for Autonome Intelligent Systems, St. Augustin, Germany in 2005; Tsinghua University, China in 2007; Sapienza University, Rome, Italy in 2008; University of Heidelberg, Germany in 2009; German Cancer Research Center (DKFZ), Germany in 2010, 2011, and 2012; Grenoble INP, France in 2010, 2013, and 2016; University of Warsaw in 2013 and 2019; University of Padova, Italy in 2014 and 2016; Corvinus University, Budapest, Hungary in 2015 and 2016; University of Ljubljana, Slovenia in 2015 and 2017; and International Center for Theoretical Physics (ICTP), Trieste, Italy in 2014, 2017, and 2018. He is the recipient of the BOYSCAST Fellowship from the Government of India in 2001, the Alexander von Humboldt Fellowship from 2010 to 2012, and the Senior Associate of ICTP, Italy from 2012 to 2018. He is a Fellow of the Institute of Electrical and Electronics Engineers (IEEE), USA; Indian National Academy of Engineers (INAE), India; International Association for Pattern Recognition (IAPR), USA; West Bengal Academy of Science and Technology (WAST), India; Institution of Engineers (IE), India; and Institution for Electronics and Telecommunication Engineers (IETE), India, 2001. He is also an ACM “Distinguished Speaker”. His research interests include machine learning, pattern analysis, data science, bioinformatics, multi‐objective optimization, social networking, IoT, and autonomous car. In these areas, he has published ten books, more than 350 papers, filed several patents, and mentored 20 doctoral students. His other interests include outdoor sports, music, and traveling extensively around the world.
1 An Overview of Applied Smart Health Care Informatics in the Context of Computational Intelligence
Sourav De1*, and Rik Das2
1 Department of Computer Science & Engineering, Cooch Behar Government Engineering College, Vill‐ Harinchawra, P.O.‐ Ghughumari, Cooch Behar, West Bengal, 736170, India
2 Department of Information Technology, Xavier Institute of Social Service, Post Box No‐7, Dr Camil Bulcke Path, Ranchi, Jharkhand, 834001, India
1.1 Introduction
Health care informatics, in other words medical informatics, alludes to the use of data design and onboarding to the field of medical care, which basically covers the administration and utilization of patient medical services data. Through a multidisciplinary approach, it utilizes health information technology to improve medical care by depending on more advanced opportunities. According to the United States National Library of Medicine (NLM), health informatics is ”an interdisciplinary study of the design, development, adoption and application of IT‐based innovations in health care services delivery, management and planning” (DeBakey, 1991). Basically, it impacts the improvement of the obtaining, stockpiling, recovery, and utilization of data in health and bio‐medication. Intelligent health care informatics expand the domain of current medical care conveniences by encompassing aspects from intelligent technologies to computational engineering. Intelligent analysis of the information upgrades the general administration by taking everything into account.
Health care informatics combine the fields of information technology, science, and medicine for a better seamless and speedy management process that serves people worldwide. The main objective for health care informatics is to render effective health care to patients with the help of technologic advancements in public health, drug discovery, pharmacy, etc. However, there is an insufficient understanding of the computational methodologies that will be highly efficient for the health care sector and its approach for patients worldwide (Durcevic, 2020).
Belle et al. (2015) discussed various smart health care informatics that can be tackled using computational techniques. Big data analytics is required for the health care sector due to the rising costs in nations like the United States (Durcevic, 2020). Moreover, expenses are much higher than they ought to be, and they have been rising over the last 20 years. Distinctly, we are in need of smart, data‐driven improvements in the health care sector.
1.2 Big Data Analytics in Healthcare
Big data analytics can be applied in different areas of medicine. The concept of big data analytics can be employed in different areas and among them image processing, signal processing, and genomics (Ritter et al., 2011) are primarily noted.
Medical images are a vital source of data, and they are frequently employed for diagnosing, assessing therapy, and designing (Ritter et al., 2011) algorithms. X‐ray, magnetic resonance imaging (MRI), molecular imaging, computerized tomography (CT) images, photo acoustic imaging, ultrasound, fluoroscopy, and mammography are some instances of imaging methods that are found inside clinical settings (Belle et al., 2015). Medical images can run from a couple of megabytes to process a solitary report to many megabytes per analysis: for example, thin‐slice CT studies (Seibert, 2010). These types of information need huge capacity limits for long term data retention and require accurate and fast algorithms for decision‐assisting automation. Likewise, if other sources of information obtained for an individual patient are additionally applied at diagnoses, prognosis, and treatment, then the issue of proving reliable storage and increasingly advantageous methods for this large scope of records turns into a challenge.
Like health care images, medical signals likewise present quantity and speed snags, particularly during the persistent acquisition of high quality images and their storage from the many screens associated with every patient (Belle et al., 2015). Physiological signals not only create information dimension problems but also have baffling complexity of a spatiotemporal nature. Nowadays, numerous heterogeneous and uninterrupted monitoring devices are employed in the health care system to apply solitary physiological waveform information or crucial discrete data provided to systems if there should be an occurrence of plain occasion (Cvach, 2012; Drew et al., 2014).
The human genome is comprised of about thirty thousand genes. It has been observed that the price to sequence the human genome decreases with the advancement of high‐throughput sequencing technology (E.S. Lander and et al., 2001; Drmanac et al., 2010). Investigating genome‐scale information with suggestions for current public fitness insurance policies, the conveyance of care, and creating noteworthy proposals in an opportune way is a sizeable undertaking to the discipline of computational biology (Caulfield et al., 2013; Dewey et al., 2014). In a clinical setting, the delivery of these recommendations are very costly as time is very crucial.
In spite of huge expenditures by the current health care systems, clinical results become minimal (Oyelade et al., 2015). For big data analytics, it is a hazard to expect an extra quintessential section to help with investigation and revelation measures, bettering the conveyance of care, helping with the plan and design of medical care strategy, and make use of a way to exhaustively assess the muddled and tangled medical services information. More specifically, appropriation of the bits of information received from big data analytics can possibly save lives, enhance care conveyance, prolong admittance to clinical services, regulate installment to execution, and assist managing the improvement of clinical offering costs (Belle et al., 2015).
1.3
