@article{1433638,
      recid = {1433638},
      author = {Patgiri, Ripon, and Biswas, Anupam, and Roy, Pinki,},
      title = {Health informatics : a computational perspective in  healthcare /},
      pages = {1 online resource (x, 377 pages) :},
      abstract = {This book presents innovative research works to  demonstrate the potential and the advancements of computing  approaches to utilize healthcare centric and medical  datasets in solving complex healthcare problems. Computing  technique is one of the key technologies that are being  currently used to perform medical diagnostics in the  healthcare domain, thanks to the abundance of medical data  being generated and collected. Nowadays, medical data is  available in many different forms like MRI images, CT scan  images, EHR data, test reports, histopathological data and  doctor patient conversation data. This opens up huge  opportunities for the application of computing techniques,  to derive data-driven models that can be of very high  utility, in terms of providing effective treatment to  patients. Moreover, machine learning algorithms can uncover  hidden patterns and relationships present in medical  datasets, which are too complex to uncover, if a  data-driven approach is not taken. With the help of  computing systems, today, it is possible for researchers to  predict an accurate medical diagnosis for new patients,  using models built from previous patient data. Apart from  automatic diagnostic tasks, computing techniques have also  been applied in the process of drug discovery, by which a  lot of time and money can be saved. Utilization of genomic  data using various computing techniques is another emerging  area, which may in fact be the key to fulfilling the dream  of personalized medications. Medical prognostics is another  area in which machine learning has shown great promise  recently, where automatic prognostic models are being built  that can predict the progress of the disease, as well as  can suggest the potential treatment paths to get ahead of  the disease progression.},
      url = {http://library.usi.edu/record/1433638},
      doi = {https://doi.org/10.1007/978-981-15-9735-0},
}