The healthcare systems in the US and globally are undergoing a period of rapid transformation. Medical technology breakthroughs, economic pressures and demographic trends are driving that transformation, but key enablers and catalysts for those changes are advancements in Analytics, Data Science, Cognitive Computing, and Machine Learning. Massive volumes of data are created during regular healthcare administration, delivery, and research operations; additionally, outside the medical community people produce data as part of their daily activities and social interactions that can be mined for medical use. How can this data be put to use in an ethical way respecting privacy and security to achieve the goal of high quality, accessible and affordable Healthcare? Advanced analytics and cognitive computing are a big part of the answer. In Applied Heath Care Analytics, the authors provide a concise yet comprehensive review of the key enabling tech and explain how those technologies are becoming the backbone of the Healthcare of tomorrow.
Contents:
- Conceptual Overview:
- The Era of Consumer Centric Health Care
- Health Care — From Transactional Economy to Informational Economy
- The Data Science Revolution in Health Care
- An Overview of Data Driven Technologies
- The Drive for Artificial Intelligence and Cognitive Computing
- A Taxonomy of Current Health Care Applications
- The Future of Health Care with Data Driven Technologies
- Technical Background:
- Predictive Analytics and Data Science
- From Machine Learning to Cognitive Computing
- Actionable Information: Decision Making and Risk Management
- Health Information Technologies: Codes and Syntax in Action
- Adapting Hit Using Semantic Processing Techniques
- Beyond Hipaa: Maintaining Privacy While Leveraging Data Effectively
- Current Applications:
- For Health Providers
- For Health Payers
- For R&D in the Life Sciences
- Transformative Applications
Readership: Supplementary textbook for postgraduate students in advance analytics and cognitive computing courses in Computer Science or Artificial Intelligence/Machine Learning degrees.
Mark Albert directs the PAC lab (http://pac-lab.org) where machine learning is used to automatically interpret data collected from worn sensors, including the accelerometers in mobile phones. He has published a number of papers measuring motor adaptation, detecting falls, recognizing activities, and measuring movement qualities for a wide variety of affected populations including individuals with Parkinson's disease, spinal cord injury, and transfemoral amputations. He also leads the Theoretical Neuroscience lab (http://theoneuro.org) pursuing aspects of visual development from a computational perspective.
Prior to joining Loyola, he was a postdoctoral research associate at the Rehabilitation Institute of Chicago where he maintains his affiliations as an adjunct faculty member in the department of physical medicine and rehabilitation at Northwestern University. Prior to Chicago, he received his PhD in computational biology from Cornell University, with an emphasis on applying efficient coding principles to computational neuroscience. While a graduate student there, he was also part of the mobile phone-based startup company Instinctiv. Before Cornell, as research assistant at Carnegie Mellon University, he applied computational models to cognition through human fMRI experiments and to vision in primate neurophysiology. He is a Fulbright Scholar from the University of Vienna and a graduate from Pittsburg State University.
Plamen Petrov is the chief technology officer of cognitive analytics at Deloitte Consulting, LLP. At the multi-disciplinary intersection of Cognitive Computing, Artificial Intelligence, Analytics, Big Data and Cloud Computing — the Cognitive Analytics group at Deloitte Consulting Innovation (DCI) develops and assembles products, solutions and platforms that solve some of the hardest problems that businesses across different industries encounter in their daily activities. Advanced technologies that were in research labs just a year or so ago are seamlessly blended and operationalized into cutting edge products and solutions that are practical, efficient, secure, and reliable in solving real-world business problems through disruptive innovation deployed at scale that few companies can match. Petrov's role as CTO for the cognitive analytics group involves defining and executing on the technology strategy through a combination of product development, strategic partnerships and technology acquisitions. Prior to joining Deloitte, Petrov was the chief enterprise architect at Blue Cross Blue Shield Association (BCBSA) in Chicago for more than 11 years. He established and led all the architecture practices of the company as well as the Technology Innovation and Knowledge Management area. Petrov is also an adjunct professor at Northwestern University's Farley Center for Entrepreneurship and Innovation. He earned his Master of Engineering Management at Northwestern University and his Doctor of Philosophy at the University of Illinois.
Rajeev Ronanki has more than 20 years of healthcare and information technology experience. He leads Deloitte Consulting LLP's cognitive computing and healthcare innovation practices, focused on implementing cognitive solutions for personalized consumer engagement, intelligent automation, and predictive analytics. He is at the forefront of some of the most strategic initiatives on cognitive computing, analytics, and big data, and is the author of several key publications on these topics. He also serves as the lead for Deloitte's innovation partnership program (IPP) with Singularity University, which provides a bridge for leveraging exponential technologies like artificial intelligence in the enterprise context. Ronanki's extensive functional and technical depth makes him specifically qualified to lead complex business and IT initiatives.
