International Conference on
Artificial Intelligence and Cognitive Healthcare
Scientific Program
Keynote Session:
Title: Watson deep learning as a service, a cloud-based deep learning platform
Biography:
Susan Diamond is the engineering manager and architect of Watson Deep Learning as a Service platform. She led the Watson product engineering team to work with IBM research to create and productize the Deep Learning as a Service platform. The DLaaS platform is being used by Watson service model training as well as Watson customers. Previously, she was one of the handful people that started the Watson Developer Cloud, a cloud platform that hosts Watson cognitive applications
Abstract:
Machine Learning workloads have traditionally been run in high-performance computing (HPC) environments, where users log in to
dedicated machines and utilize the attached GPUs to run training jobs on huge datasets. Training large neural network models is
very resource intensive, and even after exploiting parallelism and accelerators such as GPUs, a single training job can still take days. Consequently, the cost of hardware is a barrier to entry. Even when upfront cost is not a concern, the lead time to setup such an HPC environment takes months from acquiring hardware to setup the hardware with the right set of firmware, software installed and configured. Furthermore, scalability is hard to achieve in a rigid traditional lab environment. Therefore, it is slow to react to the dynamic change in artificial intelligent industry.
Watson Deep Learning as a service, a cloud-based deep learning platform that mitigates the long lead time and high upfront investment in hardware. It enables robust and scalable sharing of resources among the teams in an organization. It is designed for on-demand cloud environments. Providing a similar user experience in a multi-tenant cloud environment comes with its own unique challenges regarding fault tolerance, performance,
and security. Watson Deep Learning as a service tackles these challenges and present a deep learning stack for the cloud environments in a secure, scalable and fault-tolerant manner. It supports a wide range of deep-learning frameworks such as Tensorflow, PyTorch, Caffe, Torch, Theano, and MXNet etc.
These frameworks reduce the effort and skillset required to design, train, and use deep learning models.
Title: Artificial Intelligence and its Impact on Healthcare Jobs in the Future
Biography:
Dennis Anderson is Chairman and Professor of Management and Information Technology at St. Francis College, New York City. He also serves as Founding Executive Director of the Institute of E-government and Global Sustainability and the Center for Entrepreneurship. Prior to this appointment, he was a Professor of information systems and Associate Dean at Pace University. He also served as Founding Director of the University’s Center for Advanced Media. He is a strong advocate of technology-enhanced learning, emerging technologies, sustainable technologies, and knowledge entrepreneurship. He has taught various business, information systems and computer science courses at NYU Courant Institute, City University of New York, and Pace University. He has published and presented on various topics related to artificial intelligence and future workforce.
Abstract:
Recently there have been many global discussions about how Artificial Intelligence (AI) will change every aspects of human society. Some of them picture a dark imagery of the future and some picture a euphoric one. Most people seem to think it can have a devastating impact if a singularity or self-awareness is achieved. Regardless of the outcome, the current trajectory and development in automation, robotics and AI will have a profound impact on everything especially things that are repetitive, physical and labor intensive. It doesn’t stop at the physical world. It can also impact the professional and creative world. Healthcare is one of those industries that will go through tremendous changes and disruptions. Information technology has already changed so much in how the industry functions. This talk will focus on what AI is doing in healthcare and how it will change the future workforce in healthcare.
Title: Using latent class analysis to identify the association between latent class inpatient length of stay: a case study
Biography:
Christina Scolieri, a United States Marine Corps veteran and expert in Health Law has devoted her career to improving patient care and outcomes. Having worked in both academia and the medical device and pharmaceutical industry, she has built a model utilizing evidence-based criteria measurements paired with industry initiatives in order to facilitate true change within an organization to improve patient outcomes. The foundational idea behind many of Christina’s patient initiatives is behavioral change. Data allows us to create brilliant algorithms and subsequent outputs, but in her opinion, the real challenge comes from the implementation of the output, and that is the behavioral component. This unique perspective has allowed for significant findings and improvement for partner facilities whom have partnered with Christina and Omnicell’s Performance Center.
Abstract:
The primary objective of this case study was to identify differences in length of stay (LOS) in patients undergoing total hip arthroplasty (THA) that are associated with medications administered within a latent class assignment (LCA). The patient population included all patients who underwent total hip arthroplasty (THA) from August 2017 through June 2018 (n=248) in a 430-bed acute care hospital located in northeastern Ohio. Utilizing Latent Class Analysis to identify medication groupings by patient, and a subsequent zero-truncated negative binomial regression model (ZTNB) analysis, we were able to assess the differences in mean length of stay between latent class assignments. With LC1 as an initial reference point, patients assigned to LC2 had an increase in length of stay of 4.5 days (P <0.001). Setting the reference point at LC2, a decrease of 0.68 days (P < 0.001) length of stay was associated with patients assigned to latent class 3 (LC3) instead of LC2. A second iteration of the ZTNB model including eight additional covariates also yielded statistically significant values between latent class assignment and length of stay. The results of each analysis iteration, whether including or excluding covariates, yielded consistent results in statistically significant value differences in the LC1 vs. LC2 length of stay values and LC2 vs LC3 length of stay.
