Objective: Temporal electronic health records (EHRs) can be a wealth of information for secondary uses, such as clinical events prediction or chronic disease management. However, challenges exist for temporal data representation. We therefore sought to identify these challenges and evaluate novel methodologies for addressing them through a systematic examination of deep learning solutions. Methods: We searched five databases (PubMed, EMBASE, the Institute of Electrical and Electronics Engineers [IEEE] Xplore Digital Library, the Association for Computing Machinery [ACM] digital library, and Web of Science) complemented with hand-searching in several prestigious computer science conference proceedings. We sought articles that reported deep learning methodologies on temporal data representation in structured EHR data from January 1, 2010, to August 30, 2020. We summarized and analyzed the selected articles from three perspectives: nature of time series, methodology, and model implementation. Results: We included 98 articles related to temporal data representation using deep learning. Four major challenges were identified, including data irregularity, data heterogeneity, data sparsity, and model opacity. We then studied how deep learning techniques were applied to address these challenges. Finally, we discuss some open challenges arising from deep learning. Conclusion: Temporal EHR data present several major challenges for clinical prediction modeling and data utilization. To some extent, current deep learning solutions can address these challenges. Future studies can consider designing comprehensive and integrated solutions. Moreover, researchers should incorporate additional clinical domain knowledge into study designs and enhance the interpretability of the model to facilitate its implementation in clinical practice.

It's an exciting time to be in robotics. Driven by increasing diversification in the industry, the $100 billion global sector has been growing by leaps and bounds. Industrial robots are no longer the exclusive domain of heavy industry or huge factories. Collaborative robots in particular have helped expand the enterprise customer base to include mid-sized and even small businesses in light manufacturing, materials handling, fulfillment, and beyond. But are the good times coming to an end?

Forrester's 2020 predictions identify key market dynamics that will impact companies' growth in the coming year. Factors including heightened values-based consumer activism; the lack of clarity around Brexit; automation, Artificial Intelligence (AI) and robotics moving deeper into the organisation; and recessionary fears due to socio-political uncertainty will make 2020 a raucous year, forcing leaders to embrace adaptability. Forrester's predictions 2020 guide is underpinned by data and research found here (client access required).

Confident CDAOs and CIOs will come to the rescue to break data logjams. Data scientists often struggle to acquire, transform, and prepare the data they need to start a machine-learning (ML) project. Data lakes, data engineers, and data prep tools have helped, but the real problem is sourcing data from a complex portfolio of applications and convincing various data gatekeepers to play along. In 2020, senior executives like chief data and analytics officers (CDAOs) and CIOs who are serious about AI will come to the rescue, with a top-down mandate to get around the data access problem. Firms with chief data officers (CDOs) are already about 1.5 times more likely to use AI, ML, and/or deep learning for their insights initiatives than those without CDOs.

Over the past three years, we've been overwhelmed with hype around AI and automation's impact on customer service interactions. By now, we were all supposed to be regularly interacting with voice-based robotic systems that were indistinguishable from human beings -- remember the Google Duplex demo from the spring of 2018? Like those jetpacks we were promised as kids, the future does not yet resemble our science fiction dreams. Improvements to these customer-facing service systems have been much more incremental than revolutionary. But 2020 is the year we will see real evidence of dramatic changes to customer service organizations themselves.

AI is real and ready In 2019, 53% of global data and analytics decision makers say they have implemented, are in the process of implementing, or are expanding or upgrading their implementation of some form of artificial intelligence. Twenty-nine percent of global developers (manager level or higher) have worked on AI/ machine learning (ML) software in the past year. Fifty-four percent of global mobility decision makers whose firms are implementing edge computing tell us that the flexibility to handle present and future AI demands is one of the biggest benefits they anticipate with edge computing. It's clear that many groups across the enterprise have tiptoed into AI. But, to take full advantage, they must overcome challenges in how to prioritize use cases, acquire the right talent, design a governance framework, and choose relevant technologies.