Augmented Intelligence

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    MD-Manifold: A Medical Distance Based Manifold Learning Approach for Heart Failure Readmission Prediction
    ( 2021-01-05) Wang, Shaodong ; Li, Qing ; Zhang, Wenli
    Dimension reduction is considered as a necessary technique in Electronic Healthcare Records (EHR) data processing. However, no existing work addresses both of the two points: 1) generating low-dimensional representations for each patient visit; and 2) taking advantage of the well-organized medical concept structure as the domain knowledge. Hence, we propose a new framework to generate low-dimensional representations for medical data records by combining the concept-structure based distance with manifold learning. To demonstrate the efficacy, we generated low-dimensional representations for hospital visits of heart failure patients, which was further used for a 30-day readmission prediction. The experiments showed a great potential of the proposed representations (AUC = 60.7%) that has comparative predictive power of the state-of-the-art methods, including one hot encoding representations (AUC = 60.1%) and PCA representations (AUC = 58.3%), with much less training time (improved by 99%). The proposed framework can also be generalized to various healthcare-related prediction tasks, such as mortality prediction.
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    Beyond deep fakes: Conceptual framework, applications, and research agenda for neural rendering of realistic digital faces
    ( 2021-01-05) Seymour, Mike ; Riemer, Kai ; Yuan, Lingyao ; Dennis, Alan
    Neural rendering (NR) has emerged as a novel technology for the generation and animation of realistic digital human faces. NR is based on machine learning techniques such as generative adversarial networks and is used to infer human face features and their animation from large amounts of (video) training data. NR shot to prominence with the deep fake phenomenon, the malicious and unwanted use of someone’s face for deception or satire. In this paper we demonstrate that the potential uses of NR far outstrip its use for deep fakes. We contrast NR approaches with traditional computer graphics approaches, discuss typical types of NR applications in digital face generation, and derive a conceptual framework for both guiding the design of digital characters, and for classifying existing NR use cases. We conclude with research ideas for studying the potential applications and implications of NR-based digital characters.
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    Introduction to the Minitrack on Augmented Intelligence
    ( 2021-01-05) Paul, Souren ; Yuan, Lingyao ; Dennis, Alan