A Framework for Mining Signatures from Event Sequences and Its

Applications in Healthcare Data

Abstract

• This paper proposes a novel temporal knowledge representation and learning framework to perform large-scale temporal signature mining of longitudinal heterogeneous event data.

• The framework enables the representation, extraction, and mining of high¬order latent event structure and relationships within single and multiple event sequences.

• The proposed knowledge representation maps the heterogeneous event sequences to a geometric image by encoding events as a structured spatial-temporal shape process.

• We present a doubly constrained convolutional sparse coding framework that learns interpretable and shift-invariant latent temporal event signatures.

Abstract con…

• We show how to cope with the sparsity in the data as well as in the latent factor model by inducing a double sparsity constraint on the ^-divergence to learn an overcomplete sparse latent factor model.

• A novel stochastic optimization scheme performs large-scale incremental learning of group-specific temporal event signatures.

• We validate the framework on synthetic data and on an electronic health record dataset.

Existing system

• Temporal event data are ubiquitous in nature and all aspects of our everyday life. Examples are daily traces of our activities, behaviors, and decisions, recording a complex network of interactions that form part of our society.

• Other examples include the neural firing pattern of individual neurons in our brains [19],business transactions in the financial sector [10],external event stimuli a robot interacts with [18], or other event-related data from sensor measurements for scientific, engineering, and business applications [21], [4].

Architecture Diagram

System specification

HARDWARE REQUIREMENTS Processor : intel Pentium IV Ram : 512 MB Hard Disk : 80 GB HDD SOFTWARE REQUIREMENTS Operating System : windows XP / Windows 7 FrontEnd : JavaBackEnd : MySQL 5

CONCLUSION

• In this paper, we have presented a novel temporal event matrix representation and learning framework in conjunc¬tion with an in-depth validation on both synthetic and real world datasets. The framework has wide applicability to a variety of data and application domains that involve large- scale longitudinal event data.

• We have demonstrated that our proposed framework is able to cope with the double sparsity problem and that the induced double sparsity constraint on the ^-divergence enables automatic relevance determination for solving the optimal rank selection problem via an overcomplete sparse latent factor model.

• Further, the framework is able to learn shift invariant high-order latent event patterns in large-scale data. We empirically showed that our stochastic optimization scheme converges to a fixed point and we have demonstrated that our framework can learn the latent event patterns within a group. Future work will be devoted to a thorough clinical assessment for visual interactive knowledge discovery in large electronic health record databases.

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