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@article{147940,
        author = {Prashant Bharti and Vijay saboo and Mayank kumar and S.Satheesh kumar},
        title = {MINING TEMPORAL PATTERNS FROM SEQUENTIAL HEALTHCARE DATA},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {5},
        number = {11},
        pages = {528-530},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=147940},
        abstract = {Mining Temporal patterns from sequential healthcare data is a simple algorithmic prediction project that helps individuals to identify the disease in advance. Electronic health records (EHRs) can be represented as sequences of time-stamped events. These are used to predict the cause and the nature of disease keeping in mind the previous records. Therefore, temporal patterns, such as transitions between clinical events over time, can be extracted using temporal mining. A methodological approach is used to extract patterns of transitions between adverse events after Left Ventricular Assist Device (LVAD) implant in patients with advanced heart failure. The project includes various features that could enable prevent diseases as well as keep a person updated and healthy.},
        keywords = {Electronic health records, Left Ventricular Assist Device, temporal data},
        month = {},
        }