Knowledge graph for Decision Engine

With the advent of Bigdata technologies, healthcare data captured and stored at multiple granular levels and multiple formats. In the healthcare domain, includes hospitals, pharmaceuticals, and insurance companies have an enormous amount of data in structured tables. However, significant amounts of the big data remain underutilized due to data isolation, distribution, and heterogeneity. Despite interconnected tabular data linked together in some way for ML input, challenges are, increased dimensionality, normalization of data which is not natural representation, repetition of data on merging different aggregated data across tables. Machine learning models supposes the observations are not dependent however, the real world information is interconnected. Knowledge graphs and machine learning are two important tools to understand and model complex concepts, while machine learning is a process by which computers learn from data, without being explicitly programmed.

Autorentext

Für Pharmaunternehmen ist es sehr wichtig, von den Fachkräften des Gesundheitswesens in einem therapeutischen Universum zu erfahren, wer wahrscheinlich zum ersten Mal ein Medikament ausprobieren, mehr verschreiben oder Ihre Marke in naher Zukunft absetzen wird. Die Beantwortung dieser Fragen und die Erlangung eines besseren Verständnisses der dynamischen HCP-Landschaft haben für die Pharmaindustrie höchste Priorität.