Extracting and representing causal knowledge of health conditions

Abstract

Most healthcare and health research organizations published their health knowledge on the web through HTML or semantic presentations nowadays e.g. UK National Health Service website. Especially, the HTML contents contain valuable information about the individual health condition and graph knowledge presents the semantics of words in the contents. This paper focuses on combining these two for extracting causality knowledge. Understanding causality relations is one of the crucial tasks to support building an Artificial Intelligent (AI) enabled healthcare system. Unlike other raw data sources used by AI processes, the causality semantic dataset is generated in this paper, which is believed to be more efficient and transparent for supporting AI tasks. Currently, neural network-based deep learning processes found themselves in a hard position to explain the prediction outputs, which is majorly because of lacking knowledge-based probability analysis. Dynamic probability analysis based on causality modeling is a new research area that not only can model the knowledge in a machine-understandable way but also can create causal probability relations inside the knowledge. To achieve this, a causal probability generation framework is proposed in this paper that extends the current Description Logic (DL), applies semantic Natural Language Processing (NLP) approach, and calculates runtime causal probabilities according to the given input conditions. The framework can be easily implemented using existing programming standards. The experimental evaluations extract 383 common disease conditions from the UK NHS (the National Health Service) and enable automatically linked 418 condition terms from the DBpedia dataset.