Humans are able to provide symbolic knowledge in structured form for potential use by an AI system in learning human-desirable concepts. In clinical settings, for instance, prediction of patient outcomes by an AI can be guided by knowledge from patient history. This history contains concepts such as treatment information, observational and drug-related information, mental health conditions, and severity of disease/disorder. Additionally, there is also often a certain graphical structure to the knowledge among the concepts, for example, ”patient symptoms cause certain tests to be taken”, which in turn affects the prescription of medication. This type of structure between human interpretable concepts contained in knowledge can aid the AI in an informed prediction.
PyData Berlin 2020, 2020.
© Kaushik Roy and Manas Gaur, 2020
Roy, K. & Gaur, M. (2020) Knowledge-infused statistical learning for social good. PyData Berlin 2020.