Self-augmenting Knowledge Base for Informed Decision Making With Biomedical Applications in Cancer Diagnosis

Abstract

Fuzzy sets methodology to automatically generate knowledge base for informed decision making is proposed. As a proof of concept it has initially been applied to generate regulatory/health/environmental guidance rules for textile and apparel companies. Subsequently, the system will be augmented to incorporate additional consumer goods, and down the road, after some modifications, could be utilized as a much needed health care disruptor tool in personalized medicine for both patients and clinicians. The apparel category provides for a diverse set of mandatory regulations and some voluntary standards. Mandatory requirements such as CPSIA, FTC for Care and Textile labelling, in addition to AATCC requirements for colourfastness and formaldehyde were taken into consideration. Initial focus was on carcinogenic dyes and pigments. Databases from the International Agency for Research on Cancer (IARC), the US National Toxicology Program (NTP) are to be incorporated, in conjunction with computational intelligence, to identify potential toxins or carcinogens present in the industrial process or the final product, thus alerting manufactures and consumers through a user-friendly interface. This capability can be quickly developed and validated using modern software product development approaches incorporating Design Thinking, Agile Development with Scrum, and Business Model Generation to get this to market where key benefits can be derived