Informatics to support patient choice between diverse medical systems C3 - 2014 IEEE 16th International Conference on e-Health Networking, Applications and Services, Healthcom 2014
- Authors: Golden, Isaac , Stranieri, Andrew , Sahama, Tony , Pilapitiya, Senaka , Siribaddana, Sisira , Vaughan, Stephen
- Date: 2014
- Type: Text , Conference proceedings
- Full Text: false
- Description: Culturally, philosophically and religiously diverse medical systems including Western medicine, Traditional Chinese Medicine, Ayurvedic Medicine and Homeopathic Medicine, once situated in places and times relatively unconnected from each other, currently co-exist to a point where patients must choose which system to consult. These decisions require comparative analyses, yet the divergence in key underpinning assumptions is so great that comparisons cannot easily be made. However, diverse medical systems can be meaningfully juxtaposed for the purpose of making practical decisions if relevant information is presented appropriately. Information regarding privacy provisions inherent in the typical practice of each medical system is an important element in this juxtaposition. In this paper the information needs of patients making decisions regarding the selection of a medical system, are examined.
Data mining Traditional Chinese Medicine (TCM) : Lessons learnt from mining in law and allopathic medicine
- Authors: Stranieri, Andrew , Sahama, Tony
- Date: 2012
- Type: Text , Conference proceedings
- Full Text: false
- Description: Key decisions at the collection, pre-processing, transformation, mining and interpretation phase of any knowledge discovery from database (KDD) process depend heavily on assumptions and theoretical perspectives relating to the type of task to be performed and characteristics of data sourced. In this article, we compare and contrast theoretical perspectives and assumptions taken in data mining exercises in the legal domain with those adopted in data mining in TCM and allopathic medicine. The juxtaposition results in insights for the application of KDD for Traditional Chinese Medicine. © 2012 IEEE.
- Description: 2003009797