Patient symptoms elicitation process for breast cancer medical expert systems: A semantic web and natural language parsing approach

Information gathering from patient by clinicians during diagnostic procedures may sometimes require some skills to adequately collect required information that will be sufficient for the procedure. A situation where this information gathering may proof difficult in when a diagnostic decision making support system (DDSS) will have to gather such information from patient before carrying out the diagnostic procedure. Research has proven that it is more challenging to ensure user or patient inputs, in their raw form, maps into the list of acceptable medical terms for diagnostic tasks. This paper therefore proposes a formalized input generating model that addresses this shortcoming through the creation of an inference process, breast cancer lexicon, rule set and natural language processing (NLP). We developed an input generation algorithm which uses the python natural language processing capability in first filtering and generation the first pre-input collection. Furthermore, this algorithm then feeds in the pre-input word collection as input into the inference engine which has in its memory the rule set and ontology-based lexicon developed. Finally, this generates a list of acceptable tokens that will be sent into the medical expert system or DDSS for the diagnosing breast cancer. This proposed model was tested on a breast cancer based DDSS earlier designed by this authors, and result shows that the inference support of this model generates additional input of about 64% compared to when the patient's input where sent in as input in is state.