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Collecting health related text from patient health writings
Akbar S.a,b, Slaughter L.a,b, Nytroo O.a
a Department of Computer and Information Science, Norwegian Univ. of Science and Technology (NTNU), Norway
b School of Electrical Engineering and Informatics, Institute of Technology Bandung (ITB), Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]The Internet has been a huge resource for sharing and collecting information including health related information. Some health related information is written by patients (lay persons) discussing their experience about health problems and treatments. This paper introduces our initial work on providing physicians with clinically useful patient health writings. More specifically, the paper presented our experiments, as a part of the whole research work, on filtering health related text from patient health writings. We focused on selecting possible feature for classifying text from breast cancer mailing list into health and non health related text. Using KNN classification method, we experimented with various features, i.e. all terms, all terms except most frequently used terms, UMLS terms, health related UMLS terms, and health related UMLS semantic types. The experiments showed that UMLS terms extracted from the text is a good feature, compared to the other features. ©2010 IEEE.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Breast Cancer,Clinical analysis,Feature selection,Health related informations,KNN classification,Mailing lists,Patient health,Semantic types[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Classification,Clinical analysis,Feature selection,Patient health writings[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1109/ICCAE.2010.5452011[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]