%0 Journal Article %1 noauthororeditor %A Banjar, Haneen %D 2017 %J Health Informatics - An International Journal (HIIJ) %K %N 2 %P 1-21 %R 10.5121/hiij.2017.6201 %T PERSONALIZED MEDICINE SUPPORT SYSTEM: RESOLVING CONFLICT IN ALLOCATION TO RISK GROUPS AND PREDICTING PATIENT MOLECULAR RESPONSE TO TARGETED THERAPY %U http://aircconline.com/hiij/V6N2/6217hiij01.pdf %V 6 %X Treatment management in cancer patients is largely based on the use of a standardized set of predictive and prognostic factors. The former are used to evaluate specific clinical interventions, and they can be useful for selecting treatments because they directly predict the response to a treatment. The latter are used to evaluate a patient’s overall outcomes, and can be used to identify the risks or recurrence of a disease. Current intelligent systems can be a solution for transferring advancements in molecular biology into practice, especially for predicting the molecular response to molecular targeted therapy and the prognosis of risk groups in cancer medicine. This framework primarily focuses on the importance of integrating domain knowledge in predictive and prognostic models for personalized treatment. Our personalized medicine support system provides the needed support in complex decisions and can be incorporated into a treatment guide for selecting molecular targeted therapies.

@article{noauthororeditor, abstract = {Treatment management in cancer patients is largely based on the use of a standardized set of predictive and prognostic factors. The former are used to evaluate specific clinical interventions, and they can be useful for selecting treatments because they directly predict the response to a treatment. The latter are used to evaluate a patient’s overall outcomes, and can be used to identify the risks or recurrence of a disease. Current intelligent systems can be a solution for transferring advancements in molecular biology into practice, especially for predicting the molecular response to molecular targeted therapy and the prognosis of risk groups in cancer medicine. This framework primarily focuses on the importance of integrating domain knowledge in predictive and prognostic models for personalized treatment. Our personalized medicine support system provides the needed support in complex decisions and can be incorporated into a treatment guide for selecting molecular targeted therapies. }, added-at = {2018-08-13T07:30:27.000+0200}, author = {Banjar, Haneen}, biburl = {https://www.bibsonomy.org/bibtex/2de709f8968949f2b07fe8bd23408b1d0/jeffrenna}, doi = {10.5121/hiij.2017.6201}, interhash = {ff9d46ec88b4070c79d42cfc2bbd3f26}, intrahash = {de709f8968949f2b07fe8bd23408b1d0}, issn = {2319 - 2046}, journal = {Health Informatics - An International Journal (HIIJ)}, keywords = {}, month = may, number = 2, pages = {1-21}, timestamp = {2018-08-13T07:30:27.000+0200}, title = {PERSONALIZED MEDICINE SUPPORT SYSTEM: RESOLVING CONFLICT IN ALLOCATION TO RISK GROUPS AND PREDICTING PATIENT MOLECULAR RESPONSE TO TARGETED THERAPY}, url = {http://aircconline.com/hiij/V6N2/6217hiij01.pdf}, volume = 6, year = 2017 }