Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Background: As targeted cancer therapies and molecular profiling become widespread, the era of “precision oncology” is at hand. Hover, cancer genomes are complex, making mutation-specific outcomes difficult to track.  created a proof-of-principle, CUSTOM-SEQ: Continuously Updating System for Tracking Outcome by Mutation, to Support Evidence-based Querying, to automatically calculate and display mutation-specific survival statistics from electronic health record data.#N##N#Methods: Patients with cancer genotyping re included, and clinical data was extracted through a variety of algorithms. Results re refreshed regularly and injected into a standard reporting platform. Significant results re highlighted for visual cueing. A subset was additionally stratified by stage, smoking status, and treatment exposure.#N##N#Results: By August 2015, 4310 patients with a median follow-up of 17 months had sufficient data for survival calculation. As expected, epidermal growth factor receptor (EGFR) mutations in lung cancer re associated with superior overall survival, hazard ratio (HR) = 0.53 ( P  < .001), validating the approach. Guanine nucleotide binding protein (G protein), q polypeptide (GNAQ) mutations in melanoma re associated with inferior overall survival, a novel finding (HR = 3.42, P  < .001). Smoking status was not prognostic for epidermal growth factor receptor–mutated lung cancer patients, who also lived significantly longer than their counterparts, even with advanced disease (HR = 0.54, P  = .001).#N##N#Interpretation: CUSTOM-SEQ represents a novel rapid learning system for a precision oncology environment. Retrospective studies are often limited by study of specific time periods and can lead to incomplete conclusions. Because data is continuously updated in CUSTOM-SEQ, the evidence base is constantly growing. Future work will allow users to interactively explore populations by demographics and treatment exposure, in order to further investigate significant mutation-specific signals.
['Jeremy L. Warner', 'Lucy Wang', 'William Pao', 'Jeffrey A. Sosman', 'Ravi V. Atreya', 'Pam Carney', 'Mia A. Levy']
I am writing a research paper on a idea CUSTOM-SEQ: A Prototype for Oncology Rapid Learning in a Comprehensive EHR Environment
[{"from": "human", "value": "I am writing a research paper on a idea CUSTOM-SEQ: A Prototype for Oncology Rapid Learning in a Comprehensive EHR Environment"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Background: As targeted cancer therapies and molecular profiling become widespread, the era of \u201cprecision oncology\u201d is at hand. Hover, cancer genomes are complex, making mutation-specific outcomes difficult to track.  created a proof-of-principle, CUSTOM-SEQ: Continuously Updating System for Tracking Outcome by Mutation, to Support Evidence-based Querying, to automatically calculate and display mutation-specific survival statistics from electronic health record data.#N##N#Methods: Patients with cancer genotyping re included, and clinical data was extracted through a variety of algorithms. Results re refreshed regularly and injected into a standard reporting platform. Significant results re highlighted for visual cueing. A subset was additionally stratified by stage, smoking status, and treatment exposure.#N##N#Results: By August 2015, 4310 patients with a median follow-up of 17 months had sufficient data for survival calculation. As expected, epidermal growth factor receptor (EGFR) mutations in lung cancer re associated with superior overall survival, hazard ratio (HR)\u2009=\u20090.53 ( P \u2009<\u2009.001), validating the approach. Guanine nucleotide binding protein (G protein), q polypeptide (GNAQ) mutations in melanoma re associated with inferior overall survival, a novel finding (HR\u2009=\u20093.42, P \u2009<\u2009.001). Smoking status was not prognostic for epidermal growth factor receptor\u2013mutated lung cancer patients, who also lived significantly longer than their counterparts, even with advanced disease (HR\u2009=\u20090.54, P \u2009=\u2009.001).#N##N#Interpretation: CUSTOM-SEQ represents a novel rapid learning system for a precision oncology environment. Retrospective studies are often limited by study of specific time periods and can lead to incomplete conclusions. Because data is continuously updated in CUSTOM-SEQ, the evidence base is constantly growing. Future work will allow users to interactively explore populations by demographics and treatment exposure, in order to further investigate significant mutation-specific signals."}]