Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Formalizing eligibility criteria in a computer-interpretable language would facilitate eligibility determination for study subjects and the identification of studies on similar patient populations. Because such formalization is extremely labor intensive,  transform the problem from one of fully capturing the semantics of criteria directly in a formal expression language to one of annotating free-text criteria in a format called ERGO annotation. The annotation can be done manually, or it can be partially automated using natural-language processing techniques.  evaluated our approach in three ways. First,  assessed the extent to which ERGO annotations capture the semantics of 1000 eligibility criteria randomly drawn from ClinicalTrials.gov. Second,  demonstrated the practicality of the annotation process in a feasibility study. Finally,  demonstrate the computability of ERGO annotation by using it to (1) structure a library of eligibility criteria, (2) search for studies enrolling specified study populations, and (3) screen patients for potential eligibility for a study.  therefore demonstrate a new and practical method for incrementally capturing the semantics of free-text eligibility criteria into computable form.
['Samson W. Tu', 'Mor Peleg', 'Simona Carini', 'Michael Bobak', 'Jessica Ross', 'Daniel L. Rubin', 'Ida Sim']
I am writing a research paper on a idea A practical method for transforming free-text eligibility criteria into computable criteria
[{"from": "human", "value": "I am writing a research paper on a idea A practical method for transforming free-text eligibility criteria into computable criteria"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Formalizing eligibility criteria in a computer-interpretable language would facilitate eligibility determination for study subjects and the identification of studies on similar patient populations. Because such formalization is extremely labor intensive,  transform the problem from one of fully capturing the semantics of criteria directly in a formal expression language to one of annotating free-text criteria in a format called ERGO annotation. The annotation can be done manually, or it can be partially automated using natural-language processing techniques.  evaluated our approach in three ways. First,  assessed the extent to which ERGO annotations capture the semantics of 1000 eligibility criteria randomly drawn from ClinicalTrials.gov. Second,  demonstrated the practicality of the annotation process in a feasibility study. Finally,  demonstrate the computability of ERGO annotation by using it to (1) structure a library of eligibility criteria, (2) search for studies enrolling specified study populations, and (3) screen patients for potential eligibility for a study.  therefore demonstrate a new and practical method for incrementally capturing the semantics of free-text eligibility criteria into computable form."}]