In this paper, a restricted domain question answering (QA) system is described.
But while such studies can help a drug acquire approval or answer a restricted research question, they can leave patients and doctors in a lurch because they may not tell how the new drug or treatment will work once it is tried in real patients with complex problems.
We present an implemented approach for domain-restricted question answering from structured knowledge sources, based on robust semantic analysis in a hybrid NLP system architecture.
Note that because there are not too many instances available for each suffix, a restricted set of questions are used to avoid overfitting.
Previous attempts aimed at exploring this form of selection bias have relied mainly on re-approaching non-participants and asking a restricted number of questions (e.g. Holt et al, 1991; Madigan et al, 2000; Wrensch et al, 2000).
However a restricted version of this question can be answered stochastistically.
Such a restricted description raises many questions about this protean term.
A monodisciplinary panel was formed to define a restricted number of clinical questions, develop specific search strategies and critically appraise the literature using the grading of recommendations assessment, development, and evaluation (GRADE) method.
Thus, we also analyzed this question in a restricted sub-sample of participants ages 15 and under, for whom the incidence of sexual intercourse was only 17.3%%.
We used qualitative methods rather than quantitative so that we could probe participants about their answers and fully understand them, thus the information we obtained was much richer and broader than we could have gained in a questionnaire which usually ask closed questions with a restricted number of possible answers.
Though well suited for specific biological questions, this provides a restricted view on cell biology and furthermore, many informative assays cannot be converted to HTS format in microplate based platforms due to methodological limitations or cost considerations.
