Automatic assessment of patient eligibility by utilizing NLP and rule-based analysis.

Assessment of patient eligibility is an essential process in the clinical trial but there are a lot of manual processes involved. Natural Language Processing (NLP) is a promising technique to automate analysing of the massive volume of Electronic Medical Records (EMRs) hence it can assist in the assessment of patient eligibility, especially in clinical trials that require complex inclusion/exclusion criteria. In this paper, we proposed a hybrid model which utilized both rule-based and NLP technologies to automate the assessment of patient eligibility.

Spatially adaptive wavelet denoising using the minimum description length principle.

This paper presents a new spatially adaptive wavelet denoising method. Based on a doubly stochastic process model of wavelet coefficients, the method gives a new threshold, which varies spatially according to the variances of the coefficients, using the minimum description length (MDL) principle. The new threshold is not only easier to analyze since it is in a closed form, but also provides more facility for future compression than several other methods, almost without deteriorating mean square error risk.