Abstract: The present invention is a modeling system and process for predicting individual outcomes and conditions from written database records of a population of individuals, using iterative variation of parameters. Individual subject documents are created by concatenation of unstructured text fields from the written database records of individuals, and these are processed using Natural Language Processing. An individual subject document corpus is built, and terms in the corpus are weighted and mapped to standard vocabularies. A term-by-document matrix is built and its dimensionality is reduced by Latent Semantic Indexing. Individual and term queries are combined and scored, producing a ranked list. The parameters of the model are iteratively optimized for an input list of individuals with corresponding condition, action, or outcome score values.

Abstract: A predictive modeling method implemented on a computer for predicting patient outcomes and conditions from medical database records of a population of patients, and an optimization process of iterative variation of parameters of the method to achieve a best precision fit. Individual patient documents are created by concatenation of unstructured text fields from the patient's medical record, and these are processed using Natural Language Processing. A patient document corpus is built, and terms in the corpus are weighted and mapped to standard vocabularies. A term-by-document matrix is built and its dimensionality is reduced by Latent Semantic Indexing. Patient and term queries are combined and scored, producing a ranked list. The parameters of the model are iteratively optimized for an input list of patients with corresponding health score values.

Abstract: This document presents a system and method to extract highly meaningful terms from unstructured fields in an EMR that distinguish two different patient populations. Using this system, healthcare providers can quickly identify terms that are highly associated with a specific set of patients, for instance, chronic heart failure (CHF) patients who are high utilizers of the emergency department (ED) compared to CHF patients with low ED utilization. The system enables healthcare providers to identify root causes of health outcomes and to discover potential targets for intervention and improving healthcare delivery.

Abstract: A semantic gene classification and annotation system, method and computer program can utilize Latent Semantic Indexing (LSI) to identify conceptually related genes based on textual information in biomedical literature, including MEDLINE citations. In addition, term weights calculated from the usage of the gene terms in and across gene documents can be used to automatically assign gene aliases and extend gene function annotation based upon primary biomedical literature.

Abstract: A novel method of primer walking using octamer oligonucleotides to prime DNA sequencing reactions is described. Octamer sequencing is compatible with isotopic and fluorescent sequencing chemistry, reaction conditions are optimized such that the samples can be processed in parallel and the procedures are automated. This strategy is faster than the traditional primer walking sequencing strategy as the existence of a primer library allows immediate access to a primer for the next sequencing reaction, eliminating delays associated with designing and synthesizing gene specific primers. The octamer library is comprised of optimized sequencing primers, such that octamer sequencing yields results equivalent to or better than traditional primer walking.