Hospital Readmission and Social Risk Factors Identified from Physician Notes.

Objective: To evaluate the prevalence of seven social factors using physician notes as compared to claims and structured electronic health records (EHRs) data and the resulting association with 30-day readmissions.

Study Setting: A multihospital academic health system in southeastern Massachusetts.

Study Design: An observational study of 49,319 patients with cardiovascular disease admitted from January 1, 2011, to December 31, 2013, using multivariable logistic regression to adjust for patient characteristics.

Constraint based temporal event sequence mining for Glioblastoma survival prediction.

Objective: A significant challenge in treating rare forms of cancer such as Glioblastoma (GBM) is to find optimal personalized treatment plans for patients. The goals of our study is to predict which patients survive longer than the median survival time for GBM based on clinical and genomic factors, and to assess the predictive power of treatment patterns.

Method: We developed a predictive model based on the clinical and genomic data from approximately 300 newly diagnosed GBM patients for a period of 2years.

Text analysis of MEDLINE for discovering functional relationships among genes: evaluation of keyword extraction weighting schemes.

One of the key challenges of microarray studies is to derive biological insights from the gene-expression patterns. Clustering genes by functional keyword association can provide direct information about the functional links among genes. However, the quality of the keyword lists significantly affects the clustering results.

Text mining biomedical literature for discovering gene-to-gene relationships: a comparative study of algorithms.

Partitioning closely related genes into clusters has become an important element of practically all statistical analyses of microarray data. A number of computer algorithms have been developed for this task. Although these algorithms have demonstrated their usefulness for gene clustering, some basic problems remain.

Comparison of two schemes for automatic keyword extraction from MEDLINE for functional gene clustering.

One of the key challenges of microarray studies is to derive biological insights from the unprecedented quatities of data on gene-expression patterns. Clustering genes by functional keyword association can provide direct information about the nature of the functional links among genes within the derived clusters. However, the quality of the keyword lists extracted from biomedical literature for each gene significantly affects the clustering results.

Investigation into biomedical literature classification using support vector machines.

Specific topic search in the PubMed Database, one of the most important information resources for scientific community, presents a big challenge to the users. The researcher typically formulates boolean queries followed by scanning the retrieved records for relevance, which is very time consuming and error prone. We applied Support Vector Machines (SVM) for automatic retrieval of PubMed articles related to Human genome epidemiological research at CDC (Center for disease Control and Prevention).

MITOMAP: a human mitochondrial genome database--2004 update.

MITOMAP (http://www.MITOMAP.org), a database for the human mitochondrial genome, has grown rapidly in data content over the past several years as interest in the role of mitochondrial DNA (mtDNA) variation in human origins, forensics, degenerative diseases, cancer and aging has increased dramatically.