Posts from August 2014

Since the human genome was published in 2001, we have been talking about the potential application of this knowledge to personalized medicine, and in the last couple of years, we seem at last to be approaching this goal.

A better understanding of the molecular basis of diseases is key to development of personalized medicine across pharmaceutical R&D, as was discussed last year by Janet Woodcock, Director of the FDA’s Center for Drug Evaluation and Research (CDER).

FDA CDER has been urging adoption of pharmacogenomics strategies and pursuit of targeted therapies for a variety of reasons. These include the potential for decreasing the variability of response, improving safety, and increasing the size of treatment effect, by stratifying patient populations.

Pharmacogenomics is the study of the role an individual’s genome plays in drug response, which can vary from  adverse drug reactions to lack of therapeutic efficacy. With the recent explosion in sequence data from next generation sequencing (NGS) technologies, one of the bottlenecks in application of genomic variation data to understanding disease is access to annotation.

From NGS workflows, scientists can quickly identify long lists of candidate genes that differ between two conditions (case-control, or family hierarchies, for example). Gene annotations are essential to interpret these gene lists and to discover fundamental properties like gene function and disease relevance.


Rehospitalization is a serious problem in medicine.

Medical aspects are complicated by end of life care issues as well as a regulatory environment in which hospitals can experience financial penalties for "excess" rehospitalization rates. Existing rehospitalization predictive models, most of which are based on administrative data, have poor statistical performance, as do models that employ limited physiologic data.

At Linguamatics' upcoming seminar in San Francisco, Dr. Escobar will present work on a new rehospitalization model that employs data from a comprehensive electronic medical record and which could be instantiated in real time.

He will also present a "road map" to explain how data from natural language processing can be incorporated into this model as well as on future strategies for instantiation of NLP engines into routine clinical operations.

Dr. Escobar is a research scientist at the Kaiser Permanente Division of Research in Oakland as well as being the Regional Director for Hospital Operations Research for Kaiser Permanente Northern California.

An expert on risk adjustment and predictive modeling, Dr. Escobar has published over 130 peer-reviewed articles and is currently in the middle of deploying a real-time early warning system for deterioration outside the intensive care unit at two Kaiser Permanente hospitals.