The Laboratories in the Children's Hospital Informatics Program span a wide range of research interests in bioinformatics and clinical informatics. Our goal is to make significant contributions to biomedical research and patient care by understanding and utilizing various types of genomic and proteomic data and by developing innovative hardware and software technologies.
Directed by Ken Mandl, the IHL studies the application of medical informatics, computer science, epidemiology, and biostatistics to improve population health, research, and clinical practice. In addition to Mandl's own group, the lab has the Computational Epidemiology Group and the Predictive Medicine Group.
The Computational Epidemiology Group aims to evolve the traditional paradigm of public health practice and surveillance through innovative multi-disciplinary epidemiologic research. The overall goal is to show how emerging technologies can help clarify patterns of disease and promote public health. Our mission has materialized in a diverse set of projects that include describing the emergence of West Nile virus in New York City using satellite data, predicting patterns of Lyme disease based on climate change, analyzing patterns of influenza epidemics, finding new ways to identify problem medications using electronic medical records, understanding the geographic patterns of substance abuse, describing the impact of pollution on chronic disease, and most recently the first documented use of mobile smartphones as public health surveillance tools for both outbreak and post-marketing surveillance.
We are a bioinformatics group interested in understanding chromatin structure and function in a variety of systems using high-throughput sequencing techniques. We specialize in analysis of ChIP-seq and nucleosome profiling data but also work with RNA-seq, whole-genome sequencing, and other data types. We collaborate with a number of experimental labs, both in the Harvard Medical area and around the world.
The Predictive Medicine Group works to realize the vision of predictive medicine and public health. The diverse group of researchers, clinicians, mathematicians, computer scientists, and biologists develop advanced predictive models for a wide range of applications, including clinical risk prediction, predictive pharmacovigilance, health related social networks, and real-time public health surveillance.
The Biomedical Cybernetics Laboratory is an interdisciplinary program of the Harvard Medical School - Partners Healthcare Center for Personalized Genetic Medicine, affiliated with the Children's Hospital Informatics Program and the Harvard-MIT Division of Health Sciences and Technology. Our laboratory brings together researchers from computer science, engineering, artificial intelligence, epidemiology and statistics to develop novel methods for the integrated analysis of biomedical systems.
The Natural Language Processing Laboratory's mission is to develop and implement Natural Language Processing (NLP) technologies to apply to the electronic medical record. These technologies include core NLP tasks such as relation extraction, coreference resolution, and parsing, and make use of statistical machine learning methods. In order to use many machine learning methods, manually labeled (annotated) domain- and task-specific data is required. To that end, we are heavily involved in many different clinical document annotation projects. Since manual annotation is a time-consuming, painstaking, expensive process, it is also our goal to develop and use algorithms that minimize the required amount of labeled data required while maximizing the use of existing labeled data. Use cases for clinical NLP include automated phenotyping, cohort identification, and clinical question answering. We are a primary contributor to the end-to-end software system Apache cTAKES (clinical Text And Knowledge Extraction System).