Machine Intelligence Lab
The Machine Intelligence Lab at the Boston Children’s Hospital Computational Health Informatics Program has a multidisciplinary research agenda. Our research involves the conception and implementation of machine intelligence analytics tools, capable of predicting unobserved events in healthcare in the immediate or near future. Our work ranges from tracking disease outbreaks around the Globe, leveraging information from big data sets from Internet-based services (such as Google, Twitter, Weather, Human Mobility, Electronic Health Records), to bed-side patient-centered monitoring approaches aimed at improving care in clinical settings.
Natural Language Processing Laboratory
Our 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.
Translational Omics Medicine Lab
Dr. Kong's lab, in the Computational Health Informatics Program at Boston Children's Hospital (http://chip.org), an affiliate of Harvard Medical School and a collaborating program of its Department of Biomedical Informatics, is recruiting both research fellows and technicians. To apply, please send CV and letters of recommendation to email@example.com.
The real value in biomedical research lies not in the scale of any single source of data, but in the ability to integrate and interrogate multiple, complementary datasets simultaneously.
Our investigation focus is in translational bioinformatics, specifically in integrating multiple heterogeneous sources of clinical and genomics data in a meaningful way. We are passionate about combining data across different scales and resolutions to enable new perspectives for essential biomedical questions.
Our research focuses on the development of novel methods and techniques for the integration of biological, clinical cohorts and Electronic Health Records to encompass biological observations.
Computational Epidemiology Lab
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.
Predictive Medicine Group
The Predictive Medicine Group at Harvard Medical School works to develop novel approaches for predicting human health. Our diverse group of researchers, clinicians, mathematicians, computer scientists and biologists develop advanced predictive models for a wide range of applications, including disease risk prediction, predictive pharmacovigilance, predictive health system dynamics and real-time public health surveillance.