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The Boston Children's Hospital Computational Health Informatics Program (CHIP), founded in 1994, is a multidisciplinary applied research and education program. Our faculty advance the science of biomedical informatics for molecular characterization of the patient, gene discovery, medical decision making, diagnosis, therapeutic selection, care redesign, public health management, population health, and re-imagined clinical trials. Biomedical informatics has become a major theme and methodology for biomedical science, health care delivery, and population health, involving high-dimensional modeling and understanding of patients from the molecular to the population levels. The field is inherently interdisciplinary, drawing on traditional biomedical disciplines, the science and technology of computing, data science, biostatistics, epidemiology, decision theory, omics, implementation science, and health care policy and management. CHIP faculty are trained in medicine, data science, computer science, mathematics and epidemiology. Though CHIP has a robust pediatric research agenda, our contributions span across all ages.
CHIP innovations have transformed the landscape of big data in health care, the use of AI in clinical decision making, and the IT strategies of commercial enterprises. Our discoveries and inventions have been widely adopted by technology companies such as: Apple, Microsoft, and Google.
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Research areas
Artificial Intelligence
Federated Data Sharing
Genomic Medicine
Health Information Technology
蓝湖:2021-6-11 · 蓝湖
Population Health and Surveillance
Regulatory Science
Scientific Reproducibility and Metascience
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Tweets by @Bos_CHIP
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JAMIA open
Experiences implementing scalable, containerized, cloud-based NLP for extracting biobank participant phenotypes at scale.
Miller TA, Avillach P, Mandl KD. Experiences implementing scalable, containerized, cloud-based NLP for extracting biobank participant phenotypes at scale. JAMIA open. 2023;3(2):185-189. doi:10.1093/jamiaopen/ooaa016.
JAMIA open
Rethinking domain adaptation for machine learning over clinical language.
Laparra E, Bethard S, Miller TA. Rethinking domain adaptation for machine learning over clinical language. JAMIA open. 2023;3(2):146-150. doi:10.1093/jamiaopen/ooaa010.
Journal of medical Internet research
Real-time forecasting of the COVID-19 outbreak in Chinese provinces: Machine learning approach using novel digital data and estimates from mechanistic models.
Poirier C, Liu D, Clemente L, et al. Real-time forecasting of the COVID-19 outbreak in Chinese provinces: Machine learning approach using novel digital data and estimates from mechanistic models. Journal of medical Internet research. 2023. doi:10.2196/20285.
GigaScience
EHRtemporalVariability: delineating temporal data-set shifts in electronic health records.
Sáez C, Gutiérrez-Sacristán A, Kohane I, García-Gómez JM, Avillach P. EHRtemporalVariability: delineating temporal data-set shifts in electronic health records. GigaScience. 2023;9(8). doi:10.1093/gigascience/giaa079.
Bioinformatics (Oxford, England)
netAE: Semi-supervised dimensionality reduction of single-cell RNA sequencing to facilitate cell labeling.
Dong Z, Alterovitz G. netAE: Semi-supervised dimensionality reduction of single-cell RNA sequencing to facilitate cell labeling. Bioinformatics (Oxford, England). 2023. doi:10.1093/bioinformatics/btaa669.
CHIP publication library
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Retracted COVID-19 studies expose holes in vetting of data firms
¿Seremos un posible contagiado para internet?
Trump’s administration’s ‘Warp Speed’ plan gets this right about speeding up a Covid-19 vaccine
What businesses can learn from a coronavirus-tracking breakthrough
The Science of COVID-19
CHIP press gallery
