云帆加速器ios

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

云帆加速器ios

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. 2022;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. 2022;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. 2022. 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. 2022;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). 2022. doi:10.1093/bioinformatics/btaa669.