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Natural Language Processing Post-Doctoral Scholar
Center for Artificial Intelligence in Medicine and Imaging
Department of Radiology
The Center for Artificial Intelligence in Medicine and Imaging (AIMI Center) at Stanford University is seeking a post-doctoral scholar to develop novel information extraction methods and tools for diagnostic imaging. The candidate will lead a research program developing machine learning methods and other tools to extract key features from radiology reports and other narrative clinical documents.
The mission of the AIMI Center (http://aimi.stanford.edu) is to develop and evaluate outstanding interdisciplinary machine learning methods that advance how imaging and other clinical information is used to promote health. The center comprises more than 120 Stanford faculty from over 20 academic departments across 3 schools, predominantly the Schools of Medicine and Engineering. Our research projects are collaborations between clinical and engineering scientists, employing massive curated clinical data sets to produce systems that improve healthcare. We work closely with partners across the Stanford campus as well as large technology companies and startups to bring our advances directly to patients.
Our natural language processing laboratory has developed and employed a diverse set of methods, including conventional machine learning , rule-based techniques , and neural architectures [3-6]. We recently released a state-of-the-art neural architecture for analysis of biomedical and clinical data . The successful candidate will work with computer scientists and clinical experts to develop new methods for the analysis of imaging reports and other clinical text, and will have the opportunity to collaborate with our world class image analysis laboratories.
1. Hassanpour S, Langlotz CP. Information extraction from multi-institutional radiology reports. Artif Intell Med. 2015. doi:10.1016/j.artmed.2015.09.007
2. Irvin J, Rajpurkar P, Ko M, Yu Y, Ciurea-Ilcus S. Chexpert: A large chest radiograph dataset with uncertainty labels and expert comparison. Proc Conf AAAI Artif Intell. 2019. Available: https://www.aaai.org/ojs/index.php/AAAI/article/view/3834
3. Wang X, Zhang Y, Ren X, Zhang Y, Zitnik M, Shang J, et al. Cross-type biomedical named entity recognition with deep multi-task learning. Bioinformatics. 2019;35: 1745–1752.
4. Chen MC, Ball RL, Yang L, Moradzadeh N, Chapman BE, Larson DB, et al. Deep Learning to Classify Radiology Free-Text Reports. Radiology. 2017;286: 845–852.
5. Banerjee I, Ling Y, Chen MC, Hasan SA, Langlotz CP, Moradzadeh N, et al. Comparative effectiveness of convolutional neural network (CNN) and recurrent neural network (RNN) architectures for radiology text report classification. Artif Intell Med. 2018. doi:10.1016/j.artmed.2018.11.004
6. Zhang Y, Merck D, Tsai EB, Manning CD, Langlotz CP. Optimizing the Factual Correctness of a Summary: A Study of Summarizing Radiology Reports. arXiv [cs.CL]. 2019. Available: http://arxiv.org/abs/1911.02541
7. Zhang Y, Zhang Y, Qi P, Manning CD, Langlotz CP. Biomedical and Clinical English Model Packages in the Stanza Python NLP Library. arXiv [cs.CL]. 2020. Available: http://arxiv.org/abs/2007.14640
- Applicant must be a U.S. citizen or permanent resident.
- Must have a record of innovative work in natural language processing, computational linguistics, machine learning, and/or medical terminology/ontology.
- We seek motivated individuals who are committed not only to excellence in research, but also to training the next generation of researchers.
- Medical training or experience is desirable but not required.
- Cover letter