Uncovering clinical evidence in COVID19 publications: An integrated search via text & images The proposed research aims to develop and advance tools for using image-data appearing in scientific publications, in addition to text, in order to expedite effective access to COVID-19 published information. Current efforts aiming to address the COVID-19 pandemic include devising treatment, understanding virus mechanisms, detecting infection and antibodies, and ultimately ? developing a vaccine. All these efforts require effective access to biomedical information related to the virus. The Allen Institute has recently released the CORD-19 dataset ? a large, continually updated collection of scientific literature pertaining to COVID-19 and Corona viruses. This dataset comprises tens of thousands full text articles, forming a basis for text-mining tools that will support access to information pertaining to COVID-19. Notably, much of the evidence within these publications is provided in the form of figures. Furthermore, regions where such evidential images occur are rich in information. While biomedical text-based mining tools are being quickly developed and offered for accessing this dataset, images, which contain key clinical and biological information, are not considered. Even outside the COVID-19 realm, little has been done so far to utilize images within publications, despite the fact that they provide important cues about the relevance of the information embedded in articles. Our premise, which is supported by our own and by other informaticians and clinicians experience, is that information derived from images can (and should) be directly incorporated into the biomedical ? and specifically into the COVID-19 ? document retrieval and extraction. Doing so will improve accurate access to relevant articles, while pin-pointing significant evidence within them, and expediting access to much-needed critical information. The work on this project will result in methods and tools that take advantage of both image- and text-data, facilitating more effective and focused retrieval and mining, thus better supporting speedy data- intensive discovery in the context of COVID-19.
Uncovering clinical evidence in COVID19 publications: An integrated search via text & images Researchers and physicians looking to understand, treat and ultimately cure and vaccinate against the elusive and devastating COVID19, must search through vast amounts of published biomedical information. The proposed research aims to support and speed-up the search while improving effective access to the most relevant part of the COVID19 literature, by creating a tool that utilizes and searches for the highly-informative image data within publications. The successful outcome of this work will provide a well-targeted, effective search engine for finding information pertinent to the medical and research needs of scientists and physicians working to address COVID19, thus expediting discovery, and revealing potential complications, and their causes and their likely treatment.
| Li, Pengyuan; Jiang, Xiangying; Kambhamettu, Chandra et al. (2018) Compound image segmentation of published biomedical figures. Bioinformatics 34:1192-1199 |
| Elhalawani, Hesham; Lin, Timothy A; Volpe, Stefania et al. (2018) Machine Learning Applications in Head and Neck Radiation Oncology: Lessons From Open-Source Radiomics Challenges. Front Oncol 8:294 |
