Attend to Medical Ontologies: Content Selection for Clinical Abstractive Summarization
- Sajad Sotudeh, Nazli Goharian, Ross W. Filice
- Computer ScienceAnnual Meeting of the Association for…
- 1 May 2020
This paper approaches the content selection problem for clinical abstractive summarization by augmenting salient ontological terms into the summarizer, and shows that this model statistically significantly boosts state-of-the-art results in terms of ROUGE metrics.
Ontology-Aware Clinical Abstractive Summarization
- Sean MacAvaney, Sajad Sotudeh, Arman Cohan, Nazli Goharian, Ish A. Talati, Ross W. Filice
- Computer ScienceAnnual International ACM SIGIR Conference on…
- 14 May 2019
A sequence-to-sequence abstractive summarization model augmented with domain-specific ontological information to enhance content selection and summary generation is proposed and significantly outperforms the current state-of-the-art on this task in terms of rouge scores.
Implementing Machine Learning in Radiology Practice and Research.
- M. Kohli, L. Prevedello, Ross W. Filice, J. R. Geis
- MedicineAJR. American journal of roentgenology
- 26 January 2017
OBJECTIVE The purposes of this article are to describe concepts that radiologists should understand to evaluate machine learning projects, including common algorithms, supervised as opposed to…
Artificial Intelligence Algorithm Improves Radiologist Performance in Skeletal Age Assessment: A Prospective Multicenter Randomized Controlled Trial.
Use of an artificial intelligence algorithm improved skeletal age assessment accuracy and reduced interpretation times for radiologists, although differences were observed between centers.
Automated coronary calcium scoring using deep learning with multicenter external validation
Two deep learning models that automate CAC scoring demonstrating advantages in automated scoring for both dedicated gated coronary CT exams and routine non-gated chest CTs performed for other reasons to allow opportunistic screening are presented.
Improving Radiology Report Quality by Rapidly Notifying Radiologist of Report Errors
A successful automated tool for detecting and notifying radiologists of potential gender and laterality errors, allowing for rapid report correction and reducing the overall rate of report errors is developed.
The Radiologist’s Gaze: Mapping Three-Dimensional Visual Search in Computed Tomography of the Abdomen and Pelvis
- L. Kelahan, A. Fong, J. Blumenthal, Swaminathan Kandaswamy, R. Ratwani, Ross W. Filice
- MedicineJournal of digital imaging
- 5 October 2018
The visual search patterns of radiologists in interpretation of CTs of the abdomen and pelvis are described to better approach future endeavors in determining the effects of manipulations such as fatigue, interruptions, and computer-aided detection.
PathBot: A Radiology-Pathology Correlation Dashboard
A radiology-pathology correlation dashboard is presented that presents radiologists with pathology reports matched to their dictations, for both diagnostic imaging and image-guided procedures and hopes to encourage pathology follow-up in clinical radiology practice for purposes of self-education and to augment peer review.
Integrating an Ontology of Radiology Differential Diagnosis with ICD-10-CM, RadLex, and SNOMED CT
This work sought to map concepts of the Radiology Gamuts Ontology (RGO), an ontology that links diseases and imaging findings to support differential diagnosis in radiology, to terms in three key vocabularies for clinical radiology: the International Classification of Diseases, version 10, Clinical Modification, and the Radiological Society of North America's radiology lexicon.
Effectiveness of Deep Learning Algorithms to Determine Laterality in Radiographs
Deep learning models can classify radiographs by laterality with high accuracy and may be applied in a variety of settings that could improve patient safety and radiologist satisfaction.