Hierarchical attention networks for information extraction from cancer pathology reports

Abstract

HAN-based DL models show promise in information abstraction tasks within unstructured clinical pathology reports.

Deep Bidirectional Recurrent Neural Networks as End-To-End Models for Smoking Status Extraction from Clinical Notes in Spanish

“…Our work has several limitations. Among them, we use a set of bag-of-words models as baseline models since it is part of our usual workflow, however other alternatives such as conditional random fields or other state-of-the-art models could be excellent baseline models as well, as proposed by other authors [33,42,44,45,47,48] . Also, we only evaluated the models in a classification problem with only a few classification categories.…”

“…Also, we only evaluated the models in a classification problem with only a few classification categories. However, other teams have had very good results even with more categories and fewer observations [42,44] .…”

“…They found improvements in accuracy, recall and F1 score vs. the base system. Other applications of RNNs in medical texts have explored their performance in tasks such as deidentification of clinical notes [33] , information extraction [44,45] , named entity recognition [46] , relation extraction [47] and text classification [48] with superior results to the reference models. However, we find did not find any articles that assessed the capacity of these models to classify clinical notes in Spanish.…”

A frame semantic overview of NLP-based information extraction for cancer-related EHR notes

“…Frame Elements References CODE TERMINOLOGY: clinical terminology used for the procedure mention [52] CODE VALUE: value of the terminology code [52], [1] INSTITUTION: institution where the procedure was performed [52] NEGATION: whether the mention is negated [52], [77] MENTION: words related to any procedure term (e.g. flex sig, guaiac card) [75], [52], [15], [84], [77], [43] MARGIN: usually the rim of normal tissue taken removed during or after procedure (surgical margin) [52] ANATOMICAL SITE: part of body procedure targets (e.g., breast) [79], [52] TEMPORAL INFORMATION: time and date descriptors (e.g., "colonoscopy in 2005", "flexible sigmoidoscopy 5 years ago), date of completion [52], [15], [1], [10] STATUS: procedure or treatment status (e.g., refused, declined, scheduled, planned, completed, reported vs not reported) [15], [42], [1], [82] MODIFIER: negation and other modifiers that change the status of procedure (e.g., "no", "never") [15] TUMOR DESCRIPTION ANATOMICAL SITE: anatomic locations (e.g., "segment 5" or "left lobe") with attributes (Liver, Non Liver), target location (e.g., liver and segment #7) as well as non-target location (e.g., breast) [13], [8], [52], [85], [42], [25], [9], [23], [26], [41] LATERALITY: side of a paired organ associated with origin of the primary tumor [25] TYPE: primary/metastatic [25] STATUS: benign or malignancy status along with diagno...…”

“…GRADE: appearance of the cancerous cells [8], [52], [9], [48], [87], [26] INVASION: whether or not more than 50% of an organ is invaded [52], [88] SIZE: quantitative size of tumor (e.g., 2.2 x 2.0 cm), diameter/volume of the tumor, including unit (e.g., 1 cm, 0.3 x 0.5 x 0.7 cm) [13], [52], [88], [85], [42], [25], [48], [41] SIZE TYPE: radiological/pathological [25] NEGATION: indicator to some negation of a tumor reference (e.g., no) [85], [86], [41] COUNT: number of tumor/nodule references (e.g., two or multiple) [13], [88], [85] TUMOR REFERENCE: a radiologic artifact that may reference a tumor (e.g., lesion or focal density) [88], [85] MENTION: tumor major object (e.g., tumor, lesion, mass, and nodule) [13], [42], [47], [49], [41] QUANTIFIER: one, two, three, several [42] TEMPORAL INFORMATION: refers to information about time (e.g., year, month, and date, 2007-08-04) [52], [42], [86] NON-TUMOR SIZE ITEMS: LeVeen needle, which is used in RFA treatment [42] STATUS: this indicates the final overall tumor status (e.g., regression, stable, progression, irrelevant) [89], [41] METASTATIC STATUS INDICATORS: phrases denoting a metastatic tumor [9], [90] MAGNI...…”