ArticleBMC medical informatics and decision making2019
Family history information extraction via deep joint learning.
Article in BMC medical informatics and decision making, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed.
- ACR-SA: attention-based deep model through two-channel CNN and Bi-RNN for sentiment analysis.PeerJ. Computer science · 2022Article
- MT-clinical BERT: scaling clinical information extraction with multitask learning.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- Extracting Family History Information From Electronic Health Records: Natural Language Processing Analysis.JMIR medical informatics · 2021Article
- A Hybrid Model for Family History Information Identification and Relation Extraction: Development and Evaluation of an End-to-End Information Extraction System.JMIR medical informatics · 2021Article
- Novel Graph-Based Model With Biaffine Attention for Family History Extraction From Clinical Text: Modeling Study.JMIR medical informatics · 2021Article
- Natural language processing to measure the frequency and mode of communication between healthcare professionals and family members of critically ill patients.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- Extraction of Family History Information From Clinical Notes: Deep Learning and Heuristics Approach.JMIR medical informatics · 2020Article
- Extracting Family History of Patients From Clinical Narratives: Exploring an End-to-End Solution With Deep Learning Models.JMIR medical informatics · 2020Article
- Family History Information Extraction With Neural Attention and an Enhanced Relation-Side Scheme: Algorithm Development and Validation.JMIR medical informatics · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundFamily history (FH) information, including family members, side of family of family members (i.e., maternal or paternal), living status of family members, observations (diseases) of family members, etc., is very important in the decision-making process of disorder diagnosis and treatment. However FH information cannot be used directly by computers as it is always embedded in unstructured text in electronic health records (EHRs). In order to extract FH information form clinical text, there is a need of natural language processing (NLP). In the BioCreative/OHNLP2018 challenge, there is a task regarding FH extraction (i.e., task1), including two subtasks: (1) entity identification, identifying family members and their observations (diseases) mentioned in clinical text; (2) family history extraction, extracting side of family of family members, living status of family members, and observations of family members. For this task, we propose a system based on deep joint learning methods to extract FH information. Our system achieves the highest F1- scores of 0.8901 on subtask1 and 0.6359 on subtask2, respectively.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.