ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2021
Clinical Note Section Detection Using a Hidden Markov Model of Unified Medical Language System Semantic Types.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- DR.BENCH: Diagnostic Reasoning Benchmark for Clinical Natural Language Processing.Journal of biomedical informatics · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Clinical notes are a rich source of biomedical data for natural language processing (NLP). The identification of note sections represents a first step in creating portable NLP tools. Here, a system that used a heterogeneous hidden Markov model (HMM) was designed to identify seven note sections: (1) Medical History, (2) Medications, (3) Family and Social History, (4) Physical Exam, (5) Labs and Imaging, (6) Assessment and Plan, and (7) Review of Systems. Unified Medical Language System (UMLS) concepts were identified using MetaMap, and UMLS semantic type distributions for each section type were empirically determined. The UMLS semantic type distributions were used to train the HMM for identifying clinical note sections. The system was evaluated relative to a template boundary model using manually annotated notes from the Medical Information Mart for Intensive Care III. The results show promise for an approach to segment clinical notes into sections for subsequent NLP tasks.
Indexed as
Identifiers
35308919PMC8861726What 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.