ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2018
What's in a Note? Unpacking Predictive Value in Clinical Note Representations.
Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05176769 (The Usefulness of Artificial Intelligence for Automated Extraction and Processing of Clinical Data From Electronic Medical Records), which is not on this map. Cited by 15 papers.
What it found
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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.
The Usefulness of Artificial Intelligence for Automated Extraction and Processing of Clinical Data From Electronic Medical Records (CardioMining-AI)
Who cites it
15 citing papers in PubMed.
- Automated Prediction of Glasgow Coma Scale Scores From Unstructured Electronic Health Records Using Natural Language Processing: Development and Validation Study.Journal of medical Internet research · 2026Article
- Article
- Assessing Physician and Patient Agreement on Whether Patient Outcomes Captured in Clinical Progress Notes Reflect Treatment Success: Cross-Sectional Study.Journal of participatory medicine · 2025Article
- Using artificial intelligence to develop a measure of orthopaedic treatment success from clinical notes.Frontiers in digital health · 2025Article
- De-identification is not enough: a comparison between de-identified and synthetic clinical notes.Scientific reports · 2024Article
- Research on Multimodal Fusion of Temporal Electronic Medical Records.Bioengineering (Basel, Switzerland) · 2024Article
- Lexical stability of psychiatric clinical notes from electronic health records over a decade.Acta neuropsychiatrica · 2023Article
- Early detection of colorectal cancer by leveraging Dutch primary care consultation notes with free text embeddings.Scientific reports · 2023Article
- Article
- A prediction model with measured sentiment scores for the risk of in-hospital mortality in acute pancreatitis: a retrospective cohort study.Annals of translational medicine · 2022Article
- Word embeddings trained on published case reports are lightweight, effective for clinical tasks, and free of protected health information.Journal of biomedical informatics · 2022Article
- Machine learning applications for therapeutic tasks with genomics data.Patterns (New York, N.Y.) · 2021Review
- CATAN: Chart-aware temporal attention network for adverse outcome prediction.Proceedings. IEEE International Conference on Healthcare Informatics · 2021Article
- Combining structured and unstructured data for predictive models: a deep learning approach.BMC medical informatics and decision making · 2020Article
- Automated Survival Prediction in Metastatic Cancer Patients Using High-Dimensional Electronic Medical Record Data.Journal of the National Cancer Institute · 2019Article
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
Electronic Health Records (EHRs) have seen a rapid increase in adoption during the last decade. The narrative prose contained in clinical notes is unstructured and unlocking its full potential has proved challenging. Many studies incorporating clinical notes have applied simple information extraction models to build representations that enhance a downstream clinical prediction task, such as mortality or readmission. Improved predictive performance suggests a "good" representation. However, these extrinsic evaluations are blind to most of the insight contained in the notes. In order to better understand the power of expressive clinical prose, we investigate both intrinsic and extrinsic methods for understanding several common note representations. To ensure replicability and to support the clinical modeling community, we run all experiments on publicly-available data and provide our code.
Identifiers
29888035PMC5961801What 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.