Selective prediction for extracting unstructured clinical data - PubMed
pubmed.ncbi.nlm.nih.gov › ...
Dec 22, 2023 · Selective prediction should be considered when abstaining is preferable to making an incorrect prediction.
Selective prediction using cost-based probability thresholding can semi-automate unstructured EHR data extraction by giving “easy” notes to a model and “hard” ...
Nov 18, 2022 · Selective prediction using utility-based probability thresholds can facilitate unstructured data extraction by giving “easy” charts to a model ...
Dec 27, 2022 · This paper aims to determine whether selective prediction, which gives a model the option to abstain from generating a prediction, can improve ...
Sep 30, 2023 · This article aims to determine whether selective prediction, which gives a model the option to abstain from generating a prediction, can improve ...
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What is unstructured clinical data?
Since no prediction model is 100% accurate, it is expected that it will make some mistakes in its decision to include/exclude a patient from the dataset.
This project focuses on revolutionizing the extraction of valuable insights from unstructured clinical data in healthcare. Many existing machine learning ...
The results showed that combining structured and unstructured data improved the accuracy of the prediction of clinical outcomes in ICU patients over time. The ...
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Harnessing Unstructured Data and Hospital Interoperability
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Nov 15, 2024 · NLP plays a key role by extracting insights from clinical notes and other unstructured sources, enhancing patient profiles and improving recruitment efficiency.
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Machine learning (ML) methods enable researchers to extract characteristics from unstructured clinical notes, and represent a more cost-effective and scalable ...