Use of unstructured text in prognostic clinical prediction models: a systematic review
Objective This systematic review aims to assess how information from unstructured text is
used to develop and validate clinical prognostic prediction models. We summarize the …
used to develop and validate clinical prognostic prediction models. We summarize the …
Clinicalbert: Modeling clinical notes and predicting hospital readmission
Clinical notes contain information about patients that goes beyond structured data like lab
values and medications. However, clinical notes have been underused relative to structured …
values and medications. However, clinical notes have been underused relative to structured …
Combining structured and unstructured data for predictive models: a deep learning approach
Background The broad adoption of electronic health records (EHRs) provides great
opportunities to conduct health care research and solve various clinical problems in …
opportunities to conduct health care research and solve various clinical problems in …
Machine learning applications for therapeutic tasks with genomics data
Thanks to the increasing availability of genomics and other biomedical data, many machine
learning algorithms have been proposed for a wide range of therapeutic discovery and …
learning algorithms have been proposed for a wide range of therapeutic discovery and …
Write it like you see it: Detectable differences in clinical notes by race lead to differential model recommendations
Clinical notes are becoming an increasingly important data source for machine learning
(ML) applications in healthcare. Prior research has shown that deploying ML models can …
(ML) applications in healthcare. Prior research has shown that deploying ML models can …
Attention-based multimodal fusion with contrast for robust clinical prediction in the face of missing modalities
Objective: With the increasing amount and growing variety of healthcare data, multimodal
machine learning supporting integrated modeling of structured and unstructured data is an …
machine learning supporting integrated modeling of structured and unstructured data is an …
Clinical prompt learning with frozen language models
When the first transformer-based language models were published in the late 2010s,
pretraining with general text and then fine-tuning the model on a task-specific dataset often …
pretraining with general text and then fine-tuning the model on a task-specific dataset often …
Clinical outcome prediction from admission notes using self-supervised knowledge integration
Outcome prediction from clinical text can prevent doctors from overlooking possible risks
and help hospitals to plan capacities. We simulate patients at admission time, when decision …
and help hospitals to plan capacities. We simulate patients at admission time, when decision …
Fineehr: Refine clinical note representations to improve mortality prediction
Monitoring the health status of patients in the Intensive Care Unit (ICU) is a critical aspect of
providing superior care and treatment. The availability of large-scale electronic health …
providing superior care and treatment. The availability of large-scale electronic health …
[HTML][HTML] “Note Bloat” impacts deep learning-based NLP models for clinical prediction tasks
One unintended consequence of the Electronic Health Records (EHR) implementation is the
overuse of content-importing technology, such as copy-and-paste, that creates “bloated” …
overuse of content-importing technology, such as copy-and-paste, that creates “bloated” …