Topic
Clinical NLP research, explained
10 open-access clinical nlp studies, each with a flashcard deck and a quiz.
- Can deep learning use a whole hospital record to predict outcomes?
Neural networks reading a patient's entire raw hospital record predicted death, readmission and long stays more accurately than standard clinical scores.
- Can language models find social needs hidden in doctors' notes?
Fine-tuned language models found patients' social problems such as housing or transport trouble in clinic notes far more often than billing codes did, and were less swayed by race or gender wording than ChatGPT.
- Can ChatGPT turn messy pathology reports into clean data?
With carefully engineered prompts, ChatGPT extracted cancer stage and tumour type from pathology reports with about 89% average accuracy, beating older NLP methods, but it misapplied staging rules and invented answers for blank reports.
- Can better prompts make GPT good at spotting medical terms?
Carefully built prompts made GPT-4 nearly as good as a fine-tuned clinical model at loosely matching medical terms, but it still fell clearly short on exact boundaries.
- Does pretraining BERT on hospital notes help it link medical terms?
BERT-style models beat older rule- and dictionary-based tools at mapping medical phrases to standard terms, and pretraining on closer-domain text helped, though the gains between BERT variants were small.
- Can a local, private LLM pull symptoms out of medical notes?
An open language model running inside the hospital could reliably tag symptoms in doctors' notes, with the largest version doing best and struggling most with vaguely described symptoms.
- Can an off-the-shelf language model spot postpartum bleeding in notes?
Without any task-specific training, an open-source language model extracted bleeding-related facts from hospital notes well enough to find many postpartum hemorrhage cases that billing codes missed.
- Can language models read seizure counts from doctors' notes?
Fine-tuned language models pulled seizure frequency and last-seizure dates from clinic notes about as well as human annotators, and only needed a small labelled set.
- Can smarter retrieval help LLMs read long clinical notes?
Retrieving only the passages around clinically relevant terms let language models extract patient information slightly more accurately while using far less text and time than feeding whole notes or embedding-based chunks.
- Can AI read doctors' notes to track failing hip implants?
Training a text model on labels produced by simple hand-written rules, rather than costly hand annotation, let it find far more hip-implant problems in doctors' notes than billing codes reveal.