14 research outputs found
Unsupervised patient representations from clinical notes with interpretable classification decisions
We have two main contributions in this work: 1. We explore the usage of a
stacked denoising autoencoder, and a paragraph vector model to learn
task-independent dense patient representations directly from clinical notes. We
evaluate these representations by using them as features in multiple supervised
setups, and compare their performance with those of sparse representations. 2.
To understand and interpret the representations, we explore the best encoded
features within the patient representations obtained from the autoencoder
model. Further, we calculate the significance of the input features of the
trained classifiers when we use these pretrained representations as input.Comment: Accepted poster at NIPS 2017 Workshop on Machine Learning for Health
(https://ml4health.github.io/2017/
Extracting detailed oncologic history and treatment plan from medical oncology notes with large language models
Both medical care and observational studies in oncology require a thorough
understanding of a patient's disease progression and treatment history, often
elaborately documented in clinical notes. Despite their vital role, no current
oncology information representation and annotation schema fully encapsulates
the diversity of information recorded within these notes. Although large
language models (LLMs) have recently exhibited impressive performance on
various medical natural language processing tasks, due to the current lack of
comprehensively annotated oncology datasets, an extensive evaluation of LLMs in
extracting and reasoning with the complex rhetoric in oncology notes remains
understudied. We developed a detailed schema for annotating textual oncology
information, encompassing patient characteristics, tumor characteristics,
tests, treatments, and temporality. Using a corpus of 10 de-identified breast
cancer progress notes at University of California, San Francisco, we applied
this schema to assess the abilities of three recently-released LLMs (GPT-4,
GPT-3.5-turbo, and FLAN-UL2) to perform zero-shot extraction of detailed
oncological history from two narrative sections of clinical progress notes. Our
team annotated 2750 entities, 2874 modifiers, and 1623 relationships. The GPT-4
model exhibited overall best performance, with an average BLEU score of 0.69,
an average ROUGE score of 0.72, and an average accuracy of 67% on complex tasks
(expert manual evaluation). Notably, it was proficient in tumor characteristic
and medication extraction, and demonstrated superior performance in inferring
symptoms due to cancer and considerations of future medications. The analysis
demonstrates that GPT-4 is potentially already usable to extract important
facts from cancer progress notes needed for clinical research, complex
population management, and documenting quality patient care.Comment: Source code available at:
https://github.com/MadhumitaSushil/OncLLMExtractio
"Hadron Structure and QCD - from low to high energies"
Understanding the behavior of a trained network and finding explanations for
its outputs is important for improving the network's performance and
generalization ability, and for ensuring trust in automated systems. Several
approaches have previously been proposed to identify and visualize the most
important features by analyzing a trained network. However, the relations
between different features and classes are lost in most cases. We propose a
technique to induce sets of if-then-else rules that capture these relations to
globally explain the predictions of a network. We first calculate the
importance of the features in the trained network. We then weigh the original
inputs with these feature importance scores, simplify the transformed input
space, and finally fit a rule induction model to explain the model predictions.
We find that the output rule-sets can explain the predictions of a neural
network trained for 4-class text classification from the 20 newsgroups dataset
to a macro-averaged F-score of 0.80. We make the code available at
https://github.com/clips/interpret_with_rules.Comment: Accepted at the Workshop on 'Analyzing and interpreting neural
networks for NLP' (BlackboxNLP), EMNLP 201