DeepAI AI Chat
Log In Sign Up

Rationalizing Medical Relation Prediction from Corpus-level Statistics

05/02/2020
by   Zhen Wang, et al.
Nationwide Children's Hospital
The Ohio State University
0

Nowadays, the interpretability of machine learning models is becoming increasingly important, especially in the medical domain. Aiming to shed some light on how to rationalize medical relation prediction, we present a new interpretable framework inspired by existing theories on how human memory works, e.g., theories of recall and recognition. Given the corpus-level statistics, i.e., a global co-occurrence graph of a clinical text corpus, to predict the relations between two entities, we first recall rich contexts associated with the target entities, and then recognize relational interactions between these contexts to form model rationales, which will contribute to the final prediction. We conduct experiments on a real-world public clinical dataset and show that our framework can not only achieve competitive predictive performance against a comprehensive list of neural baseline models, but also present rationales to justify its prediction. We further collaborate with medical experts deeply to verify the usefulness of our model rationales for clinical decision making.

READ FULL TEXT

page 1

page 2

page 3

page 4

05/17/2018

Classifying medical relations in clinical text via convolutional neural networks

Deep learning research on relation classification has achieved solid per...
05/01/2015

Grounded Discovery of Coordinate Term Relationships between Software Entities

We present an approach for the detection of coordinate-term relationship...
03/29/2020

Named Entities in Medical Case Reports: Corpus and Experiments

We present a new corpus comprising annotations of medical entities in ca...
03/29/2021

IA-GCN: Interpretable Attention based Graph Convolutional Network for Disease prediction

Interpretability in Graph Convolutional Networks (GCNs) has been explore...
11/09/2017

Weakly-supervised Relation Extraction by Pattern-enhanced Embedding Learning

Extracting relations from text corpora is an important task in text mini...
04/26/2018

Open Information Extraction with Global Structure Constraints

Extracting entities and their relations from text is an important task f...
11/14/2022

Learning predictive checklists from continuous medical data

Checklists, while being only recently introduced in the medical domain, ...