Inferring Which Medical Treatments Work from Reports of Clinical Trials
@article{Lehman2019InferringWM, title={Inferring Which Medical Treatments Work from Reports of Clinical Trials}, author={Eric P. Lehman and Jay DeYoung and Regina Barzilay and Byron C. Wallace}, journal={ArXiv}, year={2019}, volume={abs/1904.01606} }
How do we know if a particular medical treatment actually works? Ideally one would consult all available evidence from relevant clinical trials. Unfortunately, such results are primarily disseminated in natural language scientific articles, imposing substantial burden on those trying to make sense of them. In this paper, we present a new task and corpus for making this unstructured published scientific evidence actionable. The task entails inferring reported findings from a full-text article…
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References
SHOWING 1-10 OF 29 REFERENCES
Automatic Summarization of Results from Clinical Trials
- Computer Science2011 IEEE International Conference on Bioinformatics and Biomedicine
- 2011
A novel method for automatically creating EBM-oriented summaries from research abstracts of randomly-controlled trials (RCTs) is presented, which extracts descriptions of the treatment groups and outcomes, as well as various associated quantities, and then calculates summary statistics.
Show Me Your Evidence - an Automatic Method for Context Dependent Evidence Detection
- Computer ScienceEMNLP
- 2015
This work proposes the task of automatically detecting evidences from unstructured text that support a given claim and suggests a system architecture based on supervised learning to address the evidence detection task.
ExaCT: automatic extraction of clinical trial characteristics from journal publications
- Computer ScienceBMC Medical Informatics Decis. Mak.
- 2010
An automatic information extraction system that assists users with locating and extracting key trial characteristics from full-text journal articles reporting on randomized controlled trials (RCTs) and can be extended to handle other characteristics and document types.
BioCause: Annotating and analysing causality in the biomedical domain
- Computer Science, BiologyBMC Bioinformatics
- 2012
Augmenting named entity and event annotations with information about causal discourse relations could benefit the development of more sophisticated IE systems and further influence theDevelopment of multiple tasks, such as enabling textual inference to detect entailments, discovering new facts and providing new hypotheses for experimental work.
Machine learning for identifying Randomized Controlled Trials: An evaluation and practitioner's guide
- Computer ScienceResearch synthesis methods
- 2018
This work trained and optimized support vector machine and convolutional neural network models on the titles and abstracts of the Cochrane Crowd RCT set and evaluated the models on an external dataset (Clinical Hedges), allowing direct comparison with traditional database search filters.
Identifying Comparative Claim Sentences in Full-Text Scientific Articles
- Computer ScienceACL 2012
- 2012
A set of semantic and syntactic features that characterize a sentence are introduced and then it is demonstrated how those features can be used in three different classifiers: Naive Bayes (NB), a Support Vector Machine (SVM) and a Bayesian network (BN).
Distributional Semantics Resources for Biomedical Text Processing
- Computer Science
- 2013
This study introduces the first set of such language resources created from analysis of the entire available biomedical literature, including a dataset of all 1to 5-grams and their probabilities in these texts and new models of word semantics.
Question Answering in Webclopedia
- EducationTREC
- 2000
The QA Typology contains 94 nodes, of which 47 are leaf nodes; each Typology node has been annotated with examples and typical patterns of expression of both Question and Answer, as indicated in Figure 3.
A large annotated corpus for learning natural language inference
- Computer ScienceEMNLP
- 2015
The Stanford Natural Language Inference corpus is introduced, a new, freely available collection of labeled sentence pairs, written by humans doing a novel grounded task based on image captioning, which allows a neural network-based model to perform competitively on natural language inference benchmarks for the first time.
Coarse-to-Fine Question Answering for Long Documents
- Computer ScienceACL
- 2017
A framework for question answering that can efficiently scale to longer documents while maintaining or even improving performance of state-of-the-art models is presented and sentence selection is treated as a latent variable trained jointly from the answer only using reinforcement learning.