16 research outputs found
Building an Ontology for Health Dialogs with Virtual Health Agents
Virtual Health Agents (VHA) are human-like autonomous intelligent agents built using articial intelligence techniques, specically designed to deliver health interventions that assist patients. By asking the patient questions about their lifestyle, they can infer whether the individual at risk, and if so, provide them with potential plans to choose from in order to change. Virtual health agents promise to revolutionize the way healthcare is delivered and provide access to health interventions for the underserved population. Download file for full abstrac
REflex: Flexible Framework for Relation Extraction in Multiple Domains
Systematic comparison of methods for relation extraction (RE) is difficult
because many experiments in the field are not described precisely enough to be
completely reproducible and many papers fail to report ablation studies that
would highlight the relative contributions of their various combined
techniques. In this work, we build a unifying framework for RE, applying this
on three highly used datasets (from the general, biomedical and clinical
domains) with the ability to be extendable to new datasets. By performing a
systematic exploration of modeling, pre-processing and training methodologies,
we find that choices of pre-processing are a large contributor performance and
that omission of such information can further hinder fair comparison. Other
insights from our exploration allow us to provide recommendations for future
research in this area.Comment: accepted by BioNLP 2019 at the Association of Computation Linguistics
201
How Good Is NLP? A Sober Look at NLP Tasks through the Lens of Social Impact
Recent years have seen many breakthroughs in natural language processing
(NLP), transitioning it from a mostly theoretical field to one with many
real-world applications. Noting the rising number of applications of other
machine learning and AI techniques with pervasive societal impact, we
anticipate the rising importance of developing NLP technologies for social
good. Inspired by theories in moral philosophy and global priorities research,
we aim to promote a guideline for social good in the context of NLP. We lay the
foundations via the moral philosophy definition of social good, propose a
framework to evaluate the direct and indirect real-world impact of NLP tasks,
and adopt the methodology of global priorities research to identify priority
causes for NLP research. Finally, we use our theoretical framework to provide
some practical guidelines for future NLP research for social good. Our data and
code are available at http://github.com/zhijing-jin/nlp4sg_acl2021. In
addition, we curate a list of papers and resources on NLP for social good at
https://github.com/zhijing-jin/NLP4SocialGood_Papers.Comment: Findings of ACL 2021; also accepted at the NLP for Positive Impact
workshop@ACL 202
Bidirectional Captioning for Clinically Accurate and Interpretable Models
Vision-language pretraining has been shown to produce high-quality visual
encoders which transfer efficiently to downstream computer vision tasks. While
generative language models have gained widespread attention, image captioning
has thus far been mostly overlooked as a form of cross-modal pretraining in
favor of contrastive learning, especially in medical image analysis. In this
paper, we experiment with bidirectional captioning of radiology reports as a
form of pretraining and compare the quality and utility of learned embeddings
with those from contrastive pretraining methods. We optimize a CNN encoder,
transformer decoder architecture named RadTex for the radiology domain. Results
show that not only does captioning pretraining yield visual encoders that are
competitive with contrastive pretraining (CheXpert competition multi-label AUC
of 89.4%), but also that our transformer decoder is capable of generating
clinically relevant reports (captioning macro-F1 score of 0.349 using CheXpert
labeler) and responding to prompts with targeted, interactive outputs.Comment: 12 pages, 7 figures. Code release to follo
Modeling Occupant-Building-Appliance Interaction for Energy Waste Analysis
AbstractThe objective of this paper is to discover the emergent energy performance and determinants of energy waste in buildings. Electricity consumption in the U.S. attributes to 73% of energy waste in buildings and much of this waste is due to improper design, operation, and use of appliances. In particular, the operation or use phase of buildings and the way occupants behave significantly contribute to energy waste. Understanding the determinants of energy waste during the operation phase of buildings is a challenging task due to the complex interactions between the occupants, building units, and appliances. To decode these complex interactions and facilitate a better understanding of the determinants of energy waste, a simulation approach is used in this study. An agent-based simulation model was developed to capture the diverse attributes and dynamic behaviors of building occupants at the interface of human-building-appliance interactions. The application of the proposed model is demonstrated in a case study. Using simulation experiments, the interactions between occupant, building unit and appliance on energy consumption were investigated. The simulation model also was used for estimating determinants of energy waste. In addition, the simulation model includes a visualization interface that facilitates communication of strategies between the buildings users and facility managers. The results will highlight the significant attributes and effective strategies for energy waste reduction at the interface of human-building-appliance interactions. This information has potentially significant implications for building designers, facility managers, and users through a better understanding of emergent energy performance of buildings
Joint Modeling of Chest Radiographs and Radiology Reports for Pulmonary Edema Assessment
We propose and demonstrate a novel machine learning algorithm that assesses
pulmonary edema severity from chest radiographs. While large publicly available
datasets of chest radiographs and free-text radiology reports exist, only
limited numerical edema severity labels can be extracted from radiology
reports. This is a significant challenge in learning such models for image
classification. To take advantage of the rich information present in the
radiology reports, we develop a neural network model that is trained on both
images and free-text to assess pulmonary edema severity from chest radiographs
at inference time. Our experimental results suggest that the joint image-text
representation learning improves the performance of pulmonary edema assessment
compared to a supervised model trained on images only. We also show the use of
the text for explaining the image classification by the joint model. To the
best of our knowledge, our approach is the first to leverage free-text
radiology reports for improving the image model performance in this
application. Our code is available at
https://github.com/RayRuizhiLiao/joint_chestxray.Comment: The two first authors contributed equally. To be published in the
proceedings of MICCAI 202
REflex: Flexible framework for Relation Extraction in multiple domains
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (pages 81-89).Relation Extraction (RE) refers to the problem of extracting semantic relationships between concepts in a given sentence, and is an important component of Natural Language Understanding (NLU). It has been popularly studied in both the general purpose as well as the medical domains, and researchers have explored the effectiveness of different neural network architectures. However, systematic comparison of methods for RE is difficult because many experiments in the field are not described precisely enough to be completely reproducible and many papers fail to report ablation studies that would highlight the relative contributions of their various combined techniques. As a result, there is a lack of consensus on techniques that will generalize to novel tasks, datasets and contexts. This thesis introduces a unifying framework for RE known as REflex, applied on 3 highly used datasets (from the general, biomedical and clinical domains), with the ability to be extendable to new datasets. REflex allows exploration of the effect of different modeling techniques, pre-processing, training methodologies and evaluation metrics on a dataset of choice. This work performs such a systematic exploration on the 3 datasets and reveals interesting insights from pre-processing and training methodologies that often go unreported in the literature. Other insights from this exploration help in providing recommendations for future research in RE. REflex has experimental as well as design goals. The experimental goals are in identification of sources of variability in results for the 3 datasets and provide the field with a strong baseline model to compare against for future improvements. The design goals are in identification of best practices for relation extraction and to be a guide for approaching new datasets.by Geeticka Chauhan.S.M.S.M. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Scienc
Data and Code for "A New Approach to Animacy Detection"
This archive contains the code and data for the workshop article "A New Approach to Animacy Detection," published in 2018 in the the 27th International Conference on Computational Linguistics (COLING 2018), in Santa Fe, NM. The root of the archive contains a readme file which explains the archive contents. Furthermore, the archive can be imported directly into the Eclipse IDE as a project encapsulating the executable code and data required to reproduce the results of the paper; the code compiles with Java 1.8. The archive also contains a copy of the near-final version of the paper for reference
REflex: Flexible Framework for Relation Extraction in Multiple Domains
National Institutes of Mental Health (Grant P50-MH106933