197,882 research outputs found
An ontology for clinical questions about the contents of patient notes
AbstractObjectiveMany studies have been completed on question classification in the open domain, however only limited work focuses on the medical domain. As well, to the best of our knowledge, most of these medical question classifications were designed for literature based question and answering systems. This paper focuses on a new direction, which is to design a novel question processing and classification model for answering clinical questions applied to electronic patient notes.MethodsThere are four main steps in the work. Firstly, a relatively large set of clinical questions was collected from staff in an Intensive Care Unit. Then, a clinical question taxonomy was designed for question and answering purposes. Subsequently an annotation guideline was created and used to annotate the question set. Finally, a multilayer classification model was built to classify the clinical questions.ResultsThrough the initial classification experiments, we realized that the general features cannot contribute to high performance of a minimum classifier (a small data set with multiple classes). Thus, an automatic knowledge discovery and knowledge reuse process was designed to boost the performance by extracting and expanding the specific features of the questions. In the evaluation, the results show around 90% accuracy can be achieved in the answerable subclass classification and generic question templates classification. On the other hand, the machine learning method does not perform well at identifying the category of unanswerable questions, due to the asymmetric distribution.ConclusionsIn this paper, a comprehensive study on clinical questions has been completed. A major outcome of this work is the multilayer classification model. It serves as a major component of a patient records based clinical question and answering system as our studies continue. As well, the question collections can be reused by the research community to improve the efficiency of their own question and answering systems
Measuring and Managing Answer Quality for Online Data-Intensive Services
Online data-intensive services parallelize query execution across distributed
software components. Interactive response time is a priority, so online query
executions return answers without waiting for slow running components to
finish. However, data from these slow components could lead to better answers.
We propose Ubora, an approach to measure the effect of slow running components
on the quality of answers. Ubora randomly samples online queries and executes
them twice. The first execution elides data from slow components and provides
fast online answers; the second execution waits for all components to complete.
Ubora uses memoization to speed up mature executions by replaying network
messages exchanged between components. Our systems-level implementation works
for a wide range of platforms, including Hadoop/Yarn, Apache Lucene, the
EasyRec Recommendation Engine, and the OpenEphyra question answering system.
Ubora computes answer quality much faster than competing approaches that do not
use memoization. With Ubora, we show that answer quality can and should be used
to guide online admission control. Our adaptive controller processed 37% more
queries than a competing controller guided by the rate of timeouts.Comment: Technical Repor
Learning Sentence-internal Temporal Relations
In this paper we propose a data intensive approach for inferring
sentence-internal temporal relations. Temporal inference is relevant for
practical NLP applications which either extract or synthesize temporal
information (e.g., summarisation, question answering). Our method bypasses the
need for manual coding by exploiting the presence of markers like after", which
overtly signal a temporal relation. We first show that models trained on main
and subordinate clauses connected with a temporal marker achieve good
performance on a pseudo-disambiguation task simulating temporal inference
(during testing the temporal marker is treated as unseen and the models must
select the right marker from a set of possible candidates). Secondly, we assess
whether the proposed approach holds promise for the semi-automatic creation of
temporal annotations. Specifically, we use a model trained on noisy and
approximate data (i.e., main and subordinate clauses) to predict
intra-sentential relations present in TimeBank, a corpus annotated rich
temporal information. Our experiments compare and contrast several
probabilistic models differing in their feature space, linguistic assumptions
and data requirements. We evaluate performance against gold standard corpora
and also against human subjects
REVEAL: Retrieval-Augmented Visual-Language Pre-Training with Multi-Source Multimodal Knowledge Memory
In this paper, we propose an end-to-end Retrieval-Augmented Visual Language
Model (REVEAL) that learns to encode world knowledge into a large-scale memory,
and to retrieve from it to answer knowledge-intensive queries. REVEAL consists
of four key components: the memory, the encoder, the retriever and the
generator. The large-scale memory encodes various sources of multimodal world
knowledge (e.g. image-text pairs, question answering pairs, knowledge graph
triplets, etc) via a unified encoder. The retriever finds the most relevant
knowledge entries in the memory, and the generator fuses the retrieved
knowledge with the input query to produce the output. A key novelty in our
approach is that the memory, encoder, retriever and generator are all
pre-trained end-to-end on a massive amount of data. Furthermore, our approach
can use a diverse set of multimodal knowledge sources, which is shown to result
in significant gains. We show that REVEAL achieves state-of-the-art results on
visual question answering and image captioning
Mitigating Temporal Misalignment by Discarding Outdated Facts
While large language models are able to retain vast amounts of world
knowledge seen during pretraining, such knowledge is prone to going out of date
and is nontrivial to update. Furthermore, these models are often used under
temporal misalignment, tasked with answering questions about the present,
despite having only been trained on data collected in the past. To mitigate the
effects of temporal misalignment, we propose fact duration prediction: the task
of predicting how long a given fact will remain true. In our experiments, we
demonstrate how identifying facts that are prone to rapid change can help
models avoid from reciting outdated information and identify which predictions
require seeking out up-to-date knowledge sources. We also show how modeling
fact duration improves calibration for knowledge-intensive tasks, such as
open-retrieval question answering, under temporal misalignment by discarding
volatile facts. Our data and code will be released publicly at
https://github.com/mikejqzhang/mitigating_misalignment
KILT: a Benchmark for Knowledge Intensive Language Tasks.
Challenging problems such as open-domain question answering, fact checking, slot filling and entity linking require access to large, external knowledge sources. While some models do well on individual tasks, developing general models is difficult as each task might require computationally expensive indexing of custom knowledge sources, in addition to dedicated infrastructure. To catalyze research on models that condition on specific information in large textual resources, we present a benchmark for knowledge-intensive language tasks (KILT). All tasks in KILT are grounded in the same snapshot of Wikipedia, reducing engineering turnaround through the re-use of components, as well as accelerating research into task-agnostic memory architectures. We test both task-specific and general baselines, evaluating downstream performance in addition to the ability of the models to provide provenance. We find that a shared dense vector index coupled with a seq2seq model is a strong baseline, outperforming more tailor-made approaches for fact checking, open-domain question answering and dialogue, and yielding competitive results on entity linking and slot filling, by generating disambiguated text. KILT data and code are available at https://github.com/facebookresearc
Veränderung von Persönlichkeitsmerkmalen im Verlauf einer stationären Therapie
Background: The Freiburger Personality Inventory (FPI-R) is a well established and proven instrument for the assessment of personality traits. Although personality is conceived as a stable trait, clinical experience indicates that impressive changes are found on personality scales during intensive treatment. Method A large sample of inpatients which were treated with cognitive-behavioral therapy for bulimia nervosa, tinnitus or anxiety disorder was evaluated concerning the question which items of the FPI-R were answered differently or identically before and after intensive therapy. Results: It could be found that items which cover aspects that are central to the therapy more often show changing answers. The use of conditional form and indefinite frequency adjuncts in the formulation of items evidently allowed a more differentiated weighting of pros and cons at the end of therapy. Effects of regression to the mean could be excluded as an explanation by empirical data. Conclusion: It can be concluded that changes in answering items before and after intensive therapy can be explained as specific effects of therapy
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