66 research outputs found
Exploiting Sentence Embedding for Medical Question Answering
Despite the great success of word embedding, sentence embedding remains a
not-well-solved problem. In this paper, we present a supervised learning
framework to exploit sentence embedding for the medical question answering
task. The learning framework consists of two main parts: 1) a sentence
embedding producing module, and 2) a scoring module. The former is developed
with contextual self-attention and multi-scale techniques to encode a sentence
into an embedding tensor. This module is shortly called Contextual
self-Attention Multi-scale Sentence Embedding (CAMSE). The latter employs two
scoring strategies: Semantic Matching Scoring (SMS) and Semantic Association
Scoring (SAS). SMS measures similarity while SAS captures association between
sentence pairs: a medical question concatenated with a candidate choice, and a
piece of corresponding supportive evidence. The proposed framework is examined
by two Medical Question Answering(MedicalQA) datasets which are collected from
real-world applications: medical exam and clinical diagnosis based on
electronic medical records (EMR). The comparison results show that our proposed
framework achieved significant improvements compared to competitive baseline
approaches. Additionally, a series of controlled experiments are also conducted
to illustrate that the multi-scale strategy and the contextual self-attention
layer play important roles for producing effective sentence embedding, and the
two kinds of scoring strategies are highly complementary to each other for
question answering problems.Comment: 8 page
How can Deep Learning Retrieve the Write-Missing Additional Diagnosis from Chinese Electronic Medical Record For DRG
The purpose of write-missing diagnosis detection is to find diseases that
have been clearly diagnosed from medical records but are missed in the
discharge diagnosis. Unlike the definition of missed diagnosis, the
write-missing diagnosis is clearly manifested in the medical record without
further reasoning. The write-missing diagnosis is a common problem, often
caused by physician negligence. The write-missing diagnosis will result in an
incomplete diagnosis of medical records. While under DRG grouping, the
write-missing diagnoses will miss important additional diagnoses (CC, MCC),
thus affecting the correct rate of DRG enrollment.
Under the circumstance that countries generally start to adopt DRG enrollment
and payment, the problem of write-missing diagnosis is a common and serious
problem. The current manual-based method is expensive due to the complex
content of the full medical record. We think this problem is suitable to be
solved as natural language processing. But to the best of our knowledge, no
researchers have conducted research on this problem based on natural language
processing methods.
We propose a framework for solving the problem of write-missing diagnosis,
which mainly includes three modules: disease recall module, disease context
logic judgment module, and disease relationship comparison module. Through this
framework, we verify that the problem of write-missing diagnosis can be solved
well, and the results are interpretable. At the same time, we propose advanced
solutions for the disease context logic judgment module and disease
relationship comparison module, which have obvious advantages compared with the
mainstream methods of the same type of problems. Finally, we verified the value
of our proposed framework under DRG medical insurance payment in a tertiary
hospital
Exploring semantic information in disease: Simple Data Augmentation Techniques for Chinese Disease Normalization
The disease is a core concept in the medical field, and the task of
normalizing disease names is the basis of all disease-related tasks. However,
due to the multi-axis and multi-grain nature of disease names, incorrect
information is often injected and harms the performance when using general text
data augmentation techniques. To address the above problem, we propose a set of
data augmentation techniques that work together as an augmented training task
for disease normalization. Our data augmentation methods are based on both the
clinical disease corpus and standard disease corpus derived from ICD-10 coding.
Extensive experiments are conducted to show the effectiveness of our proposed
methods. The results demonstrate that our methods can have up to 3\%
performance gain compared to non-augmented counterparts, and they can work even
better on smaller datasets
Experimental studies of the acoustic wave field near a borehole
A monopole or a dipole source in a fluid borehole generates acoustic waves, part of which propagate along the borehole and the other part enter the formation propagating as P- or S-waves. The refracted waves propagating along the borehole wall are used to determine P- and S-wave velocities. However, a significant fraction of the seismic energy radiates into the formation. In this laboratory study, we measure the acoustic waves in the borehole and the seismic waves in the formation at different distances from the borehole.
We use scaled borehole models made of Lucite and of concrete to simulate a soft and a hard formation, respectively. The waveforms are measured in the boreholes as well as in the formations with different radial distances from the axis of the borehole. The results show that the investigation depth of the wave measured in the borehole is less than one half of the wavelength. The seismic energy radiating into the formation and scattered from interfaces and heterogeneities can be used for imaging the formation.Massachusetts Institute of Technology. Earth Resources Laboratory (Founding Member Consortium
Medical Exam Question Answering with Large-scale Reading Comprehension
Reading and understanding text is one important component in computer aided
diagnosis in clinical medicine, also being a major research problem in the
field of NLP. In this work, we introduce a question-answering task called MedQA
to study answering questions in clinical medicine using knowledge in a
large-scale document collection. The aim of MedQA is to answer real-world
questions with large-scale reading comprehension. We propose our solution
SeaReader--a modular end-to-end reading comprehension model based on LSTM
networks and dual-path attention architecture. The novel dual-path attention
models information flow from two perspectives and has the ability to
simultaneously read individual documents and integrate information across
multiple documents. In experiments our SeaReader achieved a large increase in
accuracy on MedQA over competing models. Additionally, we develop a series of
novel techniques to demonstrate the interpretation of the question answering
process in SeaReader
THiFLY Research at SemEval-2023 Task 7: A Multi-granularity System for CTR-based Textual Entailment and Evidence Retrieval
The NLI4CT task aims to entail hypotheses based on Clinical Trial Reports
(CTRs) and retrieve the corresponding evidence supporting the justification.
This task poses a significant challenge, as verifying hypotheses in the NLI4CT
task requires the integration of multiple pieces of evidence from one or two
CTR(s) and the application of diverse levels of reasoning, including textual
and numerical. To address these problems, we present a multi-granularity system
for CTR-based textual entailment and evidence retrieval in this paper.
Specifically, we construct a Multi-granularity Inference Network (MGNet) that
exploits sentence-level and token-level encoding to handle both textual
entailment and evidence retrieval tasks. Moreover, we enhance the numerical
inference capability of the system by leveraging a T5-based model, SciFive,
which is pre-trained on the medical corpus. Model ensembling and a joint
inference method are further utilized in the system to increase the stability
and consistency of inference. The system achieves f1-scores of 0.856 and 0.853
on textual entailment and evidence retrieval tasks, resulting in the best
performance on both subtasks. The experimental results corroborate the
effectiveness of our proposed method. Our code is publicly available at
https://github.com/THUMLP/NLI4CT.Comment: Accepted by SemEval202
Three-dimensional hierarchical Co(OH)F nanosheet arrays decorated by single-atom Ru for boosting oxygen evolution reaction
Electronic coupling with the support plays a crucial role in boosting the intrinsic catalytic activity of a single-atom catalyst. Herein, the three-dimensional (3D) hierarchical Co(OH)F nanosheet arrays modified by singleatom Ru (SA-Ru/Co(OH)F) are prepared by a facile one-step hydrothermal method under mild conditions, which exhibit excellent activity with an overpotential of 200 and 326 mV at 10 and 500 mA cm(-2), respectively, as well as robust stability for oxygen evolution reaction (OER) in 1.0 mol L-1 KOH electrolyte. The study of electronic structures and surface chemical states before and after OER testing reveals that the strong electronic coupling between single-atom Ru and Co(OH)F induces the charge redistribution in SA-Ru/Co(OH)F and suppresses the excessive oxidation of Ru into higher valence state (more than +4) under high OER potential. This work provides a strategy to stabilize single-atom Ru by Co(OH)F that can enhance the activity and durability for OER under large current densities
Exploring the Dominant Role of Atomic- and Nano-Ruthenium as Active Sites for Hydrogen Evolution Reaction in Both Acidic and Alkaline Media
Ru nanoparticles (NPs) and single atoms (SAs)-based materials have been investigated as alternative electrocatalysts to Pt/C for hydrogen evolution reaction (HER). Exploring the dominant role of atomic- and nano-ruthenium as active sites in acidic and alkaline media is very necessary for optimizing the performance. Herein, an electrocatalyst containing both Ru SAs and NPs anchored on defective carbon (RuSA+NP/DC) has been synthesized via a Ru-alginate metal-organic supramolecules conversion method. RuSA+NP/DC exhibits low overpotentials of 16.6 and 18.8 mV at 10 mA cm(-2) in acidic and alkaline electrolytes, respectively. Notably, its mass activities are dramatically improved, which are about 1.1 and 2.4 times those of Pt/C at an overpotential of 50 mV in acidic and alkaline media, respectively. Theoretical calculations reveal that Ru SAs own the most appropriate H* adsorption strength and thus, plays a dominant role for HER in acid electrolyte, while Ru NPs facilitate the dissociation of H2O that is the rate-determining step in alkaline electrolyte, leading to a remarkable HER activity
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