Objectives: This study aims to systematically review the literature on the
computational processing of the language of pain, whether generated by patients
or physicians, identifying current trends and challenges. Methods: Following
the PRISMA guidelines, a comprehensive literature search was conducted to
select relevant studies on the computational processing of the language of pain
and answer pre-defined research questions. Data extraction and synthesis were
performed to categorize selected studies according to their primary purpose and
outcome, patient and pain population, textual data, computational methodology,
and outcome targets. Results: Physician-generated language of pain,
specifically from clinical notes, was the most used data. Tasks included
patient diagnosis and triaging, identification of pain mentions, treatment
response prediction, biomedical entity extraction, correlation of linguistic
features with clinical states, and lexico-semantic analysis of pain narratives.
Only one study included previous linguistic knowledge on pain utterances in
their experimental setup. Most studies targeted their outcomes for physicians,
either directly as clinical tools or as indirect knowledge. The least targeted
stage of clinical pain care was self-management, in which patients are most
involved. The least studied dimensions of pain were affective and
sociocultural. Only two studies measured how physician performance on clinical
tasks improved with the inclusion of the proposed algorithm. Discussion: This
study found that future research should focus on analyzing patient-generated
language of pain, developing patient-centered resources for self-management and
patient-empowerment, exploring affective and sociocultural aspects of pain, and
measuring improvements in physician performance when aided by the proposed
tools.

该研究旨在系统综述有关疼痛语言的计算处理的文献，无论是由患者还是医生产生，以确定当前的趋势和挑战。

疼痛语言的计算分析：系统综述

Computational analysis of the language of pain: a systematic review

Light field imaging is limited in its computational processing demands of
high sampling for both spatial and angular dimensions. Single-shot light field
cameras sacrifice spatial resolution to sample angular viewpoints, typically by
multiplexing incoming rays onto a 2D sensor array. While this resolution can be
recovered using compressive sensing, these iterative solutions are slow in
processing a light field. We present a deep learning approach using a new, two
branch network architecture, consisting jointly of an autoencoder and a 4D CNN,
to recover a high resolution 4D light field from a single coded 2D image. This
network decreases reconstruction time significantly while achieving average
PSNR values of 26-32 dB on a variety of light fields. In particular,
reconstruction time is decreased from 35 minutes to 6.7 minutes as compared to
the dictionary method for equivalent visual quality. These reconstructions are
performed at small sampling/compression ratios as low as 8%, allowing for
cheaper coded light field cameras. We test our network reconstructions on
synthetic light fields, simulated coded measurements of real light fields
captured from a Lytro Illum camera, and real coded images from a custom CMOS
diffractive light field camera. The combination of compressive light field
capture with deep learning allows the potential for real-time light field video
acquisition systems in the future.

本研究提出了使用新的、由自编码器和四维卷积神经网络组成的两个分支网络结构，深度学习途径来从单个编码的二维图像中恢复高分辨率的四维光场，以实现光场成像的实时视频采集系统的潜力。