Using Deep Learning Based Natural Language Processing Techniques for Clinical Decision-Making with EHRs

Zhu, Runjie; Tu, Xinhui; Huang, Jimmy Xiangji

doi:10.1007/978-3-030-33966-1_13

Cited by 11 publications

(2 citation statements)

References 45 publications

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“…Insufficient or biased training data can impact the effectiveness and generalizability of GAN-based models in biomedical prediction systems. Addressing these challenges is crucial to harness the potential of GANs for improving the accuracy and reliability of predictions in the biomedical domain (Mittal and Hasija, 2020;Zhu et al, 2020).…”

Section: Generative Adversarial Networkmentioning

confidence: 99%

Trends in using deep learning algorithms in biomedical prediction systems

Wang,

Liu,

Wang

2023

Front. Neurosci.

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In the domain of using DL-based methods in medical and healthcare prediction systems, the utilization of state-of-the-art deep learning (DL) methodologies assumes paramount significance. DL has attained remarkable achievements across diverse domains, rendering its efficacy particularly noteworthy in this context. The integration of DL with health and medical prediction systems enables real-time analysis of vast and intricate datasets, yielding insights that significantly enhance healthcare outcomes and operational efficiency in the industry. This comprehensive literature review systematically investigates the latest DL solutions for the challenges encountered in medical healthcare, with a specific emphasis on DL applications in the medical domain. By categorizing cutting-edge DL approaches into distinct categories, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), long short-term memory (LSTM) models, support vector machine (SVM), and hybrid models, this study delves into their underlying principles, merits, limitations, methodologies, simulation environments, and datasets. Notably, the majority of the scrutinized articles were published in 2022, underscoring the contemporaneous nature of the research. Moreover, this review accentuates the forefront advancements in DL techniques and their practical applications within the realm of medical prediction systems, while simultaneously addressing the challenges that hinder the widespread implementation of DL in image segmentation within the medical healthcare domains. These discerned insights serve as compelling impetuses for future studies aimed at the progressive advancement of using DL-based methods in medical and health prediction systems. The evaluation metrics employed across the reviewed articles encompass a broad spectrum of features, encompassing accuracy, precision, specificity, F-score, adoptability, adaptability, and scalability.

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Section: Generative Adversarial Networkmentioning

confidence: 99%

Trends in using deep learning algorithms in biomedical prediction systems

Wang,

Liu,

Wang

2023

Front. Neurosci.

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“…Recent advances in machine learning, particularly in deep learning, have resulted in their successful application to specific clinical tasks [ 26 , 27 ], and while most studies have relied on structured data from EHRs, some have used free-text information [ 4 , 28 , 29 ]. Some studies have even generated patient representations based on the nonlinear transformations of all encoded information in EHRs [ 30 ].…”

Section: Key Nlp Needs For Pocrcmentioning

confidence: 99%

Defining Patient-Oriented Natural Language Processing: A New Paradigm for Research and Development to Facilitate Adoption and Use by Medical Experts

et al. 2021

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The capabilities of natural language processing (NLP) methods have expanded significantly in recent years, and progress has been particularly driven by advances in data science and machine learning. However, NLP is still largely underused in patient-oriented clinical research and care (POCRC). A key reason behind this is that clinical NLP methods are typically developed, optimized, and evaluated with narrowly focused data sets and tasks (eg, those for the detection of specific symptoms in free texts). Such research and development (R&D) approaches may be described as problem oriented, and the developed systems perform specialized tasks well. As standalone systems, however, they generally do not comprehensively meet the needs of POCRC. Thus, there is often a gap between the capabilities of clinical NLP methods and the needs of patient-facing medical experts. We believe that to increase the practical use of biomedical NLP, future R&D efforts need to be broadened to a new research paradigm—one that explicitly incorporates characteristics that are crucial for POCRC. We present our viewpoint about 4 such interrelated characteristics that can increase NLP systems’ suitability for POCRC (3 that represent NLP system properties and 1 associated with the R&D process)—(1) interpretability (the ability to explain system decisions), (2) patient centeredness (the capability to characterize diverse patients), (3) customizability (the flexibility for adapting to distinct settings, problems, and cohorts), and (4) multitask evaluation (the validation of system performance based on multiple tasks involving heterogeneous data sets). By using the NLP task of clinical concept detection as an example, we detail these characteristics and discuss how they may result in the increased uptake of NLP systems for POCRC.

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