Neural Natural Language Processing for unstructured data in electronic health records: A review

Li, Irene; Pan, Janet L.; Goldwasser, Jeremy; Verma, Neha; Wong, Wai Pan; Nuzumlali, Muhammed Yavuz; Rosand, Benjamin; Li, Yixin; Zhang, Matthew; Chang, David D.; Taylor, Richard A.; Krumholz, Harlan M.; Radev, Dragomir

doi:10.1016/j.cosrev.2022.100511

Cited by 101 publications

(44 citation statements)

References 92 publications

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Section: Clinical Subtypingmentioning

confidence: 99%

“…Clinical variables with their diverse data types (binary, categorical, and numerical), often lacking a direct numerical representation (eg, visit notes, angiograms), are difficult to integrate in subtyping workflows and require specialized feature extraction procedures. 35 Furthermore, as EHRs were developed primarily for billing and accounting purposes, EHR-based clinical data are noisy, incomplete, and biased. 36 Noise in data can result from reporting inaccuracies, digitization errors, or billing requirements that may not always be consonant with relevant disease features.…”

Section: Clinical Subtypingmentioning

confidence: 99%

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Phenomics and Robust Multiomics Data for Cardiovascular Disease Subtyping

Maiorino

Loscalzo

2023

ATVB

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The complex landscape of cardiovascular diseases encompasses a wide range of related pathologies arising from diverse molecular mechanisms and exhibiting heterogeneous phenotypes. This variety of manifestations poses significant challenges in the development of treatment strategies. The increasing availability of precise phenotypic and multiomics data of cardiovascular disease patient populations has spurred the development of a variety of computational disease subtyping techniques to identify distinct subgroups with unique underlying pathogeneses. In this review, we outline the essential components of computational approaches to select, integrate, and cluster omics and clinical data in the context of cardiovascular disease research. We delve into the challenges faced during different stages of the analysis, including feature selection and extraction, data integration, and clustering algorithms. Next, we highlight representative applications of subtyping pipelines in heart failure and coronary artery disease. Finally, we discuss the current challenges and future directions in the development of robust subtyping approaches that can be implemented in clinical workflows, ultimately contributing to the ongoing evolution of precision medicine in health care.

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Section: Clinical Subtypingmentioning

confidence: 99%

Section: Clinical Subtypingmentioning

confidence: 99%

Phenomics and Robust Multiomics Data for Cardiovascular Disease Subtyping

Maiorino

Loscalzo

2023

ATVB

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“…BERT-based methods are also used in this domain, e.g., in labeling radiographic images, so they can be used as training data for a further model [ 38 , 39 ]. Specifically for MS, rule-based NLP algorithms were developed to identify and extract information concerning the clinical course of disease [ 40 ], feature-extraction with Naïve Bayes classification was used to diagnose patients with MS [ 41 ], and a Convolutional Neural Network was used to predict the Expanded Disability Status Scale (EDSS) to monitor the progression of MS patients [ 41 ]. More relevant to this paper, BERT was also used for MS. Namely, MS-BERT was created to generate embeddings and predict EDSS together with MSBC (a classifier for applying MS-BERT) [ 42 ].…”

Section: Related Workmentioning

confidence: 99%

Accurate and Reliable Classification of Unstructured Reports on Their Diagnostic Goal Using BERT Models

et al. 2023

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Understanding the diagnostic goal of medical reports is valuable information for understanding patient flows. This work focuses on extracting the reason for taking an MRI scan of Multiple Sclerosis (MS) patients using the attached free-form reports: Diagnosis, Progression or Monitoring. We investigate the performance of domain-dependent and general state-of-the-art language models and their alignment with domain expertise. To this end, eXplainable Artificial Intelligence (XAI) techniques are used to acquire insight into the inner workings of the model, which are verified on their trustworthiness. The verified XAI explanations are then compared with explanations from a domain expert, to indirectly determine the reliability of the model. BERTje, a Dutch Bidirectional Encoder Representations from Transformers (BERT) model, outperforms RobBERT and MedRoBERTa.nl in both accuracy and reliability. The latter model (MedRoBERTa.nl) is a domain-specific model, while BERTje is a generic model, showing that domain-specific models are not always superior. Our validation of BERTje in a small prospective study shows promising results for the potential uptake of the model in a practical setting.

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“…Natural Language Processing (NLP) has established itself in the medical field as a valuable method of data processing. It is widely used in biomedical research as well as in several clinical applications [1][2][3]. While the initial success of NLP stemmed from work in the English language, the need for reliable NLP tools in other languages is ever-growing, particularly regarding healthcare [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Exploring the Latest Highlights in Medical Natural Language Processing across Multiple Languages: A Survey

Shaitarova,

Zaghir,

Lavelli

et al. 2023

Yearb Med Inform

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Objectives: This survey aims to provide an overview of the current state of biomedical and clinical Natural Language Processing (NLP) research and practice in Languages other than English (LoE). We pay special attention to data resources, language models, and popular NLP downstream tasks. Methods: We explore the literature on clinical and biomedical NLP from the years 2020-2022, focusing on the challenges of multilinguality and LoE. We query online databases and manually select relevant publications. We also use recent NLP review papers to identify the possible information lacunae. Results: Our work confirms the recent trend towards the use of transformer-based language models for a variety of NLP tasks in medical domains. In addition, there has been an increase in the availability of annotated datasets for clinical NLP in LoE, particularly in European languages such as Spanish, German and French. Common NLP tasks addressed in medical NLP research in LoE include information extraction, named entity recognition, normalization, linking, and negation detection. However, there is still a need for the development of annotated datasets and models specifically tailored to the unique characteristics and challenges of medical text in some of these languages, especially low-resources ones. Lastly, this survey highlights the progress of medical NLP in LoE, and helps at identifying opportunities for future research and development in this field.

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Neural Natural Language Processing for unstructured data in electronic health records: A review

Cited by 101 publications

References 92 publications

Phenomics and Robust Multiomics Data for Cardiovascular Disease Subtyping

Phenomics and Robust Multiomics Data for Cardiovascular Disease Subtyping

Accurate and Reliable Classification of Unstructured Reports on Their Diagnostic Goal Using BERT Models

Exploring the Latest Highlights in Medical Natural Language Processing across Multiple Languages: A Survey

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