Detecting Section Boundaries in Medical Dictations: Toward Real-Time Conversion of Medical Dictations to Clinical Reports

Sadoughi, Najmeh; Finley, Greg; Edwards, Erik; Robinson, Amanda; Korenevsky, Maxim; Brenndoerfer, Michael; Axtmann, Nico; Miller, Mark A.; Suendermann-Oeft, David

doi:10.1007/978-3-319-99579-3_58

Cited by 9 publications

(3 citation statements)

References 7 publications

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“…Sadoughi et al [117] have applied section detection on clinical dictations in real time. They used automatic speech recognition to transform the speech into plain text.…”

Section: Hybrid Methodsmentioning

confidence: 99%

Information extraction from electronic medical documents: state of the art and future research directions

2022

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In the medical field, a doctor must have a comprehensive knowledge by reading and writing narrative documents, and he is responsible for every decision he takes for patients. Unfortunately, it is very tiring to read all necessary information about drugs, diseases and patients due to the large amount of documents that are increasing every day. Consequently, so many medical errors can happen and even kill people. Likewise, there is such an important field that can handle this problem, which is the information extraction. There are several important tasks in this field to extract the important and desired information from unstructured text written in natural language. The main principal tasks are named entity recognition and relation extraction since they can structure the text by extracting the relevant information. However, in order to treat the narrative text we should use natural language processing techniques to extract useful information and features. In our paper, we introduce and discuss the several techniques and solutions used in these tasks. Furthermore, we outline the challenges in information extraction from medical documents. In our knowledge, this is the most comprehensive survey in the literature with an experimental analysis and a suggestion for some uncovered directions.

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“…Sadoughi et al [117] have applied section detection on clinical dictations in real time. They used automatic speech recognition to transform the speech into plain text.…”

Section: Hybrid Methodsmentioning

confidence: 99%

Information extraction from electronic medical documents: state of the art and future research directions

2022

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“…For example, Sadoughi et al used unidirectional long-short term memory (LSTM) units. And Salloum et al proposed using bi-direction LSTM to detect sections while converting from medical dictations into clinical reports [16,17]. In a recent study, Rosenthal et al applied recurrent neural network (RNN) or the fine-tune BERT model using gated recurrence units trained with medical literature.…”

Section: Approaches For Section Detectionmentioning

confidence: 99%

Assessing document section heterogeneity across multiple electronic health record systems for computational phenotyping: A case study of heart-failure phenotyping algorithm

et al. 2023

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Background The incorporation of information from clinical narratives is critical for computational phenotyping. The accurate interpretation of clinical terms highly depends on their associated context, especially the corresponding clinical section information. However, the heterogeneity across different Electronic Health Record (EHR) systems poses challenges in utilizing the section information. Objectives Leveraging the eMERGE heart failure (HF) phenotyping algorithm, we assessed the heterogeneity quantitatively through the performance comparison of machine learning (ML) classifiers which map clinical sections containing HF-relevant terms across different EHR systems to standard sections in Health Level 7 (HL7) Clinical Document Architecture (CDA). Methods We experimented with both random forest models with sentence-embedding features and bidirectional encoder representations from transformers models. We trained MLs using an automated labeled corpus from an EHR system that adopted HL7 CDA standard. We assessed the performance using a blind test set (n = 300) from the same EHR system and a gold standard (n = 900) manually annotated from three other EHR systems. Results The F-measure of those ML models varied widely (0.00–0.91%), indicating MLs with one tuning parameter set were insufficient to capture sections across different EHR systems. The error analysis indicates that the section does not always comply with the corresponding standardized sections, leading to low performance. Conclusions We presented the potential use of ML techniques to map the sections containing HF-relevant terms in multiple EHR systems to standard sections. However, the findings suggested that the quality and heterogeneity of section structure across different EHRs affect applications due to the poor adoption of documentation standards.

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“…These segmented documents have many uses across various domains and downstream tasks. Segmentation can, for example, be used to convert unstructured medical dictations into clinical reports (Sadoughi et al, 2018), which in turn can help with medical coding (since a diagnosis mentioned in a "Medical History" might be different from a diagnosis mentioned in an "Intake" section (Ganesan and Subotin, 2014)). Segmentation can also be used downstream for retrieval (Hearst and Plaunt, 2002;Edinger et al, 2017;Allan et al, 1998), where it can be particularly useful when applied to informal text or speech that lacks explicit segment markup.…”

Section: Introductionmentioning

confidence: 99%

A Joint Model for Document Segmentation and Segment Labeling

Barrow¹,

Jain²,

Morariu³

et al. 2020

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

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Text segmentation aims to uncover latent structure by dividing text from a document into coherent sections. Where previous work on text segmentation considers the tasks of document segmentation and segment labeling separately, we show that the tasks contain complementary information and are best addressed jointly. We introduce the Segment Pooling LSTM (S-LSTM) model, which is capable of jointly segmenting a document and labeling segments. In support of joint training, we develop a method for teaching the model to recover from errors by aligning the predicted and ground truth segments. We show that S-LSTM reduces segmentation error by 30% on average, while also improving segment labeling.

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Detecting Section Boundaries in Medical Dictations: Toward Real-Time Conversion of Medical Dictations to Clinical Reports

Cited by 9 publications

References 7 publications

Information extraction from electronic medical documents: state of the art and future research directions

Information extraction from electronic medical documents: state of the art and future research directions

Assessing document section heterogeneity across multiple electronic health record systems for computational phenotyping: A case study of heart-failure phenotyping algorithm

A Joint Model for Document Segmentation and Segment Labeling

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