Classification of neurologic outcomes from medical notes using natural language processing

Fernandes, Marta Bento; Valizadeh, Navid; Alabsi, Haitham; Quadri, Syed A.; Tesh, Ryan A.; Bucklin, Abigail A.; Sun, Haoqi; Jain, Aayushee; Brenner, Laura; Ye, Elissa; Ge, Wendong; Collens, Sarah I.; Lin, Stacie; Das, Sudeshna; Robbins, Gregory K.; Zafar, Sahar F.; Mukerji, Shibani S.; Westover, M. Brandon

doi:10.1016/j.eswa.2022.119171

Cited by 12 publications

(5 citation statements)

References 64 publications

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“…When applied to large-scale data sets, such as electronic health records (EHRs) or databases of scientific literature, LLMs could improve classification accuracy and help streamline the process of clinical observational research. For example, Fernandes et al 31 demonstrated that an NLP algorithm was able to assign neurologic disability outcomes after intensive care unit hospitalization, based on free-text clinical notes. In another study, Xie and coauthors used 3 different LLMs (BERT, RoBERTa, and Bio_ClinicalBERT) to comb through clinical notes and determine whether and how frequently patients had seizures.…”

Section: Autoregressionmentioning

confidence: 99%

Large Language Models in Neurology Research and Future Practice

Romano,

Shih,

Paschalidis

et al. 2023

Neurology

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Recent advancements in generative artificial intelligence, particularly using large language models (LLMs), are gaining increased public attention. We provide a perspective on the potential of LLMs to analyze enormous amounts of data from medical records and gain insights on specific topics in neurology. Additionally, we explore use cases for LLMs such as early diagnosis, supporting patient and caregivers, and acting as an assistant for clinicians. We point to the potential ethical and technical challenges raised by LLMs, such as concerns about privacy and data security, potential biases in the data for model training, and the need for careful validation of results. Researchers must consider these challenges and take steps to address them to ensure that their work is conducted in a safe and responsible manner. Despite these challenges, LLMs offer promising opportunities for improving care and treatment of various neurological disorders.

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Section: Autoregressionmentioning

confidence: 99%

Large Language Models in Neurology Research and Future Practice

Romano,

Shih,

Paschalidis

et al. 2023

Neurology

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“…However, this information is currently only available in an unstructured format and thus impractical for analysis. Due to the rapidly increasing number of clinical free texts, various groups made efforts to work towards an automated analysis of such texts by analyzing and classifying clinical free texts by natural language processing (NLP), a subfield of machine learning [3][4][5][6][7][8]. In previous studies, automatic extraction of date and time references [9] and classification into predefined categories [10] were performed on notes of the HerzMobil program.…”

Section: Introductionmentioning

confidence: 99%

Classification of Clinical Notes from a Heart Failure Telehealth Network

Wiesmüller,

Lauschenski,

Baumgartner

et al. 2023

Caring Is Sharing – Exploiting the Value in Data for Health and Innovation

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Heart failure is a common chronic disease which is associated with high re-hospitalization and mortality rates. Within the telemedicine-assisted transitional care disease management program HerzMobil, monitoring data such as daily measured vital parameters and various other heart failure related data are collected in a structured way. Additionally, involved healthcare professionals communicate with one another via the system using free-text clinical notes. Since manual annotation of such notes is too time-consuming for routine care applications, an automated analysis process is needed. In the present study, we established a ground truth classification of 636 randomly selected clinical notes from HerzMobil based on annotations of 9 experts with different professional background (2 physicians, 4 nurses, and 3 engineers). We analyzed the influence of the professional background on the inter annotator reliability and compared the results with the accuracy of an automated classification algorithm. We found significant differences depending on the profession and on the category. These results indicate that different professional backgrounds should be considered when selecting annotators in such scenarios.

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“…By using LLMs in scientific research, researchers who have the potential to analyze the large amount of data generated using data mining can conduct sophisticated power evaluations, hence improving the efficacy of study designs and ensuring the reliability and strength of the obtained results. 13 In addition, LLMs have the potential to foster uniformity and reproducibility in research initiatives by facilitating the development of standardized language and descriptions of interventions. 14 In clinical settings, LLMs enhance patient communication via chatbots, making information comprehensible and easily accessible.…”

Section: Potential Added Valuementioning

confidence: 99%

“…Addressing these challenges requires a nuanced understanding of the intricacies within the language used to document rehabilitation processes and a careful consideration of the limitations in the current documentation practices. 7,13,14 The fundamental importance lies in guaranteeing the responsible and ethical utilization of LLMs within the healthcare domain. To fully leverage the capabilities of LLMs, it is imperative to address and overcome the various technological challenges that arise.…”

Section: Current Limitations Challenges and Pitfallsmentioning

confidence: 99%

“…Moreover, the absence of documentation on certain issues in patient descriptions may not necessarily indicate their absence but could signify a lack of focus due to resource constraints and the necessity to prioritize specific aspects of treatment. Addressing these challenges requires a nuanced understanding of the intricacies within the language used to document rehabilitation processes and a careful consideration of the limitations in the current documentation practices 7,13,14 …”

Section: Large Language Modelsmentioning

confidence: 99%

See 1 more Smart Citation

Unlocking the Black Box? A Comprehensive Exploration of Large Language Models in Rehabilitation

Bonnechère

2024

Am J Phys Med Rehabil

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Rehabilitation is a vital component of healthcare, aiming to restore function and improve the well-being of individuals with disabilities or injuries. Nevertheless, the rehabilitation process is often likened to a 'black box', with complexities that pose challenges for comprehensive analysis and optimization. The emergence of Large Language Models (LLMs) offers promising solutions to better understand this ‘black box’. LLMs excel at comprehending and generating human-like text, making them valuable in the healthcare sector. In rehabilitation, healthcare professionals must integrate a wide range of data to create effective treatment plans, akin to selecting the best ingredients for the 'black box'. LLMs enhance data integration, communication, assessment, and prediction. This paper delves into the ground-breaking use of LLMs as a tool to further understand the rehabilitation process. LLMs address current rehabilitation issues, including data bias, contextual comprehension, and ethical concerns. Collaboration with healthcare experts and rigorous validation is crucial when deploying LLMs. Integrating LLMs into rehabilitation yields insights into this intricate process, enhancing data-driven decision-making, refining clinical practices, and predicting rehabilitation outcomes. Although challenges persist, LLMs represent a significant stride in rehabilitation, underscoring the importance of ethical use and collaboration.

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Classification of neurologic outcomes from medical notes using natural language processing

Cited by 12 publications

References 64 publications

Large Language Models in Neurology Research and Future Practice

Large Language Models in Neurology Research and Future Practice

Classification of Clinical Notes from a Heart Failure Telehealth Network

Unlocking the Black Box? A Comprehensive Exploration of Large Language Models in Rehabilitation

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