Medical Dialogue Summarization for Automated Reporting in Healthcare

Molenaar, Sabine; Maas, Lientje; Burriel, Verónica; Dalpiaz, Fabiano; Brinkkemper, Sjaak

doi:10.1007/978-3-030-49165-9_7

Cited by 15 publications

(12 citation statements)

References 24 publications

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“…Medical conversation summarization can help medical providers to keep a record of patient encounters and also provide the necessary context of a patient's medical history during patient hand-offs between providers. Existing studies have leveraged techniques from computational linguistics [99], NLP (PEGASUS [100]), pretrained language models and low-shot learning to collect labelled data and perform medical dialogue summarization.…”

Section: Applicationsmentioning

confidence: 99%

Generative AI models in time varying biomedical data: a systematic review (Preprint)

He,

Sarwal,

Qiu

et al. 2024

Preprint

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BACKGROUND Trajectory modeling is a longstanding challenge in the application of computational methods to healthcare. However, traditional statistical and machine learning methods do not achieve satisfactory results as they often fail to capture the complex underlying distributions of multi-modal health data, and long-term dependencies throughout patients’ medical histories. Recent advances in generative AI have provided powerful tools to represent complex distributions and patterns with minimal underlying assumptions. These have had a major impact in fields such as finance and environmental sciences, and recently researchers have turned to these methods for disease modeling. OBJECTIVE While AI methods have proven powerful, their application in clinical practice remains limited due to their black-box like nature. The proliferation of AI algorithms poses a significant challenge for non-developers to track and incorporate these advances into clinical research and application. In this work, we survey peer-reviewed, generative AI model papers with specific applications in time series health data. METHODS Our search includes single- and multi-modal generative AI models that operate over structured and unstructured data, medical imaging or multi-omics data. We introduce current generative AI methods, review their applications in each data modality and discuss their strengths and weaknesses compared to traditional methods. RESULTS We follow the PRISMA guideline and review 155 articles on generative AI applications in time series healthcare data across modalities. Furthermore, we offer a systematic framework for clinicians to easily identify suitable AI methods for their data and task at hand. CONCLUSIONS We review and critique existing applications of generative AI to time series health data with the aim of bridging the gap between computational methods and clinical application. We also identify shortcomings of existing approaches, and highlight recent advances in generative AI that represent promising directions for healthcare modeling.

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Section: Applicationsmentioning

confidence: 99%

Generative AI models in time varying biomedical data: a systematic review (Preprint)

He,

Sarwal,

Qiu

et al. 2024

Preprint

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“…Text processing refers to the analysis and manipulation of text to find key information. Considering the continuous increase in free text clinical notes, text processing has shown promising applications [ 68 , 106 ]. The text processing method processes free texts in clinical records and scientific articles and extracts clinically relevant information by utilizing machine learning (ML) and DL models.…”

Section: Text Processing and Deep Learning Overviewmentioning

confidence: 99%

Optimizing healthcare system by amalgamation of text processing and deep learning: a systematic review

Rani

Jain

2023

Multimed Tools Appl

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The explosion of clinical textual data has drawn the attention of researchers. Owing to the abundance of clinical data, it is becoming difficult for healthcare professionals to take real-time measures. The tools and methods are lacking when compared to the amount of clinical data generated every day. This review aims to survey the text processing pipeline with deep learning methods such as CNN, RNN, LSTM, and GRU in the healthcare domain and discuss various applications such as clinical concept detection and extraction, medically aware dialogue systems, sentiment analysis of drug reviews shared online, clinical trial matching, and pharmacovigilance. In addition, we highlighted the major challenges in deploying text processing with deep learning to clinical textual data and identified the scope of research in this domain. Furthermore, we have discussed various resources that can be used in the future to optimize the healthcare domain by amalgamating text processing and deep learning.

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“…In the future, datasets with documents of rich diversity are desperately required to promote the research of multi-document summarization. Meanwhile, according to application requirements, datasets in cross-domains are ought to be collected, for example, medical records or dialogue summarization [86], email summarization [124,145], code summarization [80,108], software project activities summarization [2], legal documents summarization [57]. The development of large-scale cross-task datasets will facilitate multi-task learning [34,135].…”

Section: Creating More Datasets For Multi-document Summarizationmentioning

confidence: 99%

Multi-document Summarization via Deep Learning Techniques: A Survey

Ma¹,

Zhang²,

Guo³

et al. 2020

Preprint

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Multi-document summarization (MDS) is an effective tool for information aggregation which generates an informative and concise summary from a cluster of topic-related documents. Our survey structurally overviews the recent deep learning based multi-document summarization models via a proposed taxonomy and it is the first of its kind. Particularly, we propose a novel mechanism to summarize the design strategies of neural networks and conduct a comprehensive summary of the state-of-the-art. We highlight the differences among various objective functions which are rarely discussed in the existing literature. Finally, we propose several future directions pertaining to this new and exciting development of the field.

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Medical Dialogue Summarization for Automated Reporting in Healthcare

Cited by 15 publications

References 24 publications

Generative AI models in time varying biomedical data: a systematic review (Preprint)

Generative AI models in time varying biomedical data: a systematic review (Preprint)

Optimizing healthcare system by amalgamation of text processing and deep learning: a systematic review

Multi-document Summarization via Deep Learning Techniques: A Survey

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