A hybrid algorithm for clinical decision support in precision medicine based on machine learning

Zhang, Zicheng; Lin, Xinyue; Wu, Shanshan

doi:10.1186/s12859-022-05116-9

Cited by 5 publications

(2 citation statements)

References 57 publications

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“…Datasets associated with TREC, such as TREC-COVID and Health Misinformation, have been employed in four of the reviewed research papers [64, 65, 66, 67]. One paper also utilized three datasets of the TREC series 2017-2019 for clinical decision support [68].…”

Section: Resultsmentioning

confidence: 99%

Large Language Models to process, analyze, and synthesize biomedical texts – a scoping review

Doneva,

Qin,

Sick

et al. 2024

Preprint

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The advent of large language models (LLMs) such as BERT and, more recently, GPT, is transforming our approach of analyzing and understanding biomedical texts. To stay informed about the latest advancements in this area, there is a need for up-to-date summaries on the impact of LLM on Natural Language Processing (NLP) in the biomedical field. Thus, this scoping review aims to provide a detailed overview of the current state of biomedical NLP research and its applications, with a special focus on the evolving role of LLMs. We conducted a systematic search of PubMed, EMBASE, and Google Scholar for studies and conference proceedings published from 2017 to December 19, 2023, that develop or utilize LLMs for NLP tasks in biomedicine. From 13,823 references, we selected 199 publications and conference proceedings for our review. LLMs are being applied to a wide array of tasks in the biomedical field, including knowledge management, text mining, drug discovery, and evidence synthesis. Prominent among these tasks are text classification, relation extraction, and named entity recognition. Although BERT-based models remain prevalent, the use of GPT-based models has substantially increased since 2023.

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Section: Resultsmentioning

confidence: 99%

Large Language Models to process, analyze, and synthesize biomedical texts – a scoping review

Doneva,

Qin,

Sick

et al. 2024

Preprint

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show abstract

“…However, finding relevant research can be challenging 2 . To address this issue, there has been increasing research interest in utilizing Natural Language Processing (NLP) and Information Retrieval (IR) techniques to retrieve relevant articles or similar patients for assisting patient management 3 – 7 . In this article, we introduce the term “Retrieval-based Clinical Decision Support” (ReCDS) to describe these tasks.…”

Section: Background and Summarymentioning

confidence: 99%

A large-scale dataset of patient summaries for retrieval-based clinical decision support systems

Zhao,

Jin,

Chen

et al. 2023

Sci Data

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Retrieval-based Clinical Decision Support (ReCDS) can aid clinical workflow by providing relevant literature and similar patients for a given patient. However, the development of ReCDS systems has been severely obstructed by the lack of diverse patient collections and publicly available large-scale patient-level annotation datasets. In this paper, we collect a novel dataset of patient summaries and relations called PMC-Patients to benchmark two ReCDS tasks: Patient-to-Article Retrieval (ReCDS-PAR) and Patient-to-Patient Retrieval (ReCDS-PPR). Specifically, we extract patient summaries from PubMed Central articles using simple heuristics and utilize the PubMed citation graph to define patient-article relevance and patient-patient similarity. PMC-Patients contains 167k patient summaries with 3.1 M patient-article relevance annotations and 293k patient-patient similarity annotations, which is the largest-scale resource for ReCDS and also one of the largest patient collections. Human evaluation and analysis show that PMC-Patients is a diverse dataset with high-quality annotations. We also implement and evaluate several ReCDS systems on the PMC-Patients benchmarks to show its challenges and conduct several case studies to show the clinical utility of PMC-Patients.

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From machine learning to deep learning: Advances of the recent data-driven paradigm shift in medicine and healthcare

Chakraborty,

Bhattacharya,

Pal

et al. 2024

Current Research in Biotechnology

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A hybrid algorithm for clinical decision support in precision medicine based on machine learning

Cited by 5 publications

References 57 publications

Large Language Models to process, analyze, and synthesize biomedical texts – a scoping review

Large Language Models to process, analyze, and synthesize biomedical texts – a scoping review

A large-scale dataset of patient summaries for retrieval-based clinical decision support systems

From machine learning to deep learning: Advances of the recent data-driven paradigm shift in medicine and healthcare

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