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Background: Stroke is a prevalent disease with a significant global impact. Effective assessment of stroke severity is vital for an accurate diagnosis, appropriate treatment, and optimal clinical outcomes. The National Institutes of Health Stroke Scale (NIHSS) is a widely used scale for quantitatively assessing stroke severity. However, the current manual scoring of NIHSS is labor-intensive, time-consuming, and sometimes unreliable. Applying artificial intelligence (AI) techniques to automate the quantitative assessment of stroke on vast amounts of electronic health records (EHRs) has attracted much interest., Objective: This study aims to develop an automatic, quantitative stroke severity assessment framework through automating the entire NIHSS scoring process on Chinese clinical EHRs., Methods: Our approach consists of two major parts: Chinese clinical named entity recognition (CNER) with a domain-adaptive pre-trained large language model (LLM) and automated NIHSS scoring. To build a high-performing CNER model, we first construct a stroke-specific, densely annotated dataset "Chinese Stroke Clinical Records" (CSCR) from EHRs provided by our partner hospital, based on a stroke ontology that defines semantically related entities for stroke assessment. We then pre-train a Chinese clinical LLM coined "CliRoberta" through domain-adaptive transfer learning and construct a deep learning-based CNER model that can accurately extract entities directly from Chinese EHRs. Finally, an automated, end-to-end NIHSS scoring pipeline is proposed by mapping the extracted entities to relevant NIHSS items and values, to quantitatively assess the stroke severity., Results: Results obtained on a benchmark dataset CCKS2019 and our newly created CSCR dataset demonstrate the superior performance of our domain-adaptive pre-trained LLM and the CNER model, compared with the existing benchmark LLMs and CNER models. The high F1 score of 0.990 ensures the reliability of our model in accurately extracting the entities for the subsequent automatic NIHSS scoring. Subsequently, our automated, end-to-end NIHSS scoring approach achieved excellent inter-rater agreement (0.823) and intraclass consistency (0.986) with the ground truth and significantly reduced the processing time from minutes to a few seconds., Conclusion: Our proposed automatic and quantitative framework for assessing stroke severity demonstrates exceptional performance and reliability through directly scoring the NIHSS from diagnostic notes in Chinese clinical EHRs. Moreover, this study also contributes a new clinical dataset, a pre-trained clinical LLM, and an effective deep learning-based CNER model. The deployment of these advanced algorithms can improve the accuracy and efficiency of clinical assessment, and help improve the quality, affordability and productivity of healthcare services., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Liver Cancer is a threat to human health and life over the world. The key to reduce liver cancer incidence is to identify high-risk populations and carry out individualized interventions before cancer occurrence. Building predictive models based on machine learning algorithms is an effective and economical way to forecast potential liver cancers. However, since the dataset is usually extremely skewed (negative samples are much more than positive samples), machine learning models suffer from severe bias and make unreliable predictions. In this paper, we systematically evaluate existing approaches in tackling class-imbalance problem and introduce two undersampling methods. The first is based on K-means++, where robust clustering centers are appointed as negative samples. The second is based on learning vector quantization, which considers diagnostic labels during clustering, and the prototypes are used as negative data. In this way, positive and negative samples are rebalanced. The algorithm is applied to five-year liver cancer prediction in Early Diagnosis and Treatment of Urban Cancer project in China. We achieve an AUC of 0.76 when no clinical measure except for epidemiological information is used. Experimental results show the advantage of our method over existing oversampling, undersampling, ensemble algorithms, and state-of-the-art outlier detection algorithms. This work explores a feasible and practical roadmap to tackle skewed medical data in cancer prediction and benefits applications targeted to human health and well-being. [ABSTRACT FROM AUTHOR]