Advances in knowledge discovery and data mining : ... Pacific-Asia Conference, PAKDD ..., proceedings. Pacific-Asia Conference on Knowledge Discovery and Data Mining最新文献

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Using Multimodal Data to Improve Precision of Inpatient Event Timelines. 利用多模态数据提高住院患者事件时间表的精确性。

Advances in knowledge discovery and data mining : ... Pacific-Asia Conference, PAKDD ..., proceedings. Pacific-Asia Conference on Knowledge Discovery and Data Mining Pub Date : 2024-05-01 DOI: 10.1007/978-981-97-2238-9_25

Gabriel Frattallone-Llado, Juyong Kim, Cheng Cheng, Diego Salazar, Smitha Edakalavan, Jeremy C Weiss

{"title":"Using Multimodal Data to Improve Precision of Inpatient Event Timelines.","authors":"Gabriel Frattallone-Llado, Juyong Kim, Cheng Cheng, Diego Salazar, Smitha Edakalavan, Jeremy C Weiss","doi":"10.1007/978-981-97-2238-9_25","DOIUrl":"10.1007/978-981-97-2238-9_25","url":null,"abstract":"Textual data often describe events in time but frequently contain little information about their specific timing, whereas complementary structured data streams may have precise timestamps but may omit important contextual information. We investigate the problem in healthcare, where we produce clinician annotations of discharge summaries, with access to either unimodal (text) or multimodal (text and tabular) data, (i) to determine event interval timings and (ii) to train multimodal language models to locate those events in time. We find our annotation procedures, dashboard tools, and annotations result in high-quality timestamps. Specifically, the multimodal approach produces more precise timestamping, with uncertainties of the lower bound, upper bounds, and duration reduced by 42% (95% CI 34-51%), 36% (95% CI 28-44%), and 13% (95% CI 10-17%), respectively. In the classification version of our task, we find that, trained on our annotations, our multimodal BERT model outperforms unimodal BERT model and Llama-2 encoder-decoder models with improvements in F1 scores for upper (10% and 61%, respectively) and lower bounds (8% and 56%, respectively). The code for the annotation tool and the BERT model is available (link).","PeriodicalId":517371,"journal":{"name":"Advances in knowledge discovery and data mining : ... Pacific-Asia Conference, PAKDD ..., proceedings. Pacific-Asia Conference on Knowledge Discovery and Data Mining","volume":"14648 ","pages":"322-334"},"PeriodicalIF":0.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11228894/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141565577","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MISNN: Multiple Imputation via Semi-parametric Neural Networks. MISNN：通过半参数神经网络进行多重估算。

Zhiqi Bu, Zongyu Dai, Yiliang Zhang, Qi Long

{"title":"MISNN: Multiple Imputation via Semi-parametric Neural Networks.","authors":"Zhiqi Bu, Zongyu Dai, Yiliang Zhang, Qi Long","doi":"10.1007/978-3-031-33374-3_34","DOIUrl":"10.1007/978-3-031-33374-3_34","url":null,"abstract":"Multiple imputation (MI) has been widely applied to missing value problems in biomedical, social and econometric research, in order to avoid improper inference in the downstream data analysis. In the presence of high-dimensional data, imputation models that include feature selection, especially <math><mrow><msub><mo>ℓ</mo><mn>1</mn></msub></mrow></math> regularized regression (such as Lasso, adaptive Lasso, and Elastic Net), are common choices to prevent the model from underdetermination. However, conducting MI with feature selection is difficult: existing methods are often computationally inefficient and poor in performance. We propose MISNN, a novel and efficient algorithm that incorporates feature selection for MI. Leveraging the approximation power of neural networks, MISNN is a general and flexible framework, compatible with any feature selection method, any neural network architecture, high/low-dimensional data and general missing patterns. Through empirical experiments, MISNN has demonstrated great advantages over state-of-the-art imputation methods (e.g. Bayesian Lasso and matrix completion), in terms of imputation accuracy, statistical consistency and computation speed.","PeriodicalId":517371,"journal":{"name":"Advances in knowledge discovery and data mining : ... Pacific-Asia Conference, PAKDD ..., proceedings. Pacific-Asia Conference on Knowledge Discovery and Data Mining","volume":"13935 ","pages":"430-442"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10869892/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139901038","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0