13197 research outputs found
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Intrinsic field-effect mobility in thin-film transistor with polycrystalline In2O3 channel based on transfer length method
No full textField-effect mobility (μFE), calculated using transconductance in thin-film transistors (TFTs) includes error factors from determination of channel width/length and parasitic resistance (Rs/d) at source and drain regions. The apparent μFE is generally underestimated owing to the drain voltage drop due to Rs/d, which in turn, is caused by a low channel resistance (Rch) in high-mobility channels. This letter describes the extraction of intrinsic μFE (μFEi) in TFTs with polycrystalline In2O3 channels by separating Rs/d and Rch, based on the transfer length method. Using the proposed methodology, we obtained a high μFEi (>100 cm2 Vs−1) from TFT.journal articl
Performance Improvement of a Natural Language Processing Tool for Extracting Patient Narratives Related to Medical States From Japanese Pharmaceutical Care Records by Increasing the Amount of Training Data: Natural Language Processing Analysis and Validation Study
No full textBackground: Patients' oral expressions serve as valuable sources of clinical information to improve pharmacotherapy. Natural language processing (NLP) is a useful approach for analyzing unstructured text data, such as patient narratives. However, few studies have focused on using NLP for narratives in the Japanese language. Objective: We aimed to develop a high-performance NLP system for extracting clinical information from patient narratives by examining the performance progression with a gradual increase in the amount of training data. Methods: We used subjective texts from the pharmaceutical care records of Keio University Hospital from April 1, 2018, to March 31, 2019, comprising 12,004 records from 6559 cases. After preprocessing, we annotated diseases and symptoms within the texts. We then trained and evaluated a deep learning model (bidirectional encoder representations from transformers combined with a conditional random field [BERT-CRF]) through 10-fold cross-validation. The annotated data were divided into 10 subsets, and the amount of training data was progressively increased over 10 steps. We also analyzed the causes of errors. Finally, we applied the developed system to the analysis of case report texts to evaluate its usability for texts from other sources. Results: The F1-score of the system improved from 0.67 to 0.82 as the amount of training data increased from 1200 to 12,004 records. The F1-score reached 0.78 with 3600 records and was largely similar thereafter. As performance improved, errors from incorrect extractions decreased significantly, which resulted in an increase in precision. For case reports, the F1-score also increased from 0.34 to 0.41 as the training dataset expanded from 1200 to 12,004 records. Performance was lower for extracting symptoms from case report texts compared with pharmaceutical care records, suggesting that this system is more specialized for analyzing subjective data from pharmaceutical care records. Conclusions: We successfully developed a high-performance system specialized in analyzing subjective data from pharmaceutical care records by training a large dataset, with near-complete saturation of system performance with about 3600 training records. This system will be useful for monitoring symptoms, offering benefits for both clinical practice and research.journal articl
Improving Systematic Review Updates With Natural Language Processing Through Abstract Component Classification and Selection: Algorithm Development and Validation
No full textBackground: A challenge in updating systematic reviews is the workload in screening the articles. Many screening models using natural language processing technology have been implemented to scrutinize articles based on titles and abstracts. While these approaches show promise, traditional models typically treat abstracts as uniform text. We hypothesize that selective training on specific abstract components could enhance model performance for systematic review screening. Objective: We evaluated the efficacy of a novel screening model that selects specific components from abstracts to improve performance and developed an automatic systematic review update model using an abstract component classifier to categorize abstracts based on their components. Methods: A screening model was created based on the included and excluded articles in the existing systematic review and used as the scheme for the automatic update of the systematic review. A prior publication was selected for the systematic review, and articles included or excluded in the articles screening process were used as training data. The titles and abstracts were classified into 5 categories (Title, Introduction, Methods, Results, and Conclusion). Thirty-one component-composition datasets were created by combining 5 component datasets. We implemented 31 screening models using the component-composition datasets and compared their performances. Comparisons were conducted using 3 pretrained models: Bidirectional Encoder Representations from Transformer (BERT), BioLinkBERT, and BioM- Efficiently Learning an Encoder that Classifies Token Replacements Accurately (ELECTRA). Moreover, to automate the component selection of abstracts, we developed the Abstract Component Classifier Model and created component datasets using this classifier model classification. Using the component datasets classified using the Abstract Component Classifier Model, we created 10 component-composition datasets used by the top 10 screening models with the highest performance when implementing screening models using the component datasets that were classified manually. Ten screening models were implemented using these datasets, and their performances were compared with those of models developed using manually classified component-composition datasets. The primary evaluation metric was the F10-Score weighted by the recall. Results: A total of 256 included articles and 1261 excluded articles were extracted from the selected systematic review. In the screening models implemented using manually classified datasets, the performance of some surpassed that of models trained on all components (BERT: 9 models, BioLinkBERT: 6 models, and BioM-ELECTRA: 21 models). In models implemented using datasets classified by the Abstract Component Classifier Model, the performances of some models (BERT: 7 models and BioM-ELECTRA: 9 models) surpassed that of the models trained on all components. These models achieved an 88.6{\%} reduction in manual screening workload while maintaining high recall (0.93). Conclusions: Component selection from the title and abstract can improve the performance of screening models and substantially reduce the manual screening workload in systematic review updates. Future research should focus on validating this approach across different systematic review domains.journal articl
Evaluation of travel time to colorectal cancer care and survival: a cohort study using cancer registry data in Osaka Prefecture, Japan
No full textBackground: Cancer care in Japan faces a major challenge in maintaining equity in access and efficiency. Care is provided on the basis of catchment area, referred to as a secondary medical area (SMA); at least one designated cancer care hospital (DCCH) is placed in every SMA. We aimed to evaluate travel time and net survival by SMA among patients diagnosed with colorectal cancer (CRC) in Osaka Prefecture, Japan. Methods: We used cancer registry data for this cohort study and included patients diagnosed with CRC during 2013–2018. We evaluated equality in the utilisation of care by travel time between patients’ addresses and medical institutions for diagnosis or treatment in Osaka Prefecture. Travel time was compared by SMA of residence. We analysed which factors were associated with travel time using quantile regression. Efficiency was evaluated as un-standardised, age-standardised and stage-stratified three-year net survival by SMA of hospital for patients who received surgical resection. Results: Among the 53,301 patients, the estimated median travel time was 27 (interquartile range 14 to 61, 90th percentile 82) minutes. Travel time varied between SMAs of residence by 20 minutes and types of hospital (prefectural DCCH versus non-DCCH) by 15 minutes at most. Regarding net survival, all SMA of hospital were within the 99.8 % control limits. However, around 40 % of hospitals had annual surgical volume below ten. Conclusions: Travel time varied by SMA by 20 minutes at most. Although net survival was equalised across catchment areas, the current situation suggests an over-regionalisation of surgical care. The entire prefecture may need to reallocate resources to achieve higher efficiency. Policy Summary: Reconfiguring cancer care might be inevitable to cut the waste of resource inputs, but access equity should also be considered when centralising care.journal articl
Natural Language Processing-Based Approach to Detect Common Adverse Events of Anticancer Agents from Unstructured Clinical Notes: A Time-to-Event Analysis
No full textThis study assessed the effectiveness of natural language processing (NLP) in detecting adverse events (AEs) from anticancer agents by analyzing data from over 39,000 cancer patients. A specialized machine learning model identified known AEs from anticancer agents like capecitabine, oxaliplatin, and anthracyclines, revealing a significantly higher incidence in the treatment groups compared to non-users. While the NLP approach effectively detected most symptomatic AEs requiring manual review, it struggled with rarely documented conditions and commonly used clinical terms. Overall, the method shows promise for automated AE detection in medical records, particularly for symptoms without laboratory markers or diagnosis codes.conference pape
Towards a Risk-Secured Container Ecosystems in Cloud Environment: A Study on System, Human Insider and Human Capital Perspectives
Full text link奈良先端科学技術大学院大学博士（工学）doctoral thesi
Recommender System for Enterprise Resource Planning Package Components using Language Models
Full text link奈良先端科学技術大学院大学博士（工学）doctoral thesi
Enhancing Multibeam Satellite Communications through Selective Precoding
Full text link奈良先端科学技術大学院大学博士（工学）doctoral thesi
Functional characterization of LEARNED HEAT STRESS MEMORY 1 in Arabidopsis thaliana
Full text link奈良先端科学技術大学院大学博士（バイオサイエンス）doctoral thesi
