Determining the relatedness of publications by detecting similarities and connections between researchers and their outputs can help science stakeholders worldwide to find areas of common interest and potential collab...
Determining the relatedness of publications by detecting similarities and connections between researchers and their outputs can help science stakeholders worldwide to find areas of common interest and potential collaboration. To this end, many studies have tried to explore authorship attribution and research similarity detection through the use of automatic approaches. Nonetheless, inferring author research relatedness from imperfect data containing errors and multiple references to the same entities is a long-standing challenge. In a previous study, we conducted an experiment where a homogeneous crowd of volunteers contributed to a set of author name disambiguation tasks. The results demonstrated an overall accuracy higher than 75% and we also found important effects tied to the confidence level indicated by participants in correct answers. However, this study left many open questions regarding the comparative accuracy of a large heterogeneous crowd with monetary rewards involved. This paper seeks to address some of these unanswered questions by repeating the experiment with a crowd of 140 online paid workers recruited via MTurk’s microtask crowdsourcing platform. Our replication study shows high accuracy for name disambiguation tasks based on authorship-level information and content features. These findings can be of greater informative value since they also explore hints of crowd behavior activity in terms of time duration and mean proportion of clicks per worker with implications for interface and interaction design.
Isobaric labeling relative quantitation is one of the dominating proteomic quantitation technologies. Traditional quantitation pipelines for isobaric-labeled mass spectrometry data are based on sequence database searc...
Background and Objective: Machine learning models are vital for enhancing healthcare services. However, integrating them into health information systems (HISs) introduces challenges beyond clinical decision making, su...
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Background and Objective: Machine learning models are vital for enhancing healthcare services. However, integrating them into health information systems (HISs) introduces challenges beyond clinical decision making, such as interoperability and diverse electronic health records (EHR) formats. We proposed Model Cabinet Architecture (MoCab), a framework designed to leverage fast healthcare interoperability resources (FHIR) as the standard for data storage and retrieval when deploying machine learning models across various HISs, addressing the challenges highlighted by platforms such as EPOCH®, ePRISM®, KETOS, and others. Methods: The MoCab architecture is designed to streamline predictive modeling in healthcare through a structured framework incorporating several specialized parts. The Data Service Center manages patient data retrieval from FHIR servers. These data are then processed by the Knowledge Model Center, where they are formatted and fed into predictive models. The Model Retraining Center is crucial in continuously updating these models to maintain accuracy in dynamic clinical environments. The framework further incorporates Clinical Decision Support (CDS) Hooks for issuing clinical alerts. It uses Substitutable Medical Apps Reusable Technologies (SMART) on FHIR to develop applications for displaying alerts, prediction results, and patient records. Results: The MoCab framework was demonstrated using three types of predictive models: a scoring model (qCSI), a machine learning model (NSTI), and a deep learning model (SPC), applied to synthetic data that mimic a major EHR system. The implementations showed how MoCab integrates predictive models with health data for clinical decision support, utilizing CDS Hooks and SMART on FHIR for seamless HIS integration. The demonstration confirmed the practical utility of MoCab in supporting clinical decision making, validated by its application in various healthcare settings. Conclusions: We demonstrate MoCab's potential in
Deep learning has revolutionized medical imaging, offering advanced methods for accurate diagnosis and treatment planning. The BCLC staging system is crucial for staging Hepatocellular Carcinoma (HCC), a high-mortalit...
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ISBN:
(数字)9798350351552
ISBN:
(纸本)9798350351569
Deep learning has revolutionized medical imaging, offering advanced methods for accurate diagnosis and treatment planning. The BCLC staging system is crucial for staging Hepatocellular Carcinoma (HCC), a high-mortality cancer. An automated BCLC staging system could significantly enhance diagnosis and treatment planning efficiency. However, we found that BCLC staging, which is directly related to the size and number of liver tumors, aligns well with the principles of the Multiple Instance Learning (MIL) framework. To effectively achieve this, we proposed a new preprocessing technique called Masked Cropping and Padding(MCP), which addresses the variability in liver volumes and ensures consistent input sizes. This technique preserves the structural integrity of the liver, facilitating more effective learning. Furthermore, we introduced Re ViT, a novel hybrid model that integrates the local feature extraction capabilities of Convolutional Neural Networks (CNNs) with the global context modeling of Vision Transformers (ViTs). Re ViT leverages the strengths of both architectures within the MIL framework, enabling a robust and accurate approach for BCLC staging. We will further explore the trade-off between performance and interpretability by employing TopK Pooling strategies, as our model focuses on the most informative instances within each bag.
The Barcelona Clinic Liver Cancer (BCLC) staging system plays a crucial role in clinical planning, offering valuable insights for effectively managing hepatocellular carcinoma. Accurate prediction of BCLC stages can s...
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ISBN:
(数字)9798350371499
ISBN:
(纸本)9798350371505
The Barcelona Clinic Liver Cancer (BCLC) staging system plays a crucial role in clinical planning, offering valuable insights for effectively managing hepatocellular carcinoma. Accurate prediction of BCLC stages can significantly ease the workload on radiologists. However, few datasets are explicitly designed for discerning BCLC stages. Despite the common practice of appending BCLC labels to clinical data within datasets, the inherent imbalance in BCLC distribution is further amplified by the diverse purposes for which datasets are curated. In this study, we aim to develop a BCLC staging system using the advanced Swin Transformer model. Additionally, we explore the integration of two datasets, each originally intended for separate objectives, highlighting the critical challenge of preserving class distribution in practical study designs. This exploration is pivotal for ensuring the applicability of our developed staging system in the designed clinical settings. Our resulting BCLC staging system demonstrates an accuracy of 55.81% (±7.8%), contributing to advancing medical image-based research for predicting BCLC stages.
Machine learning (ML) in healthcare presents numerous opportunities for enhancing patient care, population health, and healthcare providers' workflows. However, the real-world clinical and cost benefits remain lim...
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Since the emergence of COVID-19, discussions of ongoing pandemic-related research have accounted for an unprecedented share of media coverage and debate in the public sphere1. The urgency of the pandemic forced resear...
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Background: Infectious disease computational modeling studies have been widely published during the coronavirus disease 2019 (COVID-19) pandemic, yet they have limited reproducibility. Developed through an iterative t...
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Background: Infectious disease computational modeling studies have been widely published during the coronavirus disease 2019 (COVID-19) pandemic, yet they have limited reproducibility. Developed through an iterative testing process with multiple reviewers, the Infectious Disease Modeling Reproducibility Checklist (IDMRC) enumerates the minimal elements necessary to support reproducible infectious disease computational modeling publications. The primary objective of this study was to assess the reliability of the IDMRC and to identify which reproducibility elements were unreported in a sample of COVID-19 computational modeling publications. Methods: Four reviewers used the IDMRC to assess 46 preprint and peer reviewed COVID-19 modeling studies published between March 13th, 2020, and July 30th, 2020. The inter-rater reliability was evaluated by mean percent agreement and Fleiss’ kappa coefficients (κ). Papers were ranked based on the average number of reported reproducibility elements, and average proportion of papers that reported each checklist item were tabulated. Results: Questions related to the computational environment (mean κ = 0.90, range = 0.90–0.90), analytical software (mean κ = 0.74, range = 0.68–0.82), model description (mean κ = 0.71, range = 0.58–0.84), model implementation (mean κ = 0.68, range = 0.39–0.86), and experimental protocol (mean κ = 0.63, range = 0.58–0.69) had moderate or greater (κ > 0.41) inter-rater reliability. Questions related to data had the lowest values (mean κ = 0.37, range = 0.23–0.59). Reviewers ranked similar papers in the upper and lower quartiles based on the proportion of reproducibility elements each paper reported. While over 70% of the publications provided data used in their models, less than 30% provided the model implementation. Conclusions: The IDMRC is the first comprehensive, quality-assessed tool for guiding researchers in reporting reproducible infectious disease computational modeling studies. The inter-rater re
Intrusion Detection System provides services related to surveillance of computer security, as one of the minimum components that must exist in a computer network architecture. Regarding the adoption of cloud technolog...
Intrusion Detection System provides services related to surveillance of computer security, as one of the minimum components that must exist in a computer network architecture. Regarding the adoption of cloud technology, many users have switched to using cloud computers to operate servers, applications, or the web in cloud computing. To meet the security needs of users, various open source and commercial tools are being developed. Although many developments have taken place in the IDS area, in cloud-based IDS many challenges such as security, interoperability, resource scheduling, virtualization still need to be improved. This paper reviews the paradigms and surveys about the Intrusion Detection System that runs on cloud computing in terms of concepts, technology, tools, and various challenges. A systematic literature review of selected papers, published from 2016 to 2020, was carried out to properly understand the Intrusion detection System paradigm in cloud computing and the security challenges faced in cloud computing. This review paper helps researchers who want to start their research careers in the cloud computing-based Intrusion Detection System
Software development process includes estimating effort as a crucial task. The Use Case Point Analysis (UCPA) is a well know size metric that can be used to calculate effort. The software size is measured using use ca...
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ISBN:
(纸本)9781665442329
Software development process includes estimating effort as a crucial task. The Use Case Point Analysis (UCPA) is a well know size metric that can be used to calculate effort. The software size is measured using use case diagrams in the UCPA method, using the calculated software size the effort required to complete the project is estimated. The traditional effort estimation with statistical methods is not accurate when compared to the real effort. In-accurate estimation of effort leads to problems with cost calculation and human resources calculation and it might lead to project failure. Machine learning techniques based on regression might help in estimating the effort with accuracy. In this study we proposed a method to identify best performing regression based machine learning model using two data sets Dataset1, Dataset2. Ensembles of different Machine learning methods is created and compared with individual methods and other ensemble methods to find an accurate estimation model. The results shows the individual model SVR and ensemble model GBR gives the best performance with a regression score above 98% with both data sets.
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