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Relevant bibliographies by topics / Predictive HCI models

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Journal articles

Academic literature on the topic 'Predictive HCI models'

Author: Grafiati

Published: 11 March 2023

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Journal articles on the topic "Predictive HCI models"

1

Paton, Chris, Andre W. Kushniruk, Elizabeth M. Borycki, Mike English, and Jim Warren. "Improving the Usability and Safety of Digital Health Systems: The Role of Predictive Human-Computer Interaction Modeling." Journal of Medical Internet Research 23, no. 5 (2021): e25281. http://dx.doi.org/10.2196/25281.

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In this paper, we describe techniques for predictive modeling of human-computer interaction (HCI) and discuss how they could be used in the development and evaluation of user interfaces for digital health systems such as electronic health record systems. Predictive HCI modeling has the potential to improve the generalizability of usability evaluations of digital health interventions beyond specific contexts, especially when integrated with models of distributed cognition and higher-level sociotechnical frameworks. Evidence generated from building and testing HCI models of the user interface (U

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2

Segkouli, Sofia, Ioannis Paliokas, Dimitrios Tzovaras, Thanos Tsakiris, Magda Tsolaki, and Charalampos Karagiannidis. "Novel Virtual User Models of Mild Cognitive Impairment for Simulating Dementia." Computational and Mathematical Methods in Medicine 2015 (2015): 1–15. http://dx.doi.org/10.1155/2015/358638.

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Virtual user modeling research has attempted to address critical issues of human-computer interaction (HCI) such as usability and utility through a large number of analytic, usability-oriented approaches as cognitive models in order to provide users with experiences fitting to their specific needs. However, there is demand for more specific modules embodied in cognitive architecture that will detect abnormal cognitive decline across new synthetic task environments. Also, accessibility evaluation of graphical user interfaces (GUIs) requires considerable effort for enhancing ICT products accessi

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3

Isiaka, Fatima, Kassim S. Mwitondi, and Adamu M. Ibrahim. "Detection of natural structures and classification of HCI-HPR data using robust forward search algorithm." International Journal of Intelligent Computing and Cybernetics 9, no. 1 (2016): 23–41. http://dx.doi.org/10.1108/ijicc-08-2015-0029.

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Purpose – The purpose of this paper is to proposes a forward search algorithm for detecting and identifying natural structures arising in human-computer interaction (HCI) and human physiological response (HPR) data. Design/methodology/approach – The paper portrays aspects that are essential to modelling and precision in detection. The methods involves developed algorithm for detecting outliers in data to recognise natural patterns in incessant data such as HCI-HPR data. The detected categorical data are simultaneously labelled based on the data reliance on parametric rules to predictive models

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4

Krabbe, Laura-Maria, Aditya Bagrodia, Ahmed Q. Haddad, et al. "Multi-institutional validation of the predictive value of Ki-67 in patients with high-grade urothelial carcinoma of the upper urinary tract." Journal of Clinical Oncology 33, no. 7_suppl (2015): 371. http://dx.doi.org/10.1200/jco.2015.33.7_suppl.371.

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371 Background: To validate the independent predictive value of Ki-67 in patients with high-grade upper tract urothelial carcinoma (UTUC). Methods: 475 patients from the international UTUC collaboration who underwent extirpative surgery for high-grade UTUC were included in this study. Immunohistochemical staining for Ki-67 was performed on tissue microarray (TMA) formed from this patient cohort. Ki-67 expression was assessed in a semi-quantitative fashion and considered overexpressed at a cut-off of 20%. Multivariate analyses (MVA) were performed to assess independent predictors of oncological

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5

Bakaev, Maxim, Sebastian Heil, and Martin Gaedke. "Reasonable Effectiveness of Features in Modeling Visual Perception of User Interfaces." Big Data and Cognitive Computing 7, no. 1 (2023): 30. http://dx.doi.org/10.3390/bdcc7010030.

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Training data for user behavior models that predict subjective dimensions of visual perception are often too scarce for deep learning methods to be applicable. With the typical datasets in HCI limited to thousands or even hundreds of records, feature-based approaches are still widely used in visual analysis of graphical user interfaces (UIs). In our paper, we benchmarked the predictive accuracy of the two types of neural network (NN) models, and explored the effects of the number of features, and the dataset volume. To this end, we used two datasets that comprised over 4000 webpage screenshots

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6

Lee, Jae Seung, Tae Seop Lim, Hye Won Lee, et al. "Suboptimal Performance of Hepatocellular Carcinoma Prediction Models in Patients with Hepatitis B Virus-Related Cirrhosis." Diagnostics 13, no. 1 (2022): 3. http://dx.doi.org/10.3390/diagnostics13010003.

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This study aimed to evaluate the predictive performance of pre-existing well-validated hepatocellular carcinoma (HCC) prediction models, established in patients with HBV-related cirrhosis who started potent antiviral therapy (AVT). We retrospectively reviewed the cases of 1339 treatment-naïve patients with HBV-related cirrhosis who started AVT (median period, 56.8 months). The scores of the pre-existing HCC risk prediction models were calculated at the time of AVT initiation. HCC developed in 211 patients (15.1%), and the cumulative probability of HCC development at 5 years was 14.6%. Multivar

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7

Nishida, Nao, Jun Ohashi, Goki Suda, et al. "Prediction Model with HLA-A*33:03 Reveals Number of Days to Develop Liver Cancer from Blood Test." International Journal of Molecular Sciences 24, no. 5 (2023): 4761. http://dx.doi.org/10.3390/ijms24054761.

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The development of liver cancer in patients with hepatitis B is a major problem, and several models have been reported to predict the development of liver cancer. However, no predictive model involving human genetic factors has been reported to date. For the items incorporated in the prediction model reported so far, we selected items that were significant in predicting liver carcinogenesis in Japanese patients with hepatitis B and constructed a prediction model of liver carcinogenesis by the Cox proportional hazard model with the addition of Human Leukocyte Antigen (HLA) genotypes. The model,

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8

Lim, Jihye, Young Eun Chon, Mi Na Kim, et al. "Cirrhosis, Age, and Liver Stiffness-Based Models Predict Hepatocellular Carcinoma in Asian Patients with Chronic Hepatitis B." Cancers 13, no. 22 (2021): 5609. http://dx.doi.org/10.3390/cancers13225609.

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Objectives: Predicting hepatocellular carcinoma (HCC) in patients with chronic hepatitis B who received long-term therapy with potent nucleos(t)ide analogs is of utmost importance to refine the strategy for HCC surveillance. Methods: We conducted a multicenter retrospective cohort study to validate the CAGE-B and SAGE-B scores, HCC prediction models developed for Caucasian patients receiving entecavir (ETV) or tenofovir (TFV) for >5 years. Consecutive patients who started ETV or TFV at two hospitals in Korea from January 2009 to December 2015 were identified. The prediction scores were calc

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9

Li, Huikai, Han Mu, Yajie Xiao, Zhikun Zhao, Xiaoli Cui, and Dongfang Wu. "Comprehensive Analysis of Histone Modifications in Hepatocellular Carcinoma Reveals Different Subtypes and Key Prognostic Models." Journal of Oncology 2022 (August 1, 2022): 1–20. http://dx.doi.org/10.1155/2022/5961603.

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Histone modification, an important epigenetic mechanism, is related to the carcinogenesis of hepatocellular carcinoma (HCC). In three datasets, we screened 88 epigenetic-dysregulated PCGs (epi-PCGs) , which were significantly associated with HCC survival and could cluster HCC into three molecular subtypes. These subtypes were associated with prognosis, immunomodulatory alterations, and response to different treatment strategies. Based on 88 epi-PCGs in the TCGA training set, a risk prediction model composed of 4 epi-PCGs was established. The model was closely related to the clinicopathological

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10

Li, Yao, Wei Wang, Weisheng Zeng, Jianjun Wang, and Jinghui Meng. "Development of Crown Ratio and Height to Crown Base Models for Masson Pine in Southern China." Forests 11, no. 11 (2020): 1216. http://dx.doi.org/10.3390/f11111216.

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Crown ratio (CR) and height to crown base (HCB) are important crown characteristics influencing the behavior of forest canopy fires. However, the labor-intensive and costly measurement of CR and HCB have hindered their wide application to forest fire management. Here, we use 301 sample trees collected in 11 provinces in China to produce predictive models of CR and HCB for Masson pine forests (Pinus massoniana Lamb.), which are vulnerable to forest canopy fires. We first identified the best basic model that used only diameter at breast height (DBH) and height (H) as independent variables to pre

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