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A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601). This special issue belongs to the section "Public Health Statistics and Risk Assessment".

Deadline for manuscript submissions: closed (31 August 2023) | Viewed by 6064

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Dr. I-Shiang Tzeng

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Department of Research, Taipei Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, New Taipei City 23142, Taiwan
Interests: genetic risk score; differentially expressed genes; data mining and analysis; machine learning; artificial intelligence; epidemiology; biostatistics; bioinformatics
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Special Issue Information

Dear Colleagues,

Biostatistics refers the development and application of statistical methods in biology, medicine, public health, and other life sciences, which plays an important role in epidemiology and public health. Epidemiology can be used to guide public health affairs, and assist to plan the healthcare and health management of individuals. The main scope of this special issue is to invite scholars to introduce and apply advanced biostatistics methods to address questions in epidemiology and public health, thereby improve the efficacy and safety of clinical decisions. The topics will be included but not limited to the following:

Clinical trials in cancer patients with different size or/and at various treatment stage;
The effect of air pollution, water contamination and other environmental factors on people health and well-being through data analysis;
Role of statistical genetics on particular diseases;
Application of biostatistics in the growth of adolescents;
Biostatistics modeling.

Dr. I-Shiang Tzeng
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. International Journal of Environmental Research and Public Health is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2500 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

clinical trial
treatment
environmental effect
statistical genetics
particular diseases
epidemiology
public health
biostatistics modeling

Published Papers (3 papers)

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Research

15 pages, 5223 KiB

Open AccessArticle

Pill Box Text Identification Using DBNet-CRNN

by Liuqing Xiang, Hanyun Wen and Ming Zhao

Int. J. Environ. Res. Public Health 2023, 20(5), 3881; https://doi.org/10.3390/ijerph20053881 - 22 Feb 2023

Cited by 1 | Viewed by 1612

Abstract

The recognition process of natural scenes is complicated at present, and images themselves may be complex owing to the special features of natural scenes. In this study, we use the detection and recognition of pill box text as an application scenario and design a deep-learning-based text detection algorithm for such natural scenes. We propose an end-to-end graphical text detection and recognition model and implement a detection system based on the B/S research application for pill box recognition, which uses DBNet as the text detection framework and a convolutional recurrent neural network (CRNN) as the text recognition framework. No prior image preprocessing is required in the detection and recognition processes. The recognition result from the back-end is returned to the front-end display. Compared with traditional methods, this recognition process reduces the complexity of preprocessing prior to image detection and improves the simplicity of the model application. Experiments on the detection and recognition of 100 pill boxes demonstrate that the proposed method achieves better accuracy in text localization and recognition results than the previous CTPN + CRNN method. The proposed method is significantly more accurate and easier to use than the traditional approach in terms of both training and recognition processes. Full article

(This article belongs to the Special Issue Enhancement of Public Health Professionals via Biostatistics and Informatics)

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13 pages, 2263 KiB

Open AccessArticle

Multi-Task Joint Learning Model for Chinese Word Segmentation and Syndrome Differentiation in Traditional Chinese Medicine

by Chenyuan Hu, Shuoyan Zhang, Tianyu Gu, Zhuangzhi Yan and Jiehui Jiang

Int. J. Environ. Res. Public Health 2022, 19(9), 5601; https://doi.org/10.3390/ijerph19095601 - 05 May 2022

Cited by 13 | Viewed by 1851

Abstract

Evidence-based treatment is the basis of traditional Chinese medicine (TCM), and the accurate differentiation of syndromes is important for treatment in this context. The automatic differentiation of syndromes of unstructured medical records requires two important steps: Chinese word segmentation and text classification. Due to the ambiguity of the Chinese language and the peculiarities of syndrome differentiation, these tasks pose a daunting challenge. We use text classification to model syndrome differentiation for TCM, and use multi-task learning (MTL) and deep learning to accomplish the two challenging tasks of Chinese word segmentation and syndrome differentiation. Two classic deep neural networks—bidirectional long short-term memory (Bi-LSTM) and text-based convolutional neural networks (TextCNN)—are fused into MTL to simultaneously carry out these two tasks. We used our proposed method to conduct a large number of comparative experiments. The experimental comparisons showed that it was superior to other methods on both tasks. Our model yielded values of accuracy, specificity, and sensitivity of 0.93, 0.94, and 0.90, and 0.80, 0.82, and 0.78 on the Chinese word segmentation task and the syndrome differentiation task, respectively. Moreover, statistical analyses showed that the accuracies of the non-joint and joint models were both within the 95% confidence interval, with

p_{v a l u e}

< 0.05. The experimental comparison showed that our method is superior to prevalent methods on both tasks. The work here can help modernize TCM through intelligent differentiation. Full article

(This article belongs to the Special Issue Enhancement of Public Health Professionals via Biostatistics and Informatics)

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10 pages, 1697 KiB

Open AccessArticle

Exploring Hepatocellular Carcinoma Mortality Using Weighted Regression Estimation for the Cohort Effect in Taiwan from 1976 to 2015

by I-Shiang Tzeng and Jiann-Hwa Chen

Int. J. Environ. Res. Public Health 2022, 19(9), 5573; https://doi.org/10.3390/ijerph19095573 - 04 May 2022

Cited by 3 | Viewed by 1548

Abstract

To estimate the cohort effects that remove the efficacy of age and the period in the age-period statistics of a contingency table, the multiphase method is put forward. Hepatocellular carcinoma (HCC) is one of the most common malignancies of the liver. Understanding the predictive effects of age, period, and cohort on HCC mortality trends may help to estimate the future HCC burden, identify etiological factors, and advise public health prevention programs. Estimates of future HCC mortality and the associated health burden were forecast using an age–period–cohort (APC) model of analysis. By running a regression of residuals that were isolated from the median polish stage of cohort classification, the study controlled for HCC mortality confounding variables and interpreted time trends in HCC rates. The literature shows that the weighted mean estimation derived from the confidence interval (CI) is relatively restricted (compared to the equal-weighted evaluation). This study aimed to illustrate the effects of age, period, and cohort on the incidence and mortality rates, along with the weight equivalent to the segment of death number caused by HCC in each cohort. The objective of that work was to evaluate the proposed method for appraising cohort effects within the age-period data of contingency tables. The weighted mean estimate from the regression model was found to be robust and thus warrants consideration in forecasting future HCC mortality trends. The final phase was factored in to calculate the magnitude of cohort effects. In conclusion, owing to the relatively constricted CI and small degree of uncertainty, the weighted mean estimates can be used for projections based on simple linear extrapolation. Full article

(This article belongs to the Special Issue Enhancement of Public Health Professionals via Biostatistics and Informatics)

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