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Public Health Public Health

Heatstroke predictions by machine learning, weather information, and an all-population registry for 12-hour heatstroke alerts.

In Nature communications ; h5-index 260.0

This study aims to develop and validate prediction models for the number of all heatstroke cases, and heatstrokes of hospital admission and death cases per city per 12 h, using multiple weather information and a population-based database for heatstroke patients in 16 Japanese cities (corresponding to around a 10,000,000 population size). In the testing dataset, mean absolute percentage error of generalized linear models with wet bulb globe temperature as the only predictor and the optimal models, respectively, are 43.0% and 14.8% for spikes in the number of all heatstroke cases, and 37.7% and 10.6% for spikes in the number of heatstrokes of hospital admission and death cases. The optimal models predict the spikes in the number of heatstrokes well by machine learning methods including non-linear multivariable predictors and/or under-sampling and bagging. Here, we develop prediction models whose predictive performances are high enough to be implemented in public health settings.

Ogata Soshiro, Takegami Misa, Ozaki Taira, Nakashima Takahiro, Onozuka Daisuke, Murata Shunsuke, Nakaoku Yuriko, Suzuki Koyu, Hagihara Akihito, Noguchi Teruo, Iihara Koji, Kitazume Keiichi, Morioka Tohru, Yamazaki Shin, Yoshida Takahiro, Yamagata Yoshiki, Nishimura Kunihiro


Public Health Public Health

Diverse experts' perspectives on ethical issues of using machine learning to predict HIV/AIDS risk in sub-Saharan Africa: a modified Delphi study.

In BMJ open

OBJECTIVE : To better understand diverse experts' views about the ethical implications of ongoing research funded by the National Institutes of Health that uses machine learning to predict HIV/AIDS risk in sub-Saharan Africa (SSA) based on publicly available Demographic and Health Surveys data.

DESIGN : Three rounds of semi-structured surveys in an online expert panel using a modified Delphi approach.

PARTICIPANTS : Experts in informatics, African public health and HIV/AIDS and bioethics were invited to participate.

MEASURES : Perceived importance of or agreement about relevance of ethical issues on 5-point unipolar Likert scales. Qualitative data analysis identified emergent themes related to ethical issues and development of an ethical framework and recommendations for open-ended questions.

RESULTS : Of the 35 invited experts, 22 participated in the online expert panel (63%). Emergent themes were the inclusion of African researchers in all aspects of study design, analysis and dissemination to identify and address local contextual issues, as well as engagement of communities. Experts focused on engagement with health and science professionals to address risks, benefits and communication of findings. Respondents prioritised the mitigation of stigma to research participants but recognised trade-offs between privacy and the need to disseminate findings to realise public health benefits. Strategies for responsible communication of results were suggested, including careful word choice in presentation of results and limited dissemination to need-to-know stakeholders such as public health planners.

CONCLUSION : Experts identified ethical issues specific to the African context and to research on sensitive, publicly available data and strategies for addressing these issues. These findings can be used to inform an ethical implementation framework with research stage-specific recommendations on how to use publicly available data for machine learning-based predictive analytics to predict HIV/AIDS risk in SSA.

Nichol Ariadne A, Bendavid Eran, Mutenherwa Farirai, Patel Chirag, Cho Mildred K


ethics (see medical ethics), health informatics, medical ethics, public health

General General

Use of the Montreal Cognitive Assessment Thai Version to Discriminate Amnestic Mild Cognitive Impairment from Alzheimer's Disease and Healthy Controls: Machine Learning Results.

In Dementia and geriatric cognitive disorders

BACKGROUND : The Montreal Cognitive Assessment (MoCA) is an effective and applicable screening instrument to confirm the diagnosis of amnestic mild cognitive impairment (aMCI) from patients with Alzheimer's disease (AD) and healthy controls (HCs).

OBJECTIVES : This study aimed to determine the reliability and validity of the following: (a) Thai translation of the MoCA (MoCA-Thai) and (b) delineate the key features of aMCI based on the MoCA subdomains.

METHODS : This study included 60 HCs, 61 aMCI patients, and 60 AD patients. The MoCA-Thai shows adequate psychometric properties including internal consistency, concurrent validity, test-retest validity, and inter-rater reliability.

RESULTS : The MoCA-Thai may be employed as a diagnostic criterion to make the diagnosis of aMCI, whereby aMCI patients are discriminated from HC with an area under the receiver-operating characteristic (AUC-ROC) curve of 0.813 and from AD patients with an AUC-ROC curve of 0.938. The best cutoff scores of the MoCA-Thai to discriminate aMCI from HC is ≤24 and from AD > 16. Neural network analysis showed that (a) aberrations in recall was the most important feature of aMCI versus HC with impairments in language and orientation being the second and third most important features and (b) aberrations in visuospatial skills and executive functions were the most important features of AD versus aMCI and that impairments in recall, language, and orientation but not attention, concentration, and working memory, further discriminated AD from aMCI.

CONCLUSIONS : The MoCA-Thai is an appropriate cognitive assessment tool to be used in the Thai population for the diagnosis of aMCI and AD.

Hemrungrojn Solaphat, Tangwongchai Sookjaroen, Charoenboon Thammanard, Panasawat Muthita, Supasitthumrong Thitiporn, Chaipresertsud Pisit, Maleevach Pacharaporn, Likitjaroen Yuttachai, Phanthumchinda Kammant, Maes Michael


Alzheimer’s disease, Diagnosis, Mild cognitive impairment, Montreal Cognitive Assessment

Ophthalmology Ophthalmology

Automatic arteriosclerotic retinopathy grading using four-channel with image merging.

In Computer methods and programs in biomedicine

BACKGROUND AND OBJECTIVE : Arteriosclerosis can reflect the severity of hypertension, which is one of the main diseases threatening human life safety. But Arteriosclerosis retinopathy detection involves costly and time-consuming manual assessment. To meet the urgent needs of automation, this paper developed a novel arteriosclerosis retinopathy grading method based on convolutional neural network.

METHODS : Firstly, we propose a good scheme for extracting features facing the fundus blood vessel background using image merging for contour enhancement. In this step, the original image is dealt with adaptive threshold processing to generate the new contour channel, which merge with the original three-channel image. Then, we employ the pre-trained convolutional neural network with transfer learning to speed up training and contour image channel parameter with Kaiming initialization. Moreover, ArcLoss is applied to increase inter-class differences and intra-class similarity aiming to the high similarity of images of different classes in the dataset.

RESULTS : The accuracy of arteriosclerosis retinopathy grading achieved by the proposed method is up to 65.354%, which is nearly 4% higher than those of the exiting methods. The Kappa of our method is 0.508 in arteriosclerosis retinopathy grading.

CONCLUSIONS : An experimental study on multiple metrics demonstrates the superiority of our method, which will be a useful to the toolbox for arteriosclerosis retinopathy grading.

Gao Shuo, Gao Li, Quan Xiongwen, Zhang Han, Bai Hang, Kang Chuanze


ArcLossdeep, Arteriosclerotic retinopathy grading, Contour channel, Image merge, convolutional neural network

General General

Targeting neoantigens for cancer immunotherapy.

In Biomarker research

Neoantigens, a type of tumor-specific antigens derived from non-synonymous mutations, have recently been characterized as attractive targets for cancer immunotherapy. Owing to the development of next-generation sequencing and utilization of machine-learning algorithms, it has become feasible to computationally predict neoantigens by depicting genetic alterations, aberrant post-transcriptional mRNA processing and abnormal mRNA translation events within tumor tissues. Consequently, neoantigen-based therapies such as cancer vaccines have been widely tested in clinical trials and have demonstrated promising safety and efficacy, opening a new era for cancer immunotherapy. We systematically summarize recent advances in the identification of both personalized and public neoantigens, neoantigen formulations and neoantigen-based clinical trials in this review. Moreover, we discuss future techniques and strategies for neoantigen-based cancer treatment either as a monotherapy or as a combination therapy with radiotherapy, chemotherapy or immune checkpoint inhibitors.

Zhao Xuan, Pan Xiaoxin, Wang Yi, Zhang Yi


Cancer immunotherapy, Neoantigen vaccine, Precision medicine

General General

Psychological Stress and Perceived School Success Among Parents of Children with Developmental Disabilities During the COVID-19 Pandemic.

In Journal of autism and developmental disorders ; h5-index 76.0

This study mainly explored psychological stress caused by the COVID-19 among parents in developmental disabilities and how it was related to parents' views of school success in mainland China. The Psychological Stress Questionnaire and Views of Social and Academic Success were administered to 1919 parents of children with developmental disabilities. Results showed that parent characteristics including gender, age, educational level, family income and job nature and children characteristics (i.e., disability types) were related to psychological stress caused by the COVID-19, and that psychological stress caused by the COVID-19 significantly negatively predicted parents' views of school success. The contributions, limitations, and implications of the present research are discussed.

Cheng Sanyin, Yang Yuqin, Deng Meng


Developmental disabilities, Psychological stress, Views of school success