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Public Health Public Health

Can we design the next generation of digital health communication programs by leveraging the power of artificial intelligence to segment target audiences, bolster impact and deliver differentiated services? A machine learning analysis of survey data from rural India.

In BMJ open

OBJECTIVES : Direct to beneficiary (D2B) mobile health communication programmes have been used to provide reproductive, maternal, neonatal and child health information to women and their families in a number of countries globally. Programmes to date have provided the same content, at the same frequency, using the same channel to large beneficiary populations. This manuscript presents a proof of concept approach that uses machine learning to segment populations of women with access to phones and their husbands into distinct clusters to support differential digital programme design and delivery.

SETTING : Data used in this study were drawn from cross-sectional survey conducted in four districts of Madhya Pradesh, India.

PARTICIPANTS : Study participant included pregnant women with access to a phone (n=5095) and their husbands (n=3842) RESULTS: We used an iterative process involving K-Means clustering and Lasso regression to segment couples into three distinct clusters. Cluster 1 (n=1408) tended to be poorer, less educated men and women, with low levels of digital access and skills. Cluster 2 (n=666) had a mid-level of digital access and skills among men but not women. Cluster 3 (n=1410) had high digital access and skill among men and moderate access and skills among women. Exposure to the D2B programme 'Kilkari' showed the greatest difference in Cluster 2, including an 8% difference in use of reversible modern contraceptives, 7% in child immunisation at 10 weeks, 3% in child immunisation at 9 months and 4% in the timeliness of immunisation at 10 weeks and 9 months.

CONCLUSIONS : Findings suggest that segmenting populations into distinct clusters for differentiated programme design and delivery may serve to improve reach and impact.

TRIAL REGISTRATION NUMBER : NCT03576157.

Bashingwa Jean Juste Harrisson, Mohan Diwakar, Chamberlain Sara, Scott Kerry, Ummer Osama, Godfrey Anna, Mulder Nicola, Moodley Deshendran, LeFevre Amnesty Elizabeth

2023-Mar-17

community child health, information technology, public health

General General

Machine learning approaches in diagnosing tuberculosis through biomarkers - A systematic review.

In Progress in biophysics and molecular biology

Biomarker-based tests may facilitate Tuberculosis (TB) diagnosis, accelerate treatment initiation, and thus improve outcomes. This review synthesizes the literature on biomarker-based detection for TB diagnosis using machine learning. The systematic review approach follows the PRISMA guideline. Articles were sought using relevant keywords from Web of Science, PubMed, and Scopus, resulting in 19 eligible studies after a meticulous screening. All the studies were found to have focused on the supervised learning approach, with Support Vector Machine (SVM) and Random Forest emerging as the top two algorithms, with the highest accuracy, sensitivity and specificity reported to be 97.0%, 99.2%, and 98.0%, respectively. Further, protein-based biomarkers were widely explored, followed by gene-based such as RNA sequence and, Spoligotypes. Publicly available datasets were observed to be popularly used by the studies reviewed whilst studies targeting specific cohorts such as HIV patients or children gathering their own data from healthcare facilities, leading to smaller datasets. Of these, most studies used the leave one out cross validation technique to mitigate overfitting. The review shows that machine learning is increasingly assessed in research to improve TB diagnosis through biomarkers, as promising results were shown in terms of model's detection performance. This provides insights on the possible application of machine learning approaches to diagnose TB using biomarkers as opposed to the traditional methods that can be time consuming. Low-middle income settings, where access to basic biomarkers could be provided as compared to sputum-based tests that are not always available, could be a major application of such models.

Balakrishnan Vimala, Kehrabi Yousra, Ramanathan Ghayathri, Paul Scott Arjay, Tiong Chiong Kian

2023-Mar-15

Biomarker, Diagnostic, Machine learning, Systematic review, Tuberculosis

General General

Organophosphate esters in soils of Beijing urban parks: Occurrence, potential sources, and probabilistic health risks.

In The Science of the total environment

Organophosphate esters (OPEs) are an emerging contaminant widely distributed in the soil. OPEs have drawn increasing attention for their biological toxicity and possible threat to human health. This research investigated the pollution characteristics of two typical OPEs, organophosphate triesters (tri-OPEs) and organophosphate diesters (di-OPEs), in soils of 104 urban parks in Beijing. The median concentrations of Σ11tri-OPEs and Σ8di-OPEs were 157 and 17.9 ng/g dw, respectively. Tris(2-chloroisopropyl) phosphate and bis(2-ethylhexyl) phosphate were the dominant tri-OPE and di-OPE, respectively. Consumer materials (such as building insulation and decorative materials), traffic emissions, and reclaimed water irrigation may be critical sources of tri-OPEs in urban park soils. Di-OPEs mainly originated from the degradation of parent compounds and industrial applications. Machine learning models were employed to determine the influencing factors of OPEs and predict changes in their concentrations. The predicted OPEs concentrations in Beijing urban park soils in 2025 and 2030 are three times and five times those in 2018, respectively. According to probabilistic health risk assessment, non-carcinogenic and carcinogenic risks of OPEs can be negligible for children and adults. Our results could inform measures for preventing and controlling OPEs pollution in urban park soils.

Tian Y X, Wang Y, Chen H Y, Ma J, Liu Q Y, Qu Y J, Sun H W, Wu L N, Li X L

2023-Mar-15

Concentration prediction, Influencing factors, OPEs, Probabilistic health risk, Source identification

Public Health Public Health

Assessment of the implementation context in preparation for a clinical study of machine-learning algorithms to automate the classification of digital cervical images for cervical cancer screening in resource-constrained settings.

In Frontiers in health services

INTRODUCTION : We assessed the implementation context and image quality in preparation for a clinical study evaluating the effectiveness of automated visual assessment devices within cervical cancer screening of women living without and with HIV.

METHODS : We developed a semi-structured questionnaire based on three Consolidated Framework for Implementation Research (CFIR) domains; intervention characteristics, inner setting, and process, in Cape Town, South Africa. Between December 1, 2020, and August 6, 2021, we evaluated two devices: MobileODT handheld colposcope; and a commercially-available cell phone (Samsung A21ST). Colposcopists visually inspected cervical images for technical adequacy. Descriptive analyses were tabulated for quantitative variables, and narrative responses were summarized in the text.

RESULTS : Two colposcopists described the devices as easy to operate, without data loss. The clinical workspace and gynecological workflow were modified to incorporate devices and manage images. Providers believed either device would likely perform better than cytology under most circumstances unless the squamocolumnar junction (SCJ) were not visible, in which case cytology was expected to be better. Image quality (N = 75) from the MobileODT device and cell phone was comparable in terms of achieving good focus (81% vs. 84%), obtaining visibility of the squamous columnar junction (88% vs. 97%), avoiding occlusion (79% vs. 87%), and detection of lesion and range of lesion includes the upper limit (63% vs. 53%) but differed in taking photographs free of glare (100% vs. 24%).

CONCLUSION : Novel application of the CFIR early in the conduct of the clinical study, including assessment of image quality, highlight real-world factors about intervention characteristics, inner clinical setting, and workflow process that may affect both the clinical study findings and ultimate pace of translating to clinical practice. The application and augmentation of the CFIR in this study context highlighted adaptations needed for the framework to better measure factors relevant to implementing digital interventions.

Castor Delivette, Saidu Rakiya, Boa Rosalind, Mbatani Nomonde, Mutsvangwa Tinashe E M, Moodley Jennifer, Denny Lynette, Kuhn Louise

2022

Automated Visual Evaluation, Automated Visual Evaluation of the cervix, CFIR, CFIR framework, cervical cancer, digital cervical-cancer screening implementation assessment cervical cancer, implementation science

General General

Characteristics Prediction of Hydrothermal Biochar Using Data Enhanced Interpretable Machine Learning.

In Bioresource technology

Hydrothermal biochar is a promising sustainable soil remediation agent for plant growth. Demands for biochar properties differ due to the diversity of soil environment. In order to achieve accurate biochar properties prediction and overcome the interpretability bottleneck of machine learning models, this study established a series of data-enhanced machine learning models and conducted relevant sensitivity analysis. Compared with traditional support vector machine, artificial neural network, and random forest models, the accuracy after data enhancement increased in average from 5.8% to 15.8%, where the optimal random forest model showed the average of accuracy was 94.89%. According to sensitivity analysis results, the essential factors influencing the predicting results of the models were reaction temperature, reaction pressure, and specific element of biomass feedstock. As a result, data-enhanced interpretable machine learning proved promising for the characteristics prediction of hydrothermal biochar.

Chen Chao, Wang Zhi, Ge Yadong, Liang Rui, Hou Donghao, Tao Junyu, Yan Beibei, Zheng Wandong, Velichkova Rositsa, Chen Guanyi

2023-Mar-15

Correlation Analysis, Feature Analysis, Hydrothermal Carbonization, Random Forest

General General

Optimization of depth of filler media in horizontal flow constructed wetlands for maximizing removal rate coefficients of targeted pollutant(s).

In Bioresource technology

Varying the depth of HFCW media causes differences in the redox status within the system, and hence the community structure and diversity of bacteria, affecting removal rates of different pollutants. The key functional microorganisms of CWs that remove contaminants belong to the phyla Proteobacteria, Bacteroidetes, Actinobacteria, and Firmicutes. Secondary data of 111 HFCWs (1232 datasets) were analyzed to deduce the relationship between volumetric removal rate coefficients (KBOD, KTN, KTKN, and KTP) and depth. Equations of depth were derived in terms of rate coefficients using machine learning approach (MLR and SVR) (R2=0.85,0.87 respectively). These equations were then used to find the optimum depth for pollutant(s) removal using Grey wolf optimization (GWO). The computed optimum depths were 1.48, 1.71, 1.91, 2.09, and 2.14 m for the removal of BOD, TKN, TN, TP, and combined nutrients, respectively, which were validated through primary data. This study would be helpful for optimal design of HFCWs.

Singh Saurabh, Soti Abhishek, Mohan Kulshreshtha Niha, Kumar Nikhil, Brighu Urmila, Bhushan Gupta Akhilendra, Bezbaruah Achintya N

2023-Mar-15

Grey Wolf Optimization (GWO), horizontal flow constructed wetlands (HFCWs), major functional microorganisms, optimum depth, volumetric removal rate coefficients