scholarly journals Use of Big Data and Machine Learning Methods in the Monitoring and Evaluation of Digital Health Programs in India: An Exploratory Protocol (Preprint)

2018 ◽  
Author(s):  
Diwakar Mohan ◽  
Jean Juste Harrisson Bashingwa ◽  
Pierre Dane ◽  
Sara Chamberlain ◽  
Nicki Tiffin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Health Information ◽  
Program Effectiveness ◽  
Health Workers ◽  
Interactive Voice Response ◽  
Digital Health ◽  
Health Programs ◽  
Frontline Health Workers ◽  
Applications Of Machine Learning

BACKGROUND Digital health programs, which encompass the subsectors of health information technology, mobile health, electronic health, telehealth, and telemedicine, have the potential to generate “big data.” OBJECTIVE Our aim is to evaluate two digital health programs in India—the maternal mobile messaging service (Kilkari) and the mobile training resource for frontline health workers (Mobile Academy). We illustrate possible applications of machine learning for public health practitioners that can be applied to generate evidence on program effectiveness and improve implementation. Kilkari is an outbound service that delivers weekly gestational age–appropriate audio messages about pregnancy, childbirth, and childcare directly to families on their mobile phones, starting from the second trimester of pregnancy until the child is one year old. Mobile Academy is an Interactive Voice Response audio training course for accredited social health activists (ASHAs) in India. METHODS Study participants include pregnant and postpartum women (Kilkari) as well as frontline health workers (Mobile Academy) across 13 states in India. Data elements are drawn from system-generated databases used in the routine implementation of programs to provide users with health information. We explain the structure and elements of the extracted data and the proposed process for their linkage. We then outline the various steps to be undertaken to evaluate and select final algorithms for identifying gaps in data quality, poor user performance, predictors for call receipt, user listening levels, and linkages between early listening and continued engagement. RESULTS The project has obtained the necessary approvals for the use of data in accordance with global standards for handling personal data. The results are expected to be published in August/September 2019. CONCLUSIONS Rigorous evaluations of digital health programs are limited, and few have included applications of machine learning. By describing the steps to be undertaken in the application of machine learning approaches to the analysis of routine system-generated data, we aim to demystify the use of machine learning not only in evaluating digital health education programs but in improving their performance. Where articles on analysis offer an explanation of the final model selected, here we aim to emphasize the process, thereby illustrating to program implementors and evaluators with limited exposure to machine learning its relevance and potential use within the context of broader program implementation and evaluation. INTERNATIONAL REGISTERED REPOR DERR1-10.2196/11456

scholarly journals Use of Big Data and Machine Learning Methods in the Monitoring and Evaluation of Digital Health Programs in India: An Exploratory Protocol

10.2196/11456 ◽  
2019 ◽  
Vol 8 (5) ◽  
pp. e11456 ◽  
Author(s):  
Diwakar Mohan ◽  
Jean Juste Harrisson Bashingwa ◽  
Pierre Dane ◽  
Sara Chamberlain ◽  
Nicki Tiffin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Digital Health ◽  
Monitoring And Evaluation ◽  
Health Programs ◽  
Learning Methods ◽  
Machine Learning Methods

What is Machine Learning? A Primer for the Epidemiologist

2019 ◽  
Author(s):  
Qifang Bi ◽  
Katherine E Goodman ◽  
Joshua Kaminsky ◽  
Justin Lessler
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Computer Science ◽  
Research Methods ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Epidemiologic Research ◽  
Learning Techniques ◽  
Applications Of Machine Learning

Abstract Machine learning is a branch of computer science that has the potential to transform epidemiologic sciences. Amid a growing focus on “Big Data,” it offers epidemiologists new tools to tackle problems for which classical methods are not well-suited. In order to critically evaluate the value of integrating machine learning algorithms and existing methods, however, it is essential to address language and technical barriers between the two fields that can make it difficult for epidemiologists to read and assess machine learning studies. Here, we provide an overview of the concepts and terminology used in machine learning literature, which encompasses a diverse set of tools with goals ranging from prediction to classification to clustering. We provide a brief introduction to 5 common machine learning algorithms and 4 ensemble-based approaches. We then summarize epidemiologic applications of machine learning techniques in the published literature. We recommend approaches to incorporate machine learning in epidemiologic research and discuss opportunities and challenges for integrating machine learning and existing epidemiologic research methods.

Machine Learning Methods in Precision Medicine Targeting Epigenetic Diseases

2019 ◽  
Vol 24 (34) ◽  
pp. 3998-4006
Author(s):  
Shijie Fan ◽  
Yu Chen ◽  
Cheng Luo ◽  
Fanwang Meng
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Precision Medicine ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Learning Methods ◽  
Advantages And Disadvantages ◽  
Machine Learning Methods ◽  
Accelerated Studies ◽  
Applications Of Machine Learning

Background: On a tide of big data, machine learning is coming to its day. Referring to huge amounts of epigenetic data coming from biological experiments and clinic, machine learning can help in detecting epigenetic features in genome, finding correlations between phenotypes and modifications in histone or genes, accelerating the screen of lead compounds targeting epigenetics diseases and many other aspects around the study on epigenetics, which consequently realizes the hope of precision medicine. Methods: In this minireview, we will focus on reviewing the fundamentals and applications of machine learning methods which are regularly used in epigenetics filed and explain their features. Their advantages and disadvantages will also be discussed. Results: Machine learning algorithms have accelerated studies in precision medicine targeting epigenetics diseases. Conclusion: In order to make full use of machine learning algorithms, one should get familiar with the pros and cons of them, which will benefit from big data by choosing the most suitable method(s).

Observations on Health Information in Developing Countries

1994 ◽  
Vol 33 (03) ◽  
pp. 304-305 ◽  
Author(s):  
C. Hull
Keyword(s):  
Developing Countries ◽  
Information Systems ◽  
Health Information ◽  
Information Science ◽  
Health Workers ◽  
Appropriate Technology ◽  
Health Programs ◽  
Personal Experiences ◽  
Local Solutions ◽  
Definition Of

Abstract:Based on personal experiences, observations are offered on health information in developing countries, ways in which information systems can be strengthened, and opportunities for health-information science graduates. Although data collection consumes a significant portion of the health worker’s day, information systems are often a low priority in developing countries. Health-information systems can be strengthened by focusing on local solutions, by building skills in health workers, by utilizing appropriate technology, and by integrating information systems into health programs. Health-information science graduates can assist in improving systems in developing countries, but this will require a broad and flexible definition of health information science, which is much more than computing technology; it is supporting health workers to define, manage, and apply the information they need.

scholarly journals ‘Ai Theory of Justice’: Using Rawlsian Approaches to Legislate Better on Machine Learning in Government

Amicus Curiae ◽  
2020 ◽  
Vol 1 (3) ◽  
pp. 338-360
Author(s):  
Jamie Grace ◽  
Roxanne Bamford
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
John Rawls ◽  
Data Analytics ◽  
The Public ◽  
Theory Of Justice ◽  
Big Data Technologies ◽  
Applications Of Machine Learning

Policymaking is increasingly being informed by ‘big data’ technologies of analytics, machine learning and artificial intelligence (AI). John Rawls used particular principles of reasoning in his 1971 book, A Theory of Justice, which might help explore known problems of data bias, unfairness, accountability and privacy, in relation to applications of machine learning and AI in government. This paper will investigate how the current assortment of UK governmental policy and regulatory developments around AI in the public sector could be said to meet, or not meet, these Rawlsian principles, and what we might do better by incorporating them when we respond legislatively to this ongoing challenge. This paper uses a case study of data analytics and machine-learning regulation as the central means of this exploration of Rawlsian thinking in relation to the redevelopment of algorithmic governance.

scholarly journals Researching, Co-creating and Testing Innovations in Paper Health Information Systems (PHISICC) to Support Health Workers Decision-making: Protocol of a Multi-country, Transdisciplinary, Mixed-methods Research Programme in Three Sub-saharan Countries

2021 ◽  
Author(s):  
Xavier Bosch-Capblanch ◽  
David O’Donnell ◽  
L Kendall Krause ◽  
Christian Auer ◽  
Angela Oyo-Ita ◽  
...  
Keyword(s):  
Health Care ◽  
Decision Making ◽  
Information Systems ◽  
Health Information ◽  
Health Workers ◽  
Health Information Systems ◽  
Primary Health ◽  
Frontline Health Workers ◽  
Rural Settings ◽  
Sub Saharan

Abstract BackgroundHealth Information Systems are crucial to provide data for decision-making and data demands are constantly growing. However, the link between data and decisions is not always rational nor linear and the management of data ends up overloading frontline health workers, who may have to compromise the health care. Despite limited evidence, there is an increasing push for the digitalisation of Health Information Systems, which faces enormous challenges, particularly in remote, rural settings in low- and middle-income countries. Paper-based tools will continue to be used and this warrants efforts to make them more responsive to local needs. Paper Health Information Systems (PHISICC) is a transdisciplinary, multi-country research initiative to create and test innovative paper-based Health Information Systems in three Sub-Saharan African countries.MethodsThe PHISICC initiative is taking place in remote, rural settings, in Côte d’Ivoire, Mozambique and Nigeria, through partnership with Ministries of Health and research institutions. We began with research syntheses to acquire the most up to date knowledge on Health Information Systems. These were coupled with field work in the three countries to understand the current design, patterns and contexts of use, and health care worker perspectives. Frontline health workers, with designers and researchers, used co-creation methods to produce the new PHISICC tools. This suite of tools is being tested in the three countries. Throughout the project, we have engaged with a wide range of stakeholders and have kept the highest scientific standards to keep it relevant to health policy in each of the three countries.DiscussionWe have deployed a comprehensive research approach to ensure the robustness and future policy uptake of the finding. Beyond the resulting paper-based tool design innovations, our process itself was innovative. Rather than emphasizing the data management compliance aspects we focused instead on frontline health workers’ decision-making; by tackling the whole scope of health care areas in Primary Health Care rather than incremental improvement to existing tools, we developed an entirely new design approach and language for a suite of tools in Primary Health Care. The initiative is being tested in remote, rural areas where the most vulnerable live.

scholarly journals Researching, characterising, co-creating and testing innovations in Paper Health Information Systems (PHISICC) to support frontline health workers decision-making: protocol of a multi-country, transdisciplinary, mixed-methods research programme in three Sub-Saharan countries

2021 ◽  
Author(s):  
Xavier Bosch-Capblanch ◽  
David O'Donnell ◽  
L Kendall Krause ◽  
Christian Auer ◽  
Angela Oyo-Ita ◽  
...  
Keyword(s):  
Health Care ◽  
Decision Making ◽  
Information Systems ◽  
Health Information ◽  
Health Workers ◽  
Health Information Systems ◽  
Frontline Health Workers ◽  
Rural Settings ◽  
Wide Range ◽  
Sub Saharan

Abstract BackgroundHealth Information Systems (HIS) are crucial to provide data for decision-making and data demands are constantly growing. However, the link between data and decisions is not always rational nor linear and the management of data ends up overloading frontline health workers, who may have to compromise the health care. Despite limited evidence, there is an increasing push for HIS digitalisation, which faces enormous challenges, particularly in remote, rural settings in low- and middle-income countries. Paper-based tools will continue to be used and this warrants efforts to make them more responsive to local needs. Paper Health Information Systems (PHISICC) is a transdisciplinary, multi-country research initiative to create and test innovative paper-based HIS in three Sub-Saharan African countries.MethodsThe PHISICC initiative is taking place in remote, rural settings, in Côte d’Ivoire, Mozambique and Nigeria, through partnership with Ministries of Health and research institutions. We began with research syntheses to acquire the most up to date knowledge on HIS. These were coupled with field work in the three countries to understand the current design, patterns and contexts of use, and health care worker perspectives. Frontline health workers, with designers and researchers, used co-creation methods to produce the new PHISICC tools. This suite of tools is being tested in the three countries. Throughout the project we have engaged with a wide range of stakeholders and have kept the highest scientific standards to keep it relevant to health policy in each of the three countries.DiscussionWe have deployed a comprehensive research approach to ensure the robustness and future policy uptake of the finding. Beyond the resulting paper-based tool design innovations, our process itself was innovative. Rather than emphasizing the data management compliance aspects we focused instead on frontline health workers’ decision-making; by tackling the whole scope of health care areas in PHC rather than incremental improvement to existing tools, we developed an entirely new design approach and language for a suite of tools in Primary Health Care. The initiative is being tested in remote, rural areas where the most vulnerable live.

scholarly journals Applications of Machine Learning For Precision Agriculture and Smart Farming

2021 ◽  
Vol 34 (1) ◽  
Author(s):  
Sai Gurrapu ◽  
Nazmul Sikder ◽  
Pei Wang ◽  
Nitish Gorentala ◽  
Madison Williams ◽  
...  
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Supply Chain ◽  
Precision Agriculture ◽  
Farm Management ◽  
Time Data ◽  
Quantitative Metrics ◽  
Smart Farming ◽  
Applications Of Machine Learning ◽  
Financial Indices

Recent deglobalization movements have had a transformativeimpact and an increase in uncertainty on manyindustries. The advent of technology, Big Data, and MachineLearning (ML) further accelerated this disposition.Many quantitative metrics that measure the globaleconomy’s equilibrium have strong and interdependentrelationships with the agricultural supply chain and internationaltrade flows. Our research employs econometricsusing ML techniques to determine relationshipsbetween commonplace financial indices (such asthe DowJones), and the production, consumption, andpricing of global agricultural commodities. Producersand farmers can use this data to make their productionmore effective while precisely following global demand.In order to make production more efficient, producerscan implement smart farming and precision agriculturemethods using the processes proposed. It enablesthem to have a farm management system that providesreal-time data to observe, measure, and respondto variability in crops. Drones and robots can be usedfor precise crop maintenance that optimize yield returnswhile minimizing resource expenditure. We developML models which can be used in combinationwith the smart farm data to accurately predict the economicvariables relevant to the farm. To ensure the accuracyof the insights generated by the models, ML assuranceis deployed to evaluate algorithmic trust.

Sign in / Sign up

Export Citation Format

Share Document