Enabling pregnant women and their physicians to make informed medication decisions using artificial intelligence

Davidson, Lena; Boland, Mary Regina

doi:10.1007/s10928-020-09685-1

Cited by 30 publications

(18 citation statements)

References 84 publications

(108 reference statements)

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“…This is important as the hydroxychloroquine clinical trials for COVID-19 specifically exclude pregnant women from enrolling in their studies. Informatics methods can also be designed, which use more sophisticated machine learning and artificial intelligence methods to study the effects of medication exposure during pregnancy on fetal and maternal outcomes [15].…”

Section: Pharmacovigilance and Repurposing Existing Data For Covid-19mentioning

confidence: 99%

Ideas for how informaticians can get involved with COVID-19 research

et al. 2020

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The coronavirus disease 2019 (COVID-19) pandemic has had a significant impact on population health and wellbeing. Biomedical informatics is central to COVID-19 research efforts and for the delivery of healthcare for COVID-19 patients. Critical to this effort is the participation of informaticians who typically work on other basic science or clinical problems. The goal of this editorial is to highlight some examples of COVID-19 research areas that could benefit from informatics expertise. Each research idea summarizes the COVID-19 application area, followed by an informatics methodology, approach, or technology that could make a contribution. It is our hope that this piece will motivate and make it easy for some informaticians to adopt COVID-19 research projects.

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Section: Pharmacovigilance and Repurposing Existing Data For Covid-19mentioning

confidence: 99%

Ideas for how informaticians can get involved with COVID-19 research

et al. 2020

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“…Many reviews included data collected from electronic medical records, hospital information systems, or any databank that used individual patient data to create predictive models or evaluate collective patterns [12,13,[16][17][18][19][20][21][24][25][26][27]30,[33][34][35]37,38,40,[42][43][44][45]. Additionally, four reviews included primary studies based on imaging datasets and databanks, assessing different parameters of accuracy [15,29,31,36].…”

Section: Data Sources and Purposes Of Included Studiesmentioning

confidence: 99%

“…Additionally, four reviews included primary studies based on imaging datasets and databanks, assessing different parameters of accuracy [15,29,31,36]. Other reviews focused on genetic databases [28,35], data from assisted reproductive technologies [30], or publicly available data [11,14,22,32]. Four studies lacked precision about the origin of the datasets used in their analysis or did not specifically use patient data in the investigation [23,37,39,41].…”

Section: Data Sources and Purposes Of Included Studiesmentioning

confidence: 99%

Impact of Big Data Analytics on People’s Health: Overview of Systematic Reviews and Recommendations for Future Studies

Nascimento¹,

Marcolino²,

Abdulazeem³

et al. 2021

J Med Internet Res

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Background Although the potential of big data analytics for health care is well recognized, evidence is lacking on its effects on public health. Objective The aim of this study was to assess the impact of the use of big data analytics on people’s health based on the health indicators and core priorities in the World Health Organization (WHO) General Programme of Work 2019/2023 and the European Programme of Work (EPW), approved and adopted by its Member States, in addition to SARS-CoV-2–related studies. Furthermore, we sought to identify the most relevant challenges and opportunities of these tools with respect to people’s health. Methods Six databases (MEDLINE, Embase, Cochrane Database of Systematic Reviews via Cochrane Library, Web of Science, Scopus, and Epistemonikos) were searched from the inception date to September 21, 2020. Systematic reviews assessing the effects of big data analytics on health indicators were included. Two authors independently performed screening, selection, data extraction, and quality assessment using the AMSTAR-2 (A Measurement Tool to Assess Systematic Reviews 2) checklist. Results The literature search initially yielded 185 records, 35 of which met the inclusion criteria, involving more than 5,000,000 patients. Most of the included studies used patient data collected from electronic health records, hospital information systems, private patient databases, and imaging datasets, and involved the use of big data analytics for noncommunicable diseases. “Probability of dying from any of cardiovascular, cancer, diabetes or chronic renal disease” and “suicide mortality rate” were the most commonly assessed health indicators and core priorities within the WHO General Programme of Work 2019/2023 and the EPW 2020/2025. Big data analytics have shown moderate to high accuracy for the diagnosis and prediction of complications of diabetes mellitus as well as for the diagnosis and classification of mental disorders; prediction of suicide attempts and behaviors; and the diagnosis, treatment, and prediction of important clinical outcomes of several chronic diseases. Confidence in the results was rated as “critically low” for 25 reviews, as “low” for 7 reviews, and as “moderate” for 3 reviews. The most frequently identified challenges were establishment of a well-designed and structured data source, and a secure, transparent, and standardized database for patient data. Conclusions Although the overall quality of included studies was limited, big data analytics has shown moderate to high accuracy for the diagnosis of certain diseases, improvement in managing chronic diseases, and support for prompt and real-time analyses of large sets of varied input data to diagnose and predict disease outcomes. Trial Registration International Prospective Register of Systematic Reviews (PROSPERO) CRD42020214048; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=214048

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“…New AI approaches are a must in future research to clarify the maternal and fetal effects of opioid exposure. An AI for other facets of pregnancy, maternal and fetal wellbeing, including lactation, will advise the necessary research into the impact of pharmacologic on breastfeeding (Davidson and Boland, 2020). however, learns mainly from the observation of data.…”

Section: Future Of Ai and Women Healthmentioning

confidence: 99%

Using AI Changes the Paradigm of Women's Participation in Politics

Efthymiou

Psomiadi²,

Diareme

et al. 2020

HAPScPBS

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The effect of AI on how people are viewed and handled in society is important and profound. However, a vicious cycle is maintained with AI's algorithms design and implementation. Among others, predictive models, machine learning and AI algorithms train and test themselves using datasets, as a result, they "learn" mainly based on the data input in a model. Nowadays and in this context, it seems that there is a growing scientific dialogue concerning bias in training AI (Falco, 2019; Lu, 2019; Straw, 2020) as well as whether datasets, on which decisions are made, only represent fractions of reality (Günther et al., 2017). The technology often captures and reproduces regulated and restrictive beliefs regarding gender and race, which are then repetitively strengthened: Gender relations be materialized by inventions and, through their enrolment and incorporation of machinery, masculinity, and femininity gain of turn their importance and character. When robots progress in certain cognitive functions, their comparatively weak abilities will definitely get better. This list incorporates the innovative approach to the dilemma, empathy, negotiation, and belief. Automation and AI will also replace many of today's workers at the same time creating new opportunities for specialized personnel-so that is why women need to get into this emerging sector and ensure that they can secure new jobs when their jobs are squeezed. In addition, AI may provide the ability to alter male and female epistemological assumptions. The narration of "hard" and "soft" intelligence, for instance, is often described as male and female. The rise and development of AI is also seen as pushing economic growth and strengthening political influence. In politics, UK politics still dominates the ambition of economic development by technical advancement. Jude Browne states (Clementine Collett & Sarah Dillon) that a national AI agency equivalent to Human Fertilization and Embryology (HFEA) has yet to be set up by the government of the UK that will fill the divide between national, experts and government, for example. Browne claims that it includes the dominance, primarily guided by the goals of economic wealth, of private interest over the public interest. There is a possibility that economic growth and political influence play an important part in influencing AI laws and policies at the cost of other motivations, which are more morally equal. Consequently, a dual-purpose must be incorporated into an equitable AI policy. Firstly, to ensure there is no rise in social and economic disparity due to the advancement of AI technology. Secondly, to call AI to cut this down. AI must first and foremost enable us to promote our democratic liberties, enhance social harmony, and enhance unity, rather than jeopardise our individual trajectories and networks of solidarity.

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Enabling pregnant women and their physicians to make informed medication decisions using artificial intelligence

Cited by 30 publications

References 84 publications

Ideas for how informaticians can get involved with COVID-19 research

Ideas for how informaticians can get involved with COVID-19 research

Impact of Big Data Analytics on People’s Health: Overview of Systematic Reviews and Recommendations for Future Studies

Using AI Changes the Paradigm of Women's Participation in Politics

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