Machine learning techniques for cognitive decision making

Chandiok, Ashish; Chaturvedi, D. K.

doi:10.1109/wci.2015.7495529

Cited by 13 publications

(1 citation statement)

References 14 publications

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“…As data storage capabilities have improved over the years in healthcare and more valuable data has been gathered, machine learning (ML) tools have been implemented to identify data patterns and predict future outcomes [29,30]. AI has already made its mark in medicine with its role in predicting phenotypes from genotypes, radiology and pathology diagnosis [31]. Particularly, advances in AI research became apparent with AI success in the field of radiology.…”

Section: Use Of Ai and Machine Learning In Medicinementioning

confidence: 99%

Literature Review on Epidemiological Modelling, Spatial Modelling and Artificial Intelligence for COVID-19

Saraee

Silva

2021

JAMMR

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Introduction: Following the outbreak of Coronavirus (COVID-19) in Wuhan, China in December 2019, the World Health Organisation (WHO) has declared this infectious disease as a pandemic. Unlike previous infectious outbreaks such as Severe Acute Respiratory Syndrome (SARS) and Middle Eastern Respiratory syndrome (MERS), the high transmission rate of COVID-19 has resulted in worldwide spread. The countries with the highest recorded incidence and mortality rates are the US and UK. Rationale/Objective: This review will compare studies that have used epidemiological models for disease forecasting and other models that have identified sociodemographic factors associated with COVID-19. We will evaluate several models, from basic equation-based mathematical models to more advanced machine-learning ones. Our expectation is that by identifying high impact models used by policy makers and discussing their limitations, we can identify possible areas for future research. Evidence Review: The bibliographic database google scholar was used to search keywords such as ‘COVID-19’, ‘epidemiological modelling’ and ‘machine learning’. We examined data review articles, research studies and government-released articles. Results: We identified that the current SEIR model used by the UK government lacked the spatial modelling to enable an accurate prediction of disease spread. We discussed that machine-learning systems which can identify high-risk groups can be used to establish the disparities in COVID-19 death in BAME groups. We found that most of the data hungry AI models used were limited by the lack of datasets available. Conclusion: In conclusion, advances in AI methods for infectious disease have overcome challenges presented in mathematical models. Whilst limitations do exist, when optimised, these highly advanced models have a great potential in public health surveillance, particularly infectious disease transmission.

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Section: Use Of Ai and Machine Learning In Medicinementioning

confidence: 99%