Intelligent health data analytics: A convergence of artificial intelligence and big data

Abidi, Samina Raza

doi:10.1177/0840470419846134

Cited by 40 publications

(31 citation statements)

References 22 publications

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“…Biomed ical engineering, [1][2][3][4][5][6][7][8][9] material science, [10][11][12][13][14][15][16][17][18] and artificial intel ligence (AI) [19][20][21][22][23][24] have been transforming the entire medical landscape: diagnostics, [25][26][27][28][29][30][31][32][33] drug development, 24,[34][35][36] drug delivery, [37][38][39][40][41][42][43][44][45][46] and data analytics. [47][48][49] The medical team has access to evergrowing information about their patients prior to and at the time of treat...…”

Section: Introductionmentioning

confidence: 99%

“…AI has emerged as a prominent tool for health data ana lytics. 20,48,[50][51][52] Among others, it has been used in diagnos tics, 47,[53][54][55] prognosis, [56][57][58] and identifying personalized care strategies. [59][60][61] Classical AI approaches consist of training an algorithm on population data to identify statistical pat terns that can be then used to diagnose and treat an indi vidual based on his or her specific demographics and a specific disease history.…”

Section: Introductionmentioning

confidence: 99%

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CURATE.AI: Optimizing Personalized Medicine with Artificial Intelligence

2020

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The clinical team attending to a patient upon a diagnosis is faced with two main questions: what treatment, and at what dose? Clinical trials’ results provide the basis for guidance and support for official protocols that clinicians use to base their decisions upon. However, individuals rarely demonstrate the reported response from relevant clinical trials, often the average from a group representing a population or subpopulation. The decision complexity increases with combination treatments where drugs administered together can interact with each other, which is often the case. Additionally, the individual’s response to the treatment varies over time with the changes in his or her condition, whether via the indication or physiology. In practice, the drug and the dose selection depend greatly on the medical protocol of the healthcare provider and the medical team’s experience. As such, the results are inherently varied and often suboptimal. Big data approaches have emerged as an excellent decision-making support tool, but their application is limited by multiple challenges, the main one being the availability of sufficiently big datasets with good quality, representative information. An alternative approach—phenotypic personalized medicine (PPM)—finds an appropriate drug combination (quadratic phenotypic optimization platform [QPOP]) and an appropriate dosing strategy over time (CURATE.AI) based on small data collected exclusively from the treated individual. PPM-based approaches have demonstrated superior results over the current standard of care. The side effects are limited while the desired output is maximized, which directly translates into improving the length and quality of individuals’ lives.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

CURATE.AI: Optimizing Personalized Medicine with Artificial Intelligence

2020

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“…The sensitivity and accuracy of a tracker heavily depend upon the data which may be physical, psychological, contact history, medical diagnosis, etc. [50,51]. The HHS tracker proved to be quite useful as it gathered data from multiple sources, provided a sense of limited resources in areas of susceptible COVID-19 red zones, and covered patients with myriad symptoms and signs and also followed their medical diagnosis and prognoses.…”

Section: Cloud-based System For Coronavirus Surveillance and Trackingmentioning

confidence: 99%

Using Computational Intelligence for Tracking COVID-19 Outbreak in Online Social Networks

Qazi

Ahmad

Raza

2020

Studies in Computational Intelligence

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The novel coronavirus disease (COVID-19) causes serious respiratory tract infections in humans, and worse leads to mortality in old-aged people or individuals with co-morbidities. Websites and online social platforms generate a gargantuan amount of data in myriad aspects namely-technology, global news, human healthcare, medicine, socio-political domain, etc., aiding to decipher significant knowledge using web mining. Since the outbreak, people from different geographical locations used hashtags about novel coronavirus. The FAMEC model, the Honghou Hybrid System (HHS), the COVID Tracking Project of Twitter are a few examples of computational intelligent online social trackers that have been devised to track the COVID-19 pandemic. Researchers have identified the significance of tweets to be consistent with the CDC and the WHO reports and discerned that mining of such personal tweets was effective to track, manage, and predict the mortality and morbidity rates, identify the geographic location of patients infected which would, in turn, lead to rapid treatment assessment, employment of telemedicine and sanitization of such regions. This chapter presents how computational intelligence along with online social networks can be used for tracking COVID-19 patients. IntroductionThe novel coronavirus, also called severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has caused a pandemic infectious disease known as coronavirus disease 2019 (COVID-19) which was initially observed in Wuhan city, China, in December 2019. This pandemic has led to a drastic change in the lifestyle around the world, with many people in quarantine, gigantic change in healthcare infrastructure,

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“…The outbreak of the coronavirus disease in China and many other countries has put huge pressure on the health care system [1]. One method of controlling the communicable diseases is the use of a surveillance system to track the exposed and infected individuals, as well as clinical outcomes [2][3][4][5][6]. However, traditional surveillance systems have limitations in terms of timeliness, spatial resolution, and scalability [7].…”

Section: Introductionmentioning

confidence: 99%

Cloud-Based System for Effective Surveillance and Control of COVID-19: Useful Experiences From Hubei, China

Gong¹,

Liu²,

Sun³

et al. 2020

J Med Internet Res

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Background Coronavirus disease (COVID-19) has been an unprecedented challenge to the global health care system. Tools that can improve the focus of surveillance efforts and clinical decision support are of paramount importance. Objective The aim of this study was to illustrate how new medical informatics technologies may enable effective control of the pandemic through the development and successful 72-hour deployment of the Honghu Hybrid System (HHS) for COVID-19 in the city of Honghu in Hubei, China. Methods The HHS was designed for the collection, integration, standardization, and analysis of COVID-19-related data from multiple sources, which includes a case reporting system, diagnostic labs, electronic medical records, and social media on mobile devices. Results HHS supports four main features: syndromic surveillance on mobile devices, policy-making decision support, clinical decision support and prioritization of resources, and follow-up of discharged patients. The syndromic surveillance component in HHS covered over 95% of the population of over 900,000 people and provided near real time evidence for the control of epidemic emergencies. The clinical decision support component in HHS was also provided to improve patient care and prioritize the limited medical resources. However, the statistical methods still require further evaluations to confirm clinical effectiveness and appropriateness of disposition assigned in this study, which warrants further investigation. Conclusions The facilitating factors and challenges are discussed to provide useful insights to other cities to build suitable solutions based on cloud technologies. The HHS for COVID-19 was shown to be feasible and effective in this real-world field study, and has the potential to be migrated.

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Intelligent health data analytics: A convergence of artificial intelligence and big data

Cited by 40 publications

References 22 publications

CURATE.AI: Optimizing Personalized Medicine with Artificial Intelligence

CURATE.AI: Optimizing Personalized Medicine with Artificial Intelligence

Using Computational Intelligence for Tracking COVID-19 Outbreak in Online Social Networks

Cloud-Based System for Effective Surveillance and Control of COVID-19: Useful Experiences From Hubei, China

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