Big data in healthcare: Prospects, challenges and resolutions

Olaronke, Iroju; Oluwaseun, Ojerinde

doi:10.1109/ftc.2016.7821747

Cited by 82 publications

(62 citation statements)

References 6 publications

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“…It is likely that AI application software will need to meet rigorous testing that is mandated for new submissions for regulatory approval, including quality control and risk assessment. Because cloud computing and virtualization are being used increasingly to process medical data, health care information technology is gradually becoming part of the “big data” revolution . This offers a fertile environment for incorporating state‐of‐the‐art AI systems that often are distributed.…”

Section: Challenges and Future Directionsmentioning

confidence: 99%

“…Because cloud computing and virtualization are being used increasingly to process medical data, health care information technology is gradually becoming part of the "big data" revolution. 214 This offers a fertile environment for incorporating state-of-the-art AI systems that often are distributed. Nevertheless, it raises data security and privacy concerns, because maintaining Health Insurance Portability and Accountability Act compliance is essential.…”

Section: Challenges and Future Directionsmentioning

confidence: 99%

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Artificial intelligence in cancer imaging: Clinical challenges and applications

Hosny

Schabath

et al. 2019

CA A Cancer J Clinicians

1,143

820

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Judgement, as one of the core tenets of medicine, relies upon the integration of multilayered data with nuanced decision making. Cancer offers a unique context for medical decisions given not only its variegated forms with evolution of disease but also the need to take into account the individual condition of patients, their ability to receive treatment, and their responses to treatment. Challenges remain in the accurate detection, characterization, and monitoring of cancers despite improved technologies. Radiographic assessment of disease most commonly relies upon visual evaluations, the interpretations of which may be augmented by advanced computational analyses. In particular, artificial intelligence (AI) promises to make great strides in the qualitative interpretation of cancer imaging by expert clinicians, including volumetric delineation of tumors over time, extrapolation of the tumor genotype and biological course from its radiographic phenotype, prediction of clinical outcome, and assessment of the impact of disease and treatment on adjacent organs. AI may automate processes in the initial interpretation of images and shift the clinical workflow of radiographic detection, management decisions on whether or not to administer an intervention, and subsequent observation to a yet to be envisioned paradigm. Here, the authors review the current state of AI as applied to medical imaging of cancer and describe advances in 4 tumor types (lung, brain, breast, and prostate) to illustrate how common clinical problems are being addressed. Although most studies evaluating AI applications in oncology to date have not been vigorously validated for reproducibility and generalizability, the results do highlight increasingly concerted efforts in pushing AI technology to clinical use and to impact future directions in cancer care.

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Section: Challenges and Future Directionsmentioning

confidence: 99%

Section: Challenges and Future Directionsmentioning

confidence: 99%

Artificial intelligence in cancer imaging: Clinical challenges and applications

Hosny

Schabath

et al. 2019

CA A Cancer J Clinicians

1,143

820

View full text Add to dashboard Cite

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“…Tools for analyzing BD are generally referred to as big data analytics (BDA). Typical BDAs include [7]: (1) Google Big Query, (2) Map Reduce, (3) Jaql, (4) Hadoop. The main objective of big data analytics is to help healthcare organizations in making better and more informed decisions.…”

Section: )mentioning

confidence: 99%

Big Data in Healthcare

Sadiku¹

2019

IJRASET

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Big data (BD) is essentially about gathering information from disparate sources and analyzing it to reveal trends that can directly improve patients' well-being. Healthcare is one of the most promising areas where big data can be applied to make a change. BD is a revolutionary, powerful tool in the healthcare industry; it is now becoming vital in current patient-centric care. This paper briefly provides an introduction to big data analysis in the healthcare sector. Keywords: healthcare, big data, big data analytics I. INTRODUCTION Health plays a crucial role in people's lives. The healthcare industry comprises of nursing homes, telemedicine, charity organizations, and medical equipment. The global healthcare industry is striving to lower the costs while improving the quality of care provided. With the ever-increasing cost for healthcare services and ever-increasing health insurance premiums, the healthcare industry is in need of proactive healthcare management and wellness. As the industry moves from a volume-based to a value-based model, data will play a pivotal role in the transition [1]. Today, data is generated daily by mobile phones, sensors, patients, hospitals, researchers, organizations, pharmacies, medical centers, medical records, commerce, and organizations. Data has always been king, but handling and analyzing huge amount of data is a challenging task. In today's digital society, it is necessary that this data should be digitized. This huge, heterogeneous, and digitized data is known as big data (BD). Big data (BD) has opened up enormous opportunities for a wide range of industries, include the healthcare sector. Its management solutions will provide insightful information, which can help healthcare organizations to made right decisions at right time. II. IMPORTANCE OF BD IN HEALTHCARE "Big Data" is a big all-too-common buzzword these days. The concept refers to massive amount of data generated through digitization of all sorts of information, including health records. It has rapidly made its way into a wide range of industries and it has changed the way we manage and analyze data. It is a major challenge for industries such as defense, transportation, agriculture, and banking. For healthcare industry, it is even more formidable due to the highly sensitive and highly dynamic nature of the data. In healthcare, big data my include data from, but not limited to, electronic health records, mobile applications, genomics, clinical reports, doctor's notes, wearable body sensors, hospital admission notes, pharmacies, insurance companies, medical imaging, laboratories, genomics, ehealth, mhealth, and social media. These include structured (such as from electronic health records (EHRs)), semi-structured (physician-to-patient and patient-to-patient communication through email, social media, and web), and unstructured (such as clinical notes and claims) data [2]. The era of big data has opened up new opportunities in personalized medicine, preventive care, chronic disease management and in telemonitoring and mana...

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“…Raghupathi et al exposed in [ 14 ] the architectural framework and challenges of big data healthcare analytics. In another study (see [ 15 ]), the importance of security and privacy issues is demonstrated in implementing successfully big data healthcare systems. Belle et al discuss in [ 16 ] the role of big data in improving the quality of care delivery by aggregating and processing the large volume of data generated by healthcare systems.…”

Section: Big Data-based Healthcare Systemsmentioning

confidence: 99%

Big Data Management for Healthcare Systems: Architecture, Requirements, and Implementation

Aboudi

Benhlima

2018

Advances in Bioinformatics

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The growing amount of data in healthcare industry has made inevitable the adoption of big data techniques in order to improve the quality of healthcare delivery. Despite the integration of big data processing approaches and platforms in existing data management architectures for healthcare systems, these architectures face difficulties in preventing emergency cases. The main contribution of this paper is proposing an extensible big data architecture based on both stream computing and batch computing in order to enhance further the reliability of healthcare systems by generating real-time alerts and making accurate predictions on patient health condition. Based on the proposed architecture, a prototype implementation has been built for healthcare systems in order to generate real-time alerts. The suggested prototype is based on spark and MongoDB tools.

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Big data in healthcare: Prospects, challenges and resolutions

Cited by 82 publications

References 6 publications

Artificial intelligence in cancer imaging: Clinical challenges and applications

Artificial intelligence in cancer imaging: Clinical challenges and applications

Big Data in Healthcare

Big Data Management for Healthcare Systems: Architecture, Requirements, and Implementation

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