Learning and recommending treatments using electronic medical records

Hoang, Khanh Hung; Ho, Tu Bao

doi:10.1016/j.knosys.2019.05.031

Cited by 20 publications

(6 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The integration of Big Data into healthcare has catalyzed a transformative shift towards personalized or precision medicine, fundamentally customizing medical care by accounting for individual genetic, environmental, and lifestyle factors (Hoang & Ho, 2019). Central to this evolution, Big Data analytics processes and analyzes vast datasets from genomic sequencing, electronic health records (EHRs), and biometric devices, identifying specific disease biomarkers and susceptibilities (Chen, Guo, Sun, & Lu, 2019).…”

Section: Improving Patient Outcomes With Big Datamentioning

confidence: 99%

Untitled

2024

JALHE

View full text Add to dashboard Cite

The integration of Big Data analytics into the healthcare sector has initiated a period of unparalleled transformation, significantly influencing patient care, research, and healthcare administration. This detailed synthesis, derived from a systematic literature review of 31 articles published between 2013 and 2023, examines the extensive impact of Big Data within the healthcare industry. It addresses how Big Data enhances personalized medicine and optimizes treatment protocols, promotes the use of predictive analytics for the early detection and management of diseases, and bolsters patient safety through the implementation of real-time monitoring and decision support systems. The review also confronts the challenges and limitations associated with Big Data's application, including concerns over data privacy and security, ethical dilemmas, interoperability issues, and the necessity for both skilled personnel and advanced technological infrastructure. Furthermore, this overview ventures into prospects and technological advancements, emphasizing the pivotal role of emerging technologies such as artificial intelligence (AI), machine learning, and blockchain in heralding a new era of healthcare. It highlights Big Data's instrumental role in supporting public health measures and preparing for pandemics, alongside forecasting the ongoing influence of Big Data in fostering innovative healthcare paradigms. This analysis advocates for a concerted effort among healthcare professionals, policymakers, and technology experts to fully leverage Big Data's capabilities in healthcare, aiming to enhance health outcomes, increase operational efficiencies, and ensure the long-term viability of healthcare systems.

show abstract

Section: Improving Patient Outcomes With Big Datamentioning

confidence: 99%

Untitled

2024

JALHE

View full text Add to dashboard Cite

show abstract

“…So EMRs have an emphasis on justifying the medical records and billing details of the patients. 25 So, using the SCT process, we can treat multiple patients at a time SCT is a research-based method. The SCT process can be done either with a research based direct method for evaluation treatment or standard of care treatment.…”

Section: Allogenic Transplantationmentioning

confidence: 99%

Leveraging electronic health records and stem cell transplants: a review

Teja,

Gundimeda,

Kolipakula

et al. 2023

Int J Sci Rep

View full text Add to dashboard Cite

Biomedical informatics is a burgeoning multidisciplinary field that seeks to optimize the utilization of biomedical data, information, and knowledge for the purposes of scientific research, inquiry, problem-solving, and decision-making, all with the overarching objective of enhancing human health and well-being. Information, informatics, and its applications are used in organ transplantations, such as stem cell transplantation programs, and in programs that directly change disorders. So, many hospitals can maintain EMRs (Electronic medical records), but few are supporting C programs. So, using informatics, we introduced software and programs to run these SCT applications in a very short period of time without any effect.

show abstract

“…In this regard, several studies have argued the case for implementing systems that are able to provide motivated suggestions [15] as well as encode clinical practice guidelines (i.e. emulate the process followed by experts to take decisions) [16]. Diagnosis prediction and treatment learning and recommendation are long-term lines of research, including challenges in the combination of static and longitudinal data features and the added value of the application of machine learning methods [17].…”

Section: Related Workmentioning

confidence: 99%

HaemoKBS: A knowledge-based system for real-time, continuous categorisation of adverse reactions in blood recipients

Ramoa

Condeço²,

Fdez-Riverola

et al. 2021

Neurocomputing

View full text Add to dashboard Cite

This work introduces HaemoKBS, a novel Haemovigilance decision support system for adverse reactions in blood recipients. Machine learning inference and rule-based reasoning were applied to build the underlying decision support models, namely to automatically extract evidence from different types of data included in hospital notifications and incorporate a priori expert knowledge. The ultimate aim is to dynamically learn and improve the reasoning abilities of the system and thus, be able to provide educated recommendations to hospital notifiers along with understandable explanations on the acquired knowledge. Experiments over the records of the Portuguese National Haemovigilance System from the last 10 years demonstrate the practical usefulness of HaemoKBS, which will contribute to a better depiction of the adverse reactions and to flag any incomplete notification enforcing data quality.

show abstract

Learning and recommending treatments using electronic medical records

Cited by 20 publications

References 15 publications

Untitled

Untitled

Leveraging electronic health records and stem cell transplants: a review

HaemoKBS: A knowledge-based system for real-time, continuous categorisation of adverse reactions in blood recipients

Contact Info

Product

Resources

About