scholarly journals Precision Neurosurgery: A Path Forward

2021 ◽  
Vol 11 (10) ◽  
pp. 1019
Author(s):  
Vianney Gilard ◽  
Stéphane Derrey ◽  
Stéphane Marret ◽  
Soumeya Bekri ◽  
Abdellah Tebani
Keyword(s):  
Precision Medicine ◽  
Data Analytics ◽  
Historical Perspective ◽  
Molecular Profiling ◽  
Data Driven ◽  
Medical Community ◽  
Technological Advances ◽  
Conceptual Shift ◽  
The Impact ◽  
Patient Centric

Since the inception of their profession, neurosurgeons have defined themselves as physicians with a surgical practice. Throughout time, neurosurgery has always taken advantage of technological advances to provide better and safer care for patients. In the ongoing precision medicine surge that drives patient-centric healthcare, neurosurgery strives to effectively embrace the era of data-driven medicine. Neuro-oncology best illustrates this convergence between surgery and precision medicine with the advent of molecular profiling, imaging and data analytics. This convenient convergence paves the way for new preventive, diagnostic, prognostic and targeted therapeutic perspectives. The prominent advances in healthcare and big data forcefully challenge the medical community to deeply rethink current and future medical practice. This work provides a historical perspective on neurosurgery. It also discusses the impact of the conceptual shift of precision medicine on neurosurgery through the lens of neuro-oncology.

The Intersection of Data Analytics and Data-Driven Innovation

2020 ◽  
pp. 317-343
Author(s):  
Marcus Tanque ◽  
Harry J. Foxwell
Keyword(s):  
Private Sector ◽  
Data Analytics ◽  
Large Scale ◽  
Data Driven ◽  
Public And Private ◽  
Managerial Practices ◽  
Project Completion ◽  
Technological Advances ◽  
Private Sector Organizations ◽  
Public And Private Sector

This chapter discusses businesses, key technology implementations, case studies, limitations, and trends. It also presents recommendations to improve data analysis, data-driven innovation, and big data project implementation. Small-to-large-scale project inefficiencies present unique challenges to both public and private sector institutions and their management. Data analytics management, data-driven innovation, and related project initiatives have grown in scope, scale, and frequency. This evolution is due to continued technological advances in analytical methods and computing technologies. Most public and private sector organizations do not deliver on project benefits and results. Many organizational and managerial practices emphasize these technical limitations. Specialized human and technical resources are essential for an organization's effective project completion. Functional and practical areas affecting analytics domain and ability requirements, stakeholder expectations, solution infrastructure choices, legal and ethical concerns will also be discussed in this chapter.

scholarly journals Long-term overall survival and prognostic score predicting survival: the IMPACT study in precision medicine

2019 ◽  
Vol 12 (1) ◽  
Author(s):  
Apostolia-Maria Tsimberidou ◽  
David S. Hong ◽  
Jennifer J. Wheler ◽  
Gerald S. Falchook ◽  
Filip Janku ◽  
...  
Keyword(s):  
Overall Survival ◽  
Clinical Trials ◽  
Precision Medicine ◽  
Phase 1 ◽  
Prognostic Score ◽  
Objective Response ◽  
Molecular Profiling ◽  
Risk Of Death ◽  
Link Type ◽  
The Impact

Abstract Background In 2007, we initiated IMPACT, a precision medicine program for patients referred for participation in early-phase clinical trials. We assessed the correlation of factors, including genomically matched therapy, with overall survival (OS). Patients and methods We performed molecular profiling (Clinical Laboratory Improvement Amendments) (genes ≤ 182) for patients with lethal/refractory advanced cancers referred to the Phase 1 Clinical Trials Program. Matched therapy, if available, was selected on the basis of genomics. Clinical trials varied over time and included investigational drugs against various targets (single agents or combinations). Patients were followed up for up to 10 years. Results Of 3487 patients who underwent tumor molecular profiling, 1307 (37.5%) had ≥ 1 alteration and received therapy (matched, 711; unmatched, 596; median age, 57 years; 39% men). Most common tumors were gastrointestinal, gynecologic, breast, melanoma, and lung. Objective response rates were: matched 16.4%, unmatched 5.4% (p < .0001); objective response plus stable disease ≥ 6 months rates were: matched 35.3% and unmatched 20.3%, (p < .001). Respective median progression-free survival: 4.0 and 2.8 months (p < .0001); OS, 9.3 and 7.3 months; 3-year, 15% versus 7%; 10-year, 6% vs. 1% (p < .0001). Independent factors associated with shorter OS (multivariate analysis) were performance status > 1 (p < .001), liver metastases (p < .001), lactate dehydrogenase levels > upper limit of normal (p < .001), PI3K/AKT/mTOR pathway alterations (p < .001), and non-matched therapy (p < .001). The five independent factors predicting shorter OS were used to design a prognostic score. Conclusions Matched targeted therapy was an independent factor predicting longer OS. A score to predict an individual patient’s risk of death is proposed. Trial registration ClinicalTrials.gov, NCT00851032, date of registration February 25, 2009.

scholarly journals The Impact of Big Data on Banking Operations

2021 ◽  
Author(s):  
Morshadul Hasan ◽  
Thi Le ◽  
Ariful Hoque
Keyword(s):  
Big Data ◽  
Banking Industry ◽  
Mobile Internet ◽  
Data Driven ◽  
Significant Implication ◽  
Technological Advances ◽  
Qualitative Research Methodology ◽  
Research Concept ◽  
The Impact ◽  
Banking Operations

Abstract In the current technological advances with the rise of the information revolution through mobile internet, cloud computing, big data, and the Internet of Things (IoT), the banking industry is receiving new opportunities and facing critical challenges. It motivates us to develop the proposed research concept to examine how data innovation influences banking operations. We employ the systematic qualitative research methodology based on the existing literature from Web of Science and SCOPUS database to accomplish our research objectives. The findings of this study include the positive implications of big data, challenges, and banking security as essential data-driven banking issues. This research will have a significant implication in the banking industry that big data operation is critical for data-driven banking decisions.

scholarly journals IAMM: A Maturity Model for Measuring Industrial Analytics Capabilities in Large-scale Manufacturing Facilities

2020 ◽  
Vol 7 (4) ◽  
Author(s):  
Peter O’Donovan ◽  
Ken Bruton ◽  
Dominic T.J. O’Sullivan
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Large Scale ◽  
Big Data Analytics ◽  
Data Driven ◽  
Maturity Model ◽  
Competitive Advantages ◽  
Engineering Statistics ◽  
Industrial Big Data ◽  
The Impact

Industrial big data analytics is an emerging multidisciplinary field, which incorporates aspects of engineering, statistics and computing, to produce data-driven insights that can enhance operational efficiencies, and produce knowledgebased competitive advantages. Developing industrial big data analytics capabilities is an ongoing process, whereby facilities continuously refine collaborations, workflows and processes to improve operational insights. Such activities should be guided by formal measurement methods, to strategically identify areas for improvement, demonstrate the impact of analytics initiatives, as well as deriving benchmarks across facilities and departments. This research presents a formal multi-dimensional maturity model for approximating industrial analytics capabilities, and demonstrates the model’s ability to assess the impact of an initiative undertaken in a real-world facility.

Cancer Biomarker Discovery for Precision Medicine: New Progress

2020 ◽  
Vol 26 (42) ◽  
pp. 7655-7671 ◽  
Author(s):  
Jinfeng Zou ◽  
Edwin Wang
Keyword(s):  
Precision Medicine ◽  
Cancer Patients ◽  
Clinical Application ◽  
Liquid Biopsy ◽  
Biomarker Discovery ◽  
Deep Understanding ◽  
Genetic Mutation ◽  
Data Driven ◽  
Novel Approaches ◽  
Functional Biomarkers

Background: Precision medicine puts forward customized healthcare for cancer patients. An important way to accomplish this task is to stratify patients into those who may respond to a treatment and those who may not. For this purpose, diagnostic and prognostic biomarkers have been pursued. Objective: This review focuses on novel approaches and concepts of exploring biomarker discovery under the circumstances that technologies are developed, and data are accumulated for precision medicine. Results: The traditional mechanism-driven functional biomarkers have the advantage of actionable insights, while data-driven computational biomarkers can fulfill more needs, especially with tremendous data on the molecules of different layers (e.g. genetic mutation, mRNA, protein etc.) which are accumulated based on a plenty of technologies. Besides, the technology-driven liquid biopsy biomarker is very promising to improve patients’ survival. The developments of biomarker discovery on these aspects are promoting the understanding of cancer, helping the stratification of patients and improving patients’ survival. Conclusion: Current developments on mechanisms-, data- and technology-driven biomarker discovery are achieving the aim of precision medicine and promoting the clinical application of biomarkers. Meanwhile, the complexity of cancer requires more effective biomarkers, which could be accomplished by a comprehensive integration of multiple types of biomarkers together with a deep understanding of cancer.

Artificial Intelligence as a Business Partner in Cardiovascular Precision Medicine: an Emerging Approach for Disease Detection and Treatment Optimization

2020 ◽  
Vol 28 ◽  
Author(s):  
Valeria Visco ◽  
Germano Junior Ferruzzi ◽  
Federico Nicastro ◽  
Nicola Virtuoso ◽  
Albino Carrizzo ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Heart Failure ◽  
Atrial Fibrillation ◽  
Precision Medicine ◽  
Clinical Decision Making ◽  
Disease Detection ◽  
Physician Patient Relationship ◽  
Invasive Approach ◽  
Tailored Treatment ◽  
The Impact

Background: In the real world, medical practice is changing hand in hand with the development of new Artificial Intelligence (AI) systems and problems from different areas have been successfully solved using AI algorithms. Specifically, the use of AI techniques in setting up or building precision medicine is significant in terms of the accuracy of disease discovery and tailored treatment. Moreover, with the use of technology, clinical personnel can deliver a very much efficient healthcare service. Objective: This article reviews AI state-of-the-art in cardiovascular disease management, focusing on diagnostic and therapeutic improvements. Methods: To that end, we conducted a detailed PubMed search on AI application from distinct areas of cardiology: heart failure, arterial hypertension, atrial fibrillation, syncope and cardiovascular rehabilitation. Particularly, to assess the impact of these technologies in clinical decision-making, this research considers technical and medical aspects. Results: On one hand, some devices in heart failure, atrial fibrillation and cardiac rehabilitation represent an inexpensive, not invasive or not very invasive approach to long-term surveillance and management in these areas. On the other hand, the availability of large datasets (big data) is a useful tool to predict the development and outcome of many cardiovascular diseases. In summary, with this new guided therapy, the physician can supply prompt, individualised, and tailored treatment and the patients feel safe as they are continuously monitored, with a significant psychological effect. Conclusion: Soon, tailored patient care via telemonitoring can improve the clinical practice because AI-based systems support cardiologists in daily medical activities, improving disease detection and treatment. However, the physician-patient relationship remains a pivotal step.

Feasibility and preliminary data for a State-wide South Carolina Lupus Registry

Lupus ◽  
2021 ◽  
pp. 096120332110145
Author(s):  
Brittany L Smalls ◽  
Trevor D Faith ◽  
Hetlena Johnson ◽  
Edith M Williams
Keyword(s):  
South Carolina ◽  
Lupus Erythematosus ◽  
Preliminary Data ◽  
Autoimmune Disorder ◽  
Medical Community ◽  
Small Samples ◽  
Control Board ◽  
Prospective Survey ◽  
And Control ◽  
The Impact

Background Systemic lupus erythematosus (SLE) or lupus is an autoimmune disorder whose cause and reason for disproportionate impact on minorities remains enigmatic. Furthermore, statistics describing lupus incidence and prevalence are outdated and often based on small samples. To begin to address this disparity this report describes preliminary data to be utilized in the development of a state-wide lupus registry in South Carolina. Methods A prospective survey and retrospective data from the South Carolina Budget and Control Board Office of Research & Statistics were used to capture data pertaining to knowledge of lupus, prevalence, and access to lupus care. Results Retrospective ORS data indicated there were 11,690 individuals living with lupus in 2014 with the average direct cost of $69,999.40 in medical care. Prospective surveys (N = 325), in over 16 locations in South Carolina, showed 31% knew someone with lupus, 16% had been diagnosed with lupus, and 50% did not know of a medical facility that treated lupus. Conclusion A lupus registry and repository will provide ongoing access for researchers on the impact of lupus on communities in South Carolina. Lupus is highly prevalent, but disproportionately represented in terms of patient information and participation in clinical trials, so it is also expected that this preliminary work will provide an ongoing process in which the medical community can better engage lupus patients.

scholarly journals A guideline for 3D printing terminology in biomedical research utilizing ISO/ASTM standards

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Amy E. Alexander ◽  
Nicole Wake ◽  
Leonid Chepelev ◽  
Philipp Brantner ◽  
Justin Ryan ◽  
...  
Keyword(s):  
3D Printing ◽  
Medical Literature ◽  
Three Dimensional ◽  
Physical Object ◽  
Medical Community ◽  
Layer By Layer ◽  
Digital File ◽  
Standardized Terminology ◽  
Standard Terms ◽  
The Impact

AbstractFirst patented in 1986, three-dimensional (3D) printing, also known as additive manufacturing or rapid prototyping, now encompasses a variety of distinct technology types where material is deposited, joined, or solidified layer by layer to create a physical object from a digital file. As 3D printing technologies continue to evolve, and as more manuscripts describing these technologies are published in the medical literature, it is imperative that standardized terminology for 3D printing is utilized. The purpose of this manuscript is to provide recommendations for standardized lexicons for 3D printing technologies described in the medical literature. For all 3D printing methods, standard general ISO/ASTM terms for 3D printing should be utilized. Additional, non-standard terms should be included to facilitate communication and reproducibility when the ISO/ASTM terms are insufficient in describing expository details. By aligning to these guidelines, the use of uniform terms for 3D printing and the associated technologies will lead to improved clarity and reproducibility of published work which will ultimately increase the impact of publications, facilitate quality improvement, and promote the dissemination and adoption of 3D printing in the medical community.

scholarly journals Quantifying the Impact of a Tsunami on Data-Driven Earthquake Relief Zone Planning in Los Angeles County via Multivariate Spatial Optimization

Geosciences ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 99 ◽  
Author(s):  
Yueqi Gu ◽  
Orhun Aydin ◽  
Jacqueline Sosa
Keyword(s):  
Los Angeles ◽  
A Priori ◽  
Safety Data ◽  
Spatial Optimization ◽  
Optimal Number ◽  
Multidisciplinary Optimization ◽  
Data Driven ◽  
Fault System ◽  
Design Data ◽  
The Impact

Post-earthquake relief zone planning is a multidisciplinary optimization problem, which required delineating zones that seek to minimize the loss of life and property. In this study, we offer an end-to-end workflow to define relief zone suitability and equitable relief service zones for Los Angeles (LA) County. In particular, we address the impact of a tsunami in the study due to LA’s high spatial complexities in terms of clustering of population along the coastline, and a complicated inland fault system. We design data-driven earthquake relief zones with a wide variety of inputs, including geological features, population, and public safety. Data-driven zones were generated by solving the p-median problem with the Teitz–Bart algorithm without any a priori knowledge of optimal relief zones. We define the metrics to determine the optimal number of relief zones as a part of the proposed workflow. Finally, we measure the impacts of a tsunami in LA County by comparing data-driven relief zone maps for a case with a tsunami and a case without a tsunami. Our results show that the impact of the tsunami on the relief zones can extend up to 160 km inland from the study area.

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