scholarly journals When Cyclodextrins Met Data Science: Unveiling Their Pharmaceutical Applications through Network Science and Text-Mining

Pharmaceutics ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1297
Author(s):  
Juliana Rincón-López ◽  
Yara C. Almanza-Arjona ◽  
Alejandro P. Riascos ◽  
Yareli Rojas-Aguirre
Keyword(s):  
Text Mining ◽  
Aqueous Solutions ◽  
Data Science ◽  
Network Science ◽  
Pharmaceutical Research ◽  
Cancer Therapies ◽  
Data Mining Approach ◽  
Solubility Improvement ◽  
Single Structure ◽  
Data Driven Approach

We present a data-driven approach to unveil the pharmaceutical technologies of cyclodextrins (CDs) by analyzing a dataset of CD pharmaceutical patents. First, we implemented network science techniques to represent CD patents as a single structure and provide a framework for unsupervised detection of keywords in the patent dataset. Guided by those keywords, we further mined the dataset to examine the patenting trends according to CD-based dosage forms. CD patents formed complex networks, evidencing the supremacy of CDs for solubility enhancement and how this has triggered cutting-edge applications based on or beyond the solubility improvement. The networks exposed the significance of CDs to formulate aqueous solutions, tablets, and powders. Additionally, they highlighted the role of CDs in formulations of anti-inflammatory drugs, cancer therapies, and antiviral strategies. Text-mining showed that the trends in CDs for aqueous solutions, tablets, and powders are going upward. Gels seem to be promising, while patches and fibers are emerging. Cyclodextrins’ potential in suspensions and emulsions is yet to be recognized and can become an opportunity area. This is the first unsupervised/supervised data-mining approach aimed at depicting a landscape of CDs to identify trending and emerging technologies and uncover opportunity areas in CD pharmaceutical research.

scholarly journals Modern Clinical Text Mining: A Guide and Review

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Bethany Percha
Keyword(s):  
Machine Learning ◽  
Text Mining ◽  
Data Science ◽  
Annual Review ◽  
Publication Date ◽  
Biomedical Data ◽  
Clinical Text ◽  
Quality Improvement Research ◽  
Comprehensive Survey ◽  
Technical Advances

Electronic health records (EHRs) are becoming a vital source of data for healthcare quality improvement, research, and operations. However, much of the most valuable information contained in EHRs remains buried in unstructured text. The field of clinical text mining has advanced rapidly in recent years, transitioning from rule-based approaches to machine learning and, more recently, deep learning. With new methods come new challenges, however, especially for those new to the field. This review provides an overview of clinical text mining for those who are encountering it for the first time (e.g., physician researchers, operational analytics teams, machine learning scientists from other domains). While not a comprehensive survey, this review describes the state of the art, with a particular focus on new tasks and methods developed over the past few years. It also identifies key barriers between these remarkable technical advances and the practical realities of implementation in health systems and in industry. Expected final online publication date for the Annual Review of Biomedical Data Science, Volume 4 is July 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

scholarly journals ADVANCING AN INTERDISCIPLINARY SCIENCE OF AGING THROUGH A PRACTICE-BASED DATA SCIENCE APPROACH

2019 ◽  
Vol 3 (Supplement_1) ◽  
pp. S480-S480
Author(s):  
Robert Lucero ◽  
Ragnhildur Bjarnadottir
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Older Adults ◽  
Text Mining ◽  
Language Processing ◽  
Fall Risk ◽  
Data Science ◽  
Care Quality ◽  
Science Approach ◽  
Hospitalized Older Adults

Abstract Two hundred and fifty thousand older adults die annually in United States hospitals because of iatrogenic conditions (ICs). Clinicians, aging experts, patient advocates and federal policy makers agree that there is a need to enhance the safety of hospitalized older adults through improved identification and prevention of ICs. To this end, we are building a research program with the goal of enhancing the safety of hospitalized older adults by reducing ICs through an effective learning health system. Leveraging unique electronic data and healthcare system and human resources at the University of Florida, we are applying a state-of-the-art practice-based data science approach to identify risk factors of ICs (e.g., falls) from structured (i.e., nursing, clinical, administrative) and unstructured or text (i.e., registered nurse’s progress notes) data. Our interdisciplinary academic-clinical partnership includes scientific and clinical experts in patient safety, care quality, health outcomes, nursing and health informatics, natural language processing, data science, aging, standardized terminology, clinical decision support, statistics, machine learning, and hospital operations. Results to date have uncovered previously unknown fall risk factors within nursing (i.e., physical therapy initiation), clinical (i.e., number of fall risk increasing drugs, hemoglobin level), and administrative (i.e., Charlson Comorbidity Index, nurse skill mix, and registered nurse staffing ratio) structured data as well as patient cognitive, environmental, workflow, and communication factors in text data. The application of data science methods (i.e., machine learning and text-mining) and findings from this research will be used to develop text-mining pipelines to support sustained data-driven interdisciplinary aging studies to reduce ICs.

scholarly journals Data-Driven Approach for Incident Management in a Smart City

2020 ◽  
Vol 10 (22) ◽  
pp. 8281
Author(s):  
Luís B. Elvas ◽  
Carolina F. Marreiros ◽  
João M. Dinis ◽  
Maria C. Pereira ◽  
Ana L. Martins ◽  
...  
Keyword(s):  
Data Mining ◽  
Smart City ◽  
City Planning ◽  
Smart Cities ◽  
Data Driven ◽  
Incident Management ◽  
Cascading Effects ◽  
Data Mining Approach ◽  
Industry Standard ◽  
Data Driven Approach

Buildings in Lisbon are often the victim of several types of events (such as accidents, fires, collapses, etc.). This study aims to apply a data-driven approach towards knowledge extraction from past incident data, nowadays available in the context of a Smart City. We apply a Cross Industry Standard Process for Data Mining (CRISP-DM) approach to perform incident management of the city of Lisbon. From this data-driven process, a descriptive and predictive analysis of an events dataset provided by the Lisbon Municipality was possible, together with other data obtained from the public domain, such as the temperature and humidity on the day of the events. The dataset provided contains events from 2011 to 2018 for the municipality of Lisbon. This data mining approach over past data identified patterns that provide useful knowledge for city incident managers. Additionally, the forecasts can be used for better city planning, and data correlations of variables can provide information about the most important variables towards those incidents. This approach is fundamental in the context of smart cities, where sensors and data can be used to improve citizens’ quality of life. Smart Cities allow the collecting of data from different systems, and for the case of disruptive events, these data allow us to understand them and their cascading effects better.

scholarly journals Extracting Value from Industrial Alarms and Events: A Data-Driven Approach Based on Exploratory Data Analysis

Sensors ◽  
2019 ◽  
Vol 19 (12) ◽  
pp. 2772 ◽  
Author(s):  
Aguinaldo Bezerra ◽  
Ivanovitch Silva ◽  
Luiz Affonso Guedes ◽  
Diego Silva ◽  
Gustavo Leitão ◽  
...  
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Data Exchange ◽  
Data Science ◽  
Exploratory Data Analysis ◽  
Data Driven ◽  
Industrial Data ◽  
Industrial Big Data ◽  
Data Driven Approach ◽  
Exploratory Data

Alarm and event logs are an immense but latent source of knowledge commonly undervalued in industry. Though, the current massive data-exchange, high efficiency and strong competitiveness landscape, boosted by Industry 4.0 and IIoT (Industrial Internet of Things) paradigms, does not accommodate such a data misuse and demands more incisive approaches when analyzing industrial data. Advances in Data Science and Big Data (or more precisely, Industrial Big Data) have been enabling novel approaches in data analysis which can be great allies in extracting hitherto hidden information from plant operation data. Coping with that, this work proposes the use of Exploratory Data Analysis (EDA) as a promising data-driven approach to pave industrial alarm and event analysis. This approach proved to be fully able to increase industrial perception by extracting insights and valuable information from real-world industrial data without making prior assumptions.

Voice of Cancer Patients: Analysis of patient concerns regarding cognitive deficits associated with treatment for breast cancer.

2015 ◽  
Vol 33 (28_suppl) ◽  
pp. 82-82
Author(s):  
Sangeeta Aggarwal ◽  
Mingfeng Liu ◽  
Rishi Sharma ◽  
Fred Yang ◽  
Ankit Gupta ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Language Processing ◽  
Data Science ◽  
Strong Association ◽  
Supportive Therapy ◽  
Relevant Information ◽  
Cancer Therapies ◽  
Online Forums ◽  
Cancer Treatments ◽  
System A

82 Background: Patients undergoing breast cancer treatment often report Symptoms of Cognitive Deficit (SCD). Many of them share their experiences on online forums, which contain millions of freely shared messages that can be used to analyze these SCD. Unfortunately, this data is unstructured, making it difficult to analyze. In this project we organize this data using methods from Big Data Science (BDS) and analyze it by creating a Decision Support System (DSS): an interface that can be used by patients and providers to understand how SCD are associated with specific types – hormonal only (HT), chemo only (CT), or both (CT/HT) – of breast cancer therapies. Methods: We collected 3.5 million unique messages from 20 unrestricted breast cancer forums that provide clinically relevant information. We next built custom ontologies for breast cancer treatments, SCD, and supportive therapies. Then, we created a DSS using methods from BDS, including topic modeling, information retrieval, and natural language processing to extract the relevant data from these messages. We also used token windows and co-occurrence-based algorithms to associate treatment with SCD and supportive therapies. To use this system, a user provides disease-related parameters and the treatment. The DSS then gives the percentage of messages discussing SCD for a similar cohort of patients and the percentage of messages that discuss supportive therapies for each of these SCD. Results: We found 15719 messages that had strong association of SCD with treatments. 3355 messages were from HT patients, 5740 messages were from CT patients, and 9095 messages were from CT/HT patients. Among HT, 28.18% patients taking aromatase inhibitors and 19.20% taking tamoxifen associated SCD to HT. Among CT, 35.26% patient receiving taxane containing chemo associated SCD to CT. SCD worsened during HT for CT/HT patients. Suggestive therapy: 80 messages found Vitamin B12 and B6 useful, 65 suggested Acetyle-L-Carnitine, and 50 suggested playing word games. Conclusions: Using methods from BDS, our DSS reliably associates SCD with HT, CT and CT/CT, and suggests supportive therapies. More research is needed to evaluate the role of supportive therapy for SCD.

scholarly journals What is Data Science? An Operational Definition based on Text Mining of Data Science Curricula

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Zhiyong Zhang ◽  
Keyword(s):  
United States ◽  
Text Mining ◽  
Computer Science ◽  
Topic Modeling ◽  
Data Science ◽  
The United States ◽  
Operational Definition ◽  
Bottom Up ◽  
Science Curricula

Data science has maintained its popularity for about 20 years. This study adopts a bottom-up approach to understand what data science is by analyzing the descriptions of courses offered by the data science programs in the United States. Through topic modeling, 14 topics are identified from the current curricula of 56 data science programs. These topics reiterate that data science is at the intersection of statistics, computer science, and substantive fields.

scholarly journals The Right Tools for the Job: The Case for Spatial Science Tool-Building

2020 ◽  
Author(s):  
Geoff Boeing
Keyword(s):  
Open Source ◽  
Open Source Software ◽  
Data Science ◽  
Network Science ◽  
Science Literature ◽  
Spatial Network ◽  
Multiple Perspectives ◽  
Development Tool ◽  
The Right ◽  
Tool Building

This paper was presented as the 8th annual Transactions in GIS plenary address at the American Association of Geographers annual meeting in Washington, DC. The spatial sciences have recently seen growing calls for more accessible software and tools that better embody geographic science and theory. Urban spatial network science offers one clear opportunity: from multiple perspectives, tools to model and analyze nonplanar urban spatial networks have traditionally been inaccessible, atheoretical, or otherwise limiting. This paper reflects on this state of the field. Then it discusses the motivation, experience, and outcomes of developing OSMnx, a tool intended to help address this. Next it reviews this tool's use in the recent multidisciplinary spatial network science literature to highlight upstream and downstream benefits of open‐source software development. Tool-building is an essential but poorly incentivized component of academic geography and social science more broadly. To conduct better science, we need to build better tools. The paper concludes with paths forward, emphasizing open-source software and reusable computational data science beyond mere reproducibility and replicability.

scholarly journals Adoption of Big Data by Global Chemical Industries

2021 ◽  
Author(s):  
Ashiff Khan ◽  
A Seetharaman ◽  
Abhijit Dasgupta
Keyword(s):  
Big Data ◽  
Data Science ◽  
Data Driven ◽  
Smart Manufacturing ◽  
Time Data ◽  
New Era ◽  
Potential Applications ◽  
Data Driven Approach ◽  
Process Plants ◽  
The Right

The new era of Big Data (BD) is influencing the chemical industries tremendously, providing several opportunities to reshape the way they operate and for shifting towards smart manufacturing. Given the availability of free software, and the large amount of real-time data generated and stored in process plants why many chemical industries are still not fully adopting BD? The industry is just starting to realize the importance of a large amount of data that they own to make the right decisions and to support their strategies. This article is exploring the importance of professional competencies and data science that influence BD in chemical industries for shifting towards smart manufacturing in a fast and reliable manner. This article utilizes a literature review and identifies potential applications in the chemical industry to shift from conventional methods towards a data-driven approach.

Sign in / Sign up

Export Citation Format

Share Document