Improving Research Patient Data Repositories from a Health Data Industry Viewpoint (Preprint)

2021 ◽  
Author(s):  
Chunlei Tang ◽  
Li Zhou ◽  
Joseph Plasek ◽  
Yangyong Zhu ◽  
Yajun Huang ◽  
...  
Keyword(s):  
Best Practices ◽  
Data Science ◽  
Patient Data ◽  
Biomedical Data ◽  
Data Repositories ◽  
New Developments ◽  
Cross Border ◽  
Critical Resource ◽  
Central Resource ◽  
Science And Research

UNSTRUCTURED Electronic patient data are critical to clinical and translational science, and research patient data repositories (RPDRs) are a central resource for any work in biomedical data science. However, the data science ecosystem, due to its inherently transdisciplinary nature, poses challenges to existing RPDRs and demands expansions and new developments, calling for a wide variety of new functions and capabilities in the administrative, educational, and organizational domains. The power of data science in the business realm is tremendous. In business, it is already viewed as a critical resource, and this will likely occur in healthcare as well. This perspective focuses on best practices in developing RPDRs, and identifies areas which we believe have not received enough attention. These include deployment, contribution calculation, internal talent marketplaces, data partnerships, data sovereigns’ new capital assets, and cross-border data sharing.

Big Data to Knowledge: Application of Machine Learning to Predictive Modeling of Therapeutic Response in Cancer.

2020 ◽  
Vol 21 ◽  
Author(s):  
Sukanya Panja ◽  
Sarra Rahem ◽  
Cassandra J. Chu ◽  
Antonina Mitrofanova
Keyword(s):  
Machine Learning ◽  
Missing Values ◽  
Therapeutic Response ◽  
Patient Data ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Complex Data ◽  
Human Machine Interaction ◽  
Data Repositories ◽  
Response Modeling

Background: In recent years, the availability of high throughput technologies, establishment of large molecular patient data repositories, and advancement in computing power and storage have allowed elucidation of complex mechanisms implicated in therapeutic response in cancer patients. The breadth and depth of such data, alongside experimental noise and missing values, requires a sophisticated human-machine interaction that would allow effective learning from complex data and accurate forecasting of future outcomes, ideally embedded in the core of machine learning design. Objective: In this review, we will discuss machine learning techniques utilized for modeling of treatment response in cancer, including Random Forests, support vector machines, neural networks, and linear and logistic regression. We will overview their mathematical foundations and discuss their limitations and alternative approaches all in light of their application to therapeutic response modeling in cancer. Conclusion: We hypothesize that the increase in the number of patient profiles and potential temporal monitoring of patient data will define even more complex techniques, such as deep learning and causal analysis, as central players in therapeutic response modeling.

scholarly journals Finding useful data across multiple biomedical data repositories using DataMed

2017 ◽  
Vol 49 (6) ◽  
pp. 816-819 ◽  
Author(s):  
Lucila Ohno-Machado ◽  
Susanna-Assunta Sansone ◽  
George Alter ◽  
Ian Fore ◽  
Jeffrey Grethe ◽  
...  
Keyword(s):  
Biomedical Data ◽  
Data Repositories

The central role of data repositories and data models in Data Science and Advanced Analytics

2021 ◽  
Author(s):  
Ladjel Bellatreche ◽  
Carlos Ordonez ◽  
Dominique Méry ◽  
Matteo Golfarelli ◽  
El Hassan Abdelwahed
Keyword(s):  
Data Science ◽  
Data Models ◽  
Data Repositories ◽  
Advanced Analytics

scholarly journals COVID-19 Evidence Accelerator: A parallel analysis to describe the use of Hydroxychloroquine with or without Azithromycin among hospitalized COVID-19 patients

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0248128
Author(s):  
Mark Stewart ◽  
Carla Rodriguez-Watson ◽  
Adem Albayrak ◽  
Julius Asubonteng ◽  
Andrew Belli ◽  
...  
Keyword(s):  
Best Practices ◽  
Adverse Events ◽  
Data Science ◽  
Proportional Hazards ◽  
Cox Proportional Hazards ◽  
Parallel Analysis ◽  
Science Data ◽  
Systems Research ◽  
Cox Proportional Hazards Models ◽  
Global Threat

Background The COVID-19 pandemic remains a significant global threat. However, despite urgent need, there remains uncertainty surrounding best practices for pharmaceutical interventions to treat COVID-19. In particular, conflicting evidence has emerged surrounding the use of hydroxychloroquine and azithromycin, alone or in combination, for COVID-19. The COVID-19 Evidence Accelerator convened by the Reagan-Udall Foundation for the FDA, in collaboration with Friends of Cancer Research, assembled experts from the health systems research, regulatory science, data science, and epidemiology to participate in a large parallel analysis of different data sets to further explore the effectiveness of these treatments. Methods Electronic health record (EHR) and claims data were extracted from seven separate databases. Parallel analyses were undertaken on data extracted from each source. Each analysis examined time to mortality in hospitalized patients treated with hydroxychloroquine, azithromycin, and the two in combination as compared to patients not treated with either drug. Cox proportional hazards models were used, and propensity score methods were undertaken to adjust for confounding. Frequencies of adverse events in each treatment group were also examined. Results Neither hydroxychloroquine nor azithromycin, alone or in combination, were significantly associated with time to mortality among hospitalized COVID-19 patients. No treatment groups appeared to have an elevated risk of adverse events. Conclusion Administration of hydroxychloroquine, azithromycin, and their combination appeared to have no effect on time to mortality in hospitalized COVID-19 patients. Continued research is needed to clarify best practices surrounding treatment of COVID-19.

scholarly journals A Review of AI and Data Science Support for Cancer Management

2020 ◽  
Author(s):  
E. Parimbelli ◽  
S. Wilk ◽  
R. Cornet ◽  
P. Sniatala ◽  
K. Sniatala ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Decision Support ◽  
Best Practices ◽  
Cancer Patients ◽  
Predictive Modeling ◽  
Data Science ◽  
Cancer Management ◽  
Research Challenges ◽  
Open Research

AbstractIntroductionThanks to improvement of care, cancer has become a chronic condition. But due to the toxicity of treatment, the importance of supporting the quality of life (QoL) of cancer patients increases. Monitoring and managing QoL relies on data collected by the patient in his/her home environment, its integration, and its analysis, which supports personalization of cancer management recommendations. We review the state-of-the-art of computerized systems that employ AI and Data Science methods to monitor the health status and provide support to cancer patients managed at home.ObjectiveOur main objective is to analyze the literature to identify open research challenges that a novel decision support system for cancer patients and clinicians will need to address, point to potential solutions, and provide a list of established best-practices to adopt.MethodsWe designed a review study, in compliance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, analyzing studies retrieved from PubMed related to monitoring cancer patients in their home environments via sensors and self-reporting: what data is collected, what are the techniques used to collect data, semantically integrate it, infer the patient’s state from it and deliver coaching/behavior change interventions.ResultsStarting from an initial corpus of 819 unique articles, a total of 180 papers were considered in the full-text analysis and 109 were finally included in the review. Our findings are organized and presented in four main sub-topics consisting of data collection, data integration, predictive modeling and patient coaching.ConclusionDevelopment of modern decision support systems for cancer needs to utilize best practices like the use of validated electronic questionnaires for quality-of-life assessment, adoption of appropriate information modeling standards supplemented by terminologies/ontologies, adherence to FAIR data principles, external validation, stratification of patients in subgroups for better predictive modeling, and adoption of formal behavior change theories. Open research challenges include supporting emotional and social dimensions of well-being, including PROs in predictive modeling, and providing better customization of behavioral interventions for the specific population of cancer patients.

scholarly journals Evaluation of Privacy Risks of Patients’ Data in China: Case Study

10.2196/13046 ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. e13046 ◽  
Author(s):  
Mengchun Gong ◽  
Shuang Wang ◽  
Lezi Wang ◽  
Chao Liu ◽  
Jianyang Wang ◽  
...  
Keyword(s):  
Health Care ◽  
Data Sharing ◽  
Data Privacy ◽  
Patient Data ◽  
Chinese Patients ◽  
Biomedical Data ◽  
Safe Harbor ◽  
Patient Privacy ◽  
Privacy Risk ◽  
Privacy Risks

Background Patient privacy is a ubiquitous problem around the world. Many existing studies have demonstrated the potential privacy risks associated with sharing of biomedical data. Owing to the increasing need for data sharing and analysis, health care data privacy is drawing more attention. However, to better protect biomedical data privacy, it is essential to assess the privacy risk in the first place. Objective In China, there is no clear regulation for health systems to deidentify data. It is also not known whether a mechanism such as the Health Insurance Portability and Accountability Act (HIPAA) safe harbor policy will achieve sufficient protection. This study aimed to conduct a pilot study using patient data from Chinese hospitals to understand and quantify the privacy risks of Chinese patients. Methods We used g-distinct analysis to evaluate the reidentification risks with regard to the HIPAA safe harbor approach when applied to Chinese patients’ data. More specifically, we estimated the risks based on the HIPAA safe harbor and limited dataset policies by assuming an attacker has background knowledge of the patient from the public domain. Results The experiments were conducted on 0.83 million patients (with data field of date of birth, gender, and surrogate ZIP codes generated based on home address) across 33 provincial-level administrative divisions in China. Under the Limited Dataset policy, 19.58% (163,262/833,235) of the population could be uniquely identifiable under the g-distinct metric (ie, 1-distinct). In contrast, the Safe Harbor policy is able to significantly reduce privacy risk, where only 0.072% (601/833,235) of individuals are uniquely identifiable, and the majority of the population is 3000 indistinguishable (ie the population is expected to share common attributes with 3000 or less people). Conclusions Through the experiments based on real-world patient data, this work illustrates that the results of g-distinct analysis about Chinese patient privacy risk are similar to those from a previous US study, in which data from different organizations/regions might be vulnerable to different reidentification risks under different policies. This work provides reference to Chinese health care entities for estimating patients’ privacy risk during data sharing, which laid the foundation of privacy risk study about Chinese patients’ data in the future.

scholarly journals Entering the Era of Data Science: Targeted Learning and the Integration of Statistics and Computational Data Analysis

2014 ◽  
Vol 2014 ◽  
pp. 1-19 ◽  
Author(s):  
Mark J. van der Laan ◽  
Richard J. C. M. Starmans
Keyword(s):  
Big Data ◽  
Data Science ◽  
State Of The Art ◽  
Accurate Estimation ◽  
Learning Tools ◽  
New Developments ◽  
Data Movement ◽  
Computational Data ◽  
Adaptive Estimators ◽  
Data Adaptive

This outlook paper reviews the research of van der Laan’s group on Targeted Learning, a subfield of statistics that is concerned with the construction of data adaptive estimators of user-supplied target parameters of the probability distribution of the data and corresponding confidence intervals, aiming at only relying on realistic statistical assumptions. Targeted Learning fully utilizes the state of the art in machine learning tools, while still preserving the important identity of statistics as a field that is concerned with both accurate estimation of the true target parameter value and assessment of uncertainty in order to make sound statistical conclusions. We also provide a philosophical historical perspective on Targeted Learning, also relating it to the new developments in Big Data. We conclude with some remarks explaining the immediate relevance of Targeted Learning to the current Big Data movement.

scholarly journals Shared Data Science Infrastructure for Genomics Data

2018 ◽  
Author(s):  
Hamid Bagher ◽  
Usha Muppiral ◽  
Andrew J Severin ◽  
Hridesh Rajan
Keyword(s):  
Data Science ◽  
Gene Annotation ◽  
Large Data ◽  
Biological Data ◽  
Genomic Research ◽  
Data Repository ◽  
Small Data ◽  
Data Repositories ◽  
Shared Data ◽  
Genome Assemblies

AbstractBackgroundCreating a computational infrastructure to analyze the wealth of information contained in data repositories that scales well is difficult due to significant barriers in organizing, extracting and analyzing relevant data. Shared Data Science Infrastructures like Boa can be used to more efficiently process and parse data contained in large data repositories. The main features of Boa are inspired from existing languages for data intensive computing and can easily integrate data from biological data repositories.ResultsHere, we present an implementation of Boa for Genomic research (BoaG) on a relatively small data repository: RefSeq’s 97,716 annotation (GFF) and assembly (FASTA) files and metadata. We used BoaG to query the entire RefSeq dataset and gain insight into the RefSeq genome assemblies and gene model annotations and show that assembly quality using the same assembler varies depending on species.ConclusionsIn order to keep pace with our ability to produce biological data, innovative methods are required. The Shared Data Science Infrastructure, BoaG, can provide greater access to researchers to efficiently explore data in ways previously not possible for anyone but the most well funded research groups. We demonstrate the efficiency of BoaG to explore the RefSeq database of genome assemblies and annotations to identify interesting features of gene annotation as a proof of concept for much larger datasets.

scholarly journals NFDI4Chem - Towards a National Research Data Infrastructure for Chemistry in Germany

2020 ◽  
Vol 6 ◽  
Author(s):  
Christoph Steinbeck ◽  
Oliver Koepler ◽  
Felix Bach ◽  
Sonja Herres-Pawlis ◽  
Nicole Jung ◽  
...  
Keyword(s):  
Data Management ◽  
Data Science ◽  
Open Data ◽  
Research Data ◽  
Data Standards ◽  
Data Repositories ◽  
Data Infrastructure ◽  
Research Data Management ◽  
Wide Range ◽  
Chemistry Community

The vision of NFDI4Chem is the digitalisation of all key steps in chemical research to support scientists in their efforts to collect, store, process, analyse, disclose and re-use research data. Measures to promote Open Science and Research Data Management (RDM) in agreement with the FAIR data principles are fundamental aims of NFDI4Chem to serve the chemistry community with a holistic concept for access to research data. To this end, the overarching objective is the development and maintenance of a national research data infrastructure for the research domain of chemistry in Germany, and to enable innovative and easy to use services and novel scientific approaches based on re-use of research data. NFDI4Chem intends to represent all disciplines of chemistry in academia. We aim to collaborate closely with thematically related consortia. In the initial phase, NFDI4Chem focuses on data related to molecules and reactions including data for their experimental and theoretical characterisation. This overarching goal is achieved by working towards a number of key objectives: Key Objective 1: Establish a virtual environment of federated repositories for storing, disclosing, searching and re-using research data across distributed data sources. Connect existing data repositories and, based on a requirements analysis, establish domain-specific research data repositories for the national research community, and link them to international repositories. Key Objective 2: Initiate international community processes to establish minimum information (MI) standards for data and machine-readable metadata as well as open data standards in key areas of chemistry. Identify and recommend open data standards in key areas of chemistry, in order to support the FAIR principles for research data. Finally, develop standards, if there is a lack. Key Objective 3: Foster cultural and digital change towards Smart Laboratory Environments by promoting the use of digital tools in all stages of research and promote subsequent Research Data Management (RDM) at all levels of academia, beginning in undergraduate studies curricula. Key Objective 4: Engage with the chemistry community in Germany through a wide range of measures to create awareness for and foster the adoption of FAIR data management. Initiate processes to integrate RDM and data science into curricula. Offer a wide range of training opportunities for researchers. Key Objective 5: Explore synergies with other consortia and promote cross-cutting development within the NFDI. Key Objective 6: Provide a legally reliable framework of policies and guidelines for FAIR and open RDM.

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