Equation Correction: Technological Innovations in Disease Management: Text Mining US Patent Data From 1995 to 2017 (Preprint)

2019 ◽  
Author(s):  
Ming Huang ◽  
Maryam Zolnoori ◽  
Joyce E Balls-Berry ◽  
Tabetha A Brockman ◽  
Christi A Patten ◽  
...  
Keyword(s):  
Text Mining ◽  
Disease Management ◽  
Technological Innovations ◽  
Patent Data

scholarly journals Multimedia Appendix Correction: Technological Innovations in Disease Management: Text Mining US Patent Data From 1995 to 2017

10.2196/14678 ◽  
2019 ◽  
Vol 21 (7) ◽  
pp. e14678
Author(s):  
Ming Huang ◽  
Maryam Zolnoori ◽  
Joyce E Balls-Berry ◽  
Tabetha A Brockman ◽  
Christi A Patten ◽  
...  
Keyword(s):  
Text Mining ◽  
Disease Management ◽  
Technological Innovations ◽  
Patent Data

scholarly journals Technological Innovations in Disease Management: Text Mining US Patent Data From 1995 to 2017

10.2196/13316 ◽  
2019 ◽  
Vol 21 (4) ◽  
pp. e13316 ◽  
Author(s):  
Ming Huang ◽  
Maryam Zolnoori ◽  
Joyce E Balls-Berry ◽  
Tabetha A Brockman ◽  
Christi A Patten ◽  
...  
Keyword(s):  
Text Mining ◽  
Disease Management ◽  
Technological Innovations ◽  
Patent Data

scholarly journals Technological Innovations in Disease Management: Text Mining US Patent Data From 1995 to 2017 (Preprint)

2019 ◽  
Author(s):  
Ming Huang ◽  
Maryam Zolnoori ◽  
Joyce E Balls-Berry ◽  
Tabetha A Brockman ◽  
Christi A Patten ◽  
...  
Keyword(s):  
United States ◽  
Resource Allocation ◽  
Disease Management ◽  
Research And Development ◽  
Biomedical Research ◽  
Malignant Neoplasm ◽  
The United States ◽  
Technological Innovations ◽  
Patent Data ◽  
Patent Documents

BACKGROUND Patents are important intellectual property protecting technological innovations that inspire efficient research and development in biomedicine. The number of awarded patents serves as an important indicator of economic growth and technological innovation. Researchers have mined patents to characterize the focuses and trends of technological innovations in many fields. OBJECTIVE To expand patent mining to biomedicine and facilitate future resource allocation in biomedical research for the United States, we analyzed US patent documents to determine the focuses and trends of protected technological innovations across the entire disease landscape. METHODS We analyzed more than 5 million US patent documents between 1995 and 2017, using summary statistics and dynamic topic modeling. More specifically, we investigated the disease coverage and latent topics in patent documents over time. We also incorporated the patent data into the calculation of our recently developed Research Opportunity Index (ROI) and Public Health Index (PHI), to recalibrate the resource allocation in biomedical research. RESULTS Our analysis showed that protected technological innovations have been primarily focused on socioeconomically critical diseases such as “other cancers” (malignant neoplasm of head, face, neck, abdomen, pelvis, or limb; disseminated malignant neoplasm; Merkel cell carcinoma; and malignant neoplasm, malignant carcinoid tumors, neuroendocrine tumor, and carcinoma in situ of an unspecified site), diabetes mellitus, and obesity. The United States has significantly improved resource allocation to biomedical research and development over the past 17 years, as illustrated by the decreasing PHI. Diseases with positive ROI, such as ankle and foot fracture, indicate potential research opportunities for the future. Development of novel chemical or biological drugs and electrical devices for diagnosis and disease management is the dominating topic in patented inventions. CONCLUSIONS This multifaceted analysis of patent documents provides a deep understanding of the focuses and trends of technological innovations in disease management in patents. Our findings offer insights into future research and innovation opportunities and provide actionable information to facilitate policy makers, payers, and investors to make better evidence-based decisions regarding resource allocation in biomedicine.

scholarly journals Text mining factor analysis (TFA) in green tea patent data

2017 ◽  
Author(s):  
Sela Rahmawati ◽  
Jadi Suprijadi ◽  
Zulhanif
Keyword(s):  
Factor Analysis ◽  
Text Mining ◽  
Green Tea ◽  
Patent Data
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