MODIFICATION
R -- Advanced Natural Language Processing and Artificial Intelligence Platform
- Notice Date
- 11/5/2018
- Notice Type
- Modification/Amendment
- NAICS
- 541519
— Other Computer Related Services
- Contracting Office
- Department of Health and Human Services, National Institutes of Health, National Library of Medicine, 6707 Democracy Blvd., Suite 105, Bethesda, Maryland, 20894, United States
- ZIP Code
- 20894
- Solicitation Number
- 75N97019Q00005
- Archive Date
- 12/4/2018
- Point of Contact
- Em'Ria J. Briscoe, Phone: 3014354384
- E-Mail Address
-
briscoee@mail.nih.gov
(briscoee@mail.nih.gov)
- Small Business Set-Aside
- N/A
- Description
- This notice is a request for information (RFI) and is not a request for proposals (RFP). The government will not award a contract on the basis of this notice, or otherwise pay for information solicited by it. Proprietary information should be clearly marked. The requested information is for planning and market research purposes only and will not be publicly released. In accordance with FAR 15.201(E), responses to this RFI are not offers and cannot be accepted by the Government to form a binding contract. The National Institutes of Health (NIH), Office of the Director (OD), Office of Extramural Research (OER), Office of Research Information Systems (ORIS) has a requirement to identify and test an Advanced Natural Language Processing and Artificial Intelligence platform for categorization of scientific projects. It is the intent of the NIH/OD/OER/ORIS to identify and test a platform that uses advanced natural language processing to read descriptions of scientific projects and categorize the projects as Alzheimer's and/or one of 16 other disease, condition, or research categories. The results of the test will be compared against the current NIH Research, Condition, and Disease Categorization (RCDC) system (https://report.nih.gov/categorical_spending.aspx; https://report.nih.gov/rcdc/index.aspx), by analyzing the results of the identification and categorization of projects to Alzheimer's and 16 other disease, condition, or research categories. Background: The National Institutes of Health (NIH), Office of Extramural Research (OER), provides the corporate framework for research administration, ensuring scientific integrity, public accountability, and effective stewardship of the NIH extramural research portfolio. OER develops and maintains research portfolio reporting databases and analysis tools on behalf of the NIH, as listed at https://RePORT.nih.gov. These tools are intended to improve NIH-wide internal staff and public access to data on NIH research and management activities to allow staff to evaluate and manage research portfolios as well as to satisfy certain reporting requirements of the NIH Reform Act of 2006 (P.L. 109-482; H.R. 6164, §402B). These tools integrate new and existing data sources to allow user-defined querying, reporting, analysis, evaluation, and visualization of NIH grants, intramural projects, contracts, and associated outputs. An additional purpose is to develop and maintain similar tools to support other offices within NIH and other federal science agencies. Information Requested: The NIH/OER, Office of Research Information Systems (ORIS), is seeking to identify and test an artificial intelligence platform against the current NIH RCDC system by comparing the results of identification and categorization of 3 years of NIH sponsored projects to categories of Alzheimer's and 16 other disease, condition, or research categories. The NIH/OER/ORIS, is requesting information from offerors that: a. Propose an easy to use system that employs advanced natural language processing algorithms and artificial intelligence to categorize 3 years of NIH projects to categories of Alzheimer's and 16 other disease, condition, or research categories. b. The categories to be tested are: 1. Alzheimer's Disease 2. Frontotemporal Dementia 3. Pick's Disease 4. Dementia 5. Lewy Body Dementia 6. Vascular Cognitive Impairment/Dementias 7. Traumatic brain injury 8. ALS 9. Parkinson's Disease 10. Multiple Sclerosis 11. Prevention 12. Nutrition 13. Hodgkin's Disease 14. American Indians/Alask Natives 15. Breast Cancer 16. Ovarian Cancer 17. Tyberculosis c. The platform may be installed locally at NIH or in the cloud but must conform to NIST security requirements for moderately sensitive data. d. Allows the NIH to actively participate in the test via hands on use of the system including training, category development and validation. e. Once the test is complete, provide a written report on the project including categorization results and comparisons to existing categorical results (e.g. 1) How the system being tested determines the projects relevant to the category, 2) why the system being tested produces better or worse results, 3) how the tested technology differs from the existing technology, etc.) f. Once the test is complete, provide a breakdown of the costs for the test and an estimate of the costs needed to license, install and implement your product for NIH categorization in its entirety. g. Propose an estimated cost for this test and reports over the 6 month period of performance. All responses will be evaluated on their ability to handle each of the following specified requirements for the platform: 1. Utilizes advanced natural language process to "read" the documents (e.g. to identify scientific intent, recognize negation, recognize abbreviations, recognize strings and coordination, etc) 2. Uses artificial intelligence to categorize the scientific projects. 3. Ease of training of the AI platform (Gold standard sets will be available to train the platform.) 4. Previous experience in machine reading and categorization. 5. Subscription and/or license(s) for use of the categorization platform provided either as a local hardware (HW)/software (SW)* package or through cloud services. 6. Ensure that either the HW/SW package or cloud services conform to NIST security requirements for moderately sensitive data and can pass NIH security scanning and penetration testing. 7. Provide seats for up to 40 users during testing. 8. Provide robust performance and responsiveness. 9. Provide an easy to use interface. 10. The platform is easily scaled to run at an enterprise level and can handle at least 1000 different categories 11. Can return data as a list or as an interactive, customizable graphic. 12. Support multiple data export formats (i.e., Excel, PDF, CSV, and Word). 13. Provide facilities to access data in an Oracle database in static objects or loaded through dynamic scheduling capabilities. 14. Data access can be individually configured to allow only specific data to be accessible if needed. 15. The proposed cost of the test. *If just a software package, the application should be compatible with standard virtual machine Linux. Response Requirements Please submit the following information: Name and address of company and or companies (if there is a teaming arrangement) 1. Corporate capabilities Advanced Augmented Analytics experience Federal grants management systems experience Describe sample system 2. Up to three (3) relevant experiences, including: Contract name Contracting Agency or Department or Company Agency/Department/Company contact information (Name, e-mail address, and telephone number) Yearly contract value (in $$) Period of performance Description of work and how it relates to NIH/OER/ORIS requirements 3. Suggested Contract Type for Requirement • All responses should include a suggested contract type (Firm fixed price, Labor Hours/Time & Materials, etc.), and rationale which will provide the most cost efficient and technically effective approach to the contract performance Submission requirements: 1. Page limit - Five (5) 8 ½ x 11 pages 2. 1 inch margins (top, bottom and sides) 3. Times New Roman font - 12 point 4. Page limitation does not include: 1 cover page, 1 letter of introduction page, 1 table of contents page) 5. Do not include promotional materials Vendors are requested to provide the name of Contact Person for their company, telephone number, and email and complete address with each submission of data and include 75N97019Q00005 -Advanced Natural Language Processing and Artificial Intelligence Platform on cover page. Please submit all information electronically to Em'Ria J. Briscoe at briscoee@mail.nih.gov by November 19, 2018 no later than 11:00 a.m. The National Institutes of Health is under no obligation to select any participating firms. NO TELEPHONE REQUEST WILL BE ACCEPTED. Disclaimer and Important Notes: This notice does not obligate the Government to award a contract or otherwise pay for the information provided in response. The Government reserves the right to use information provided by respondents for any purpose deemed necessary and legally appropriate. Any organization responding to this notice should ensure that its response is complete and sufficiently detailed to allow the Government to determine the organization's qualifications to perform the work. Respondents are advised that the Government is under no obligation to acknowledge receipt of the information received or provide feedback to respondents with respect to any information submitted. After a review of the responses received, a pre-solicitation synopsis and solicitation may be published in Federal Business Opportunities. However, responses to this notice will not be considered adequate responses to a solicitation. Confidentiality: No proprietary, classified, confidential, or sensitive information should be included in your response. The Government reserves the right to use any non-proprietary technical information in any resultant solicitation(s).
- Web Link
-
FBO.gov Permalink
(https://www.fbo.gov/spg/HHS/NIH/OAM/75N97019Q00005/listing.html)
- Place of Performance
- Address: 6705 Rockledge Drive, Bethesda, Maryland, 20817, United States
- Zip Code: 20817
- Zip Code: 20817
- Record
- SN05143562-W 20181107/181105230703-27c1bed12ed6268e4028fbe8e3965e72 (fbodaily.com)
- Source
-
FedBizOpps Link to This Notice
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