# Presentation: 273878

## Applied Knowledge Management to NIH ProcessesA presentation to the eRA Third Annual Symposium

**Dr. Bob Lewis, PhD – **_**[bob.lewis@mitretek.org](mailto:bob.lewis@mitretek.org)**__** **_
**Dr. Clem McGowan, PhD – **_**[mcgowan@mitretek.org](mailto:mcgowan@mitretek.org)**__** **_
**Archna Bhandari – **_**[archna.bhandari@mitretek.org](mailto:archna.bhandari@mitretek.org)**__** **_

***30 April 2003***

## KM Demonstration Homepage

## Creating Research Proposal ProfilesBased on MeSH Thesaurus

**View Entire Research Plan**

**View Research Plan Fingerprint**

**View Entire Proposal**

**Search Proposals by Grant Number**

*NOTE: Fingerprint topics derived from MeSH Thesaurus*

## Locating Subject Matter ExpertsMultiple Sources

**Search for Experts based on MeSH Thesaurus terms**

**View Expert Profile**

## KM Advanced Capabilities

## Grant Reviewer SelectionLocate Qualified Reviewers for Research Proposals

**Identify Qualified Reviewers by comparing fingerprints**

**List possible Candidate Reviewers based on topics within Research Proposal**

## Identifying Similar Research ProposalsCurrent Proposed and Previously Funded Proposals

**Identify Similar Proposals by comparing fingerprints**

**List all Proposed and Funded Research Proposals containing similar research**

## Establishing Communities of InterestAdvanced Capabilities

**Enter topic area(s)**

**Identify similar Content & Experts based on fingerprints**

**Bring together Content and Expertise sharing common interests/topics**

**Perform Document-based Collaboration**

**Access additional Applications/Tools**

## Additional KM Advanced Capabilities

## Heat MapsData Visualization

**Proposals by Funding/Date across IRGs and Activity Types**

## Neighborhood MapsData Visualization

**Identifying amount/recency of funded topic areas related to Arthritis**

## Online Analytical ProcessingData Visualization

**“****Slice and Dice” data to identify patterns and trends**

## Proposal Receipt and ReferralMachine Learning/Pattern Recognition

## Discovering Relationships from TextRelationship Identification

## Locating and Accessing ContentAutomatic Categorization

**Taxonomy Drill Down Path:**
- _Root Category > NIH  > Diseases > Musculoskeletal Diseases_

**Taxonomy**

**Content**

## KM Technical Presentation

## What is Knowledge Management?

| Function | Core Element |
| --- | --- |
| Capture and share the experience, expertise and insight of people | Expertise Location |
| Allow members with similar interests to work together regardless of location | Collaboration |
| Analyze and organize information in a meaningful manner | Content Structuring &
Categorization |
| Tying it all together | Integration & Portals |

- KM is the *process* of _identifying_, _capturing_, _organizing_, and leveraging knowledge assets within an organization to improve performance and efficiency.

## Grant Reviewer Selection (GRS)Overview

**Technology: Profiling (Fingerprinting) **
- Applies to any text  (an example of “text mining”)
- Grant proposals
- Descriptions of funded research projects
- Technical papers and CVs of researchers
- Assigns a profile (a “fingerprint”)
- Profile is a vector in the MeSH “Taxonomy Space”
- Also associate “meta information” such as name, institution, etc.
- Retrieves the best profile matches (using proximity)
- Also display the meta-information (person, grant) of the profiles
**Proposed NIH Usage**
- Referral of grant proposals to appropriate reviewers
- Particularly when there is not a standing set of reviewers
- Search over a large set of possible reviewers to find best matches
**Potential Benefits**
- Improved quality and consistency of referrals
- Speed-up in processing

## Grant Reviewer Selection (GRS)Process Flow Diagram

**Grant Reviewer Selection (GRS)*****Process Flow Diagram***

**Fingerprints of**
**Applications**

**Research **
**Plans**

**Meta **
**Information**

**Reviewers’**
**Bio-Sketches ***

**List of Candidate Reviewers**

**Text Mining**

**Text Mining**

**MeSH**
**Thesaurus**

**Fingerprints of**
**Reviewers**

***Used CRISP for the prototype**

**Match & Retrieval**

**M**

**M**

**M**

## Relationship Identification & CategorizationProcess Flow Diagram

**Relationship Identification & Categorization*****Process Flow Diagram***

***Content Structuring***

**Research**
**Plans**

**Text Mining**

**Concepts**

**Categorization**

**Relationship Identification**

**MeSH**
**Thesaurus**

***Visualization Map of Related Concepts***

## Data VisualizationProcess Flow Diagram

**Data Visualization*****Process Flow Diagram***

**Funding**
**Data**

***“******Slice and Dice”***
***to see data patterns***

**CRISP**

**Information Extraction**

**Relational**
**Database**

**Data Analysis**

**M**

**M**

**M**

**M**

***Visualization***
***to see data patterns***

## Proposal Receipt and ReferralProcess Flow Diagram

**Proposal Receipt and Referral*****Process Flow Diagram***

**.**
**.**
**.**

**IC**
**Training Set**

**Classifier**

**Institutes and Centers (IC)**

- NCI

- NIAMS

- NIGMS

- NHLBI

- NIAID

**NCI**

**NIAMS**

**NIGMS**

**NIAID**

**NHLBI**

***Unclassified***
***Research Proposals***

**Classified Proposals**

**M**

**M**

## Express-Lining ProposalsProcess Flow Diagram

**Express-Lining Proposals*****Process Flow Diagram***

**Queue of Applications to be Reviewed**

**Classification Engine**

**Training Set of Previously funded grants**

*Unranked*
*Research Proposals*

- High Funding
- Probability

- Low Funding
- Probability

**Pattern of **
**Successful grants**