🏆 Finalist — NIH Data Sharing Index (“S-Index”) Challenge

Pascal Brandt

Northwestern University

ORCID: 0000-0001-5116-0555

Also affiliated with University of Washington, Cornell University, Mayo Clinic in Florida, Temple University

Computer ScienceBiochemistry, Genetics and Molecular BiologyHealth ProfessionsMedicinePsychology

Pilot corpus only

This score is computed over theSindex pilot corpus and does not cover the full scientific literature. Scores are relative to papers we have ingested — papers, authors, and institutions outside the pilot are not represented. Methodology.

Top 4%percentile
0.747Author DataRank

Indexed papers

1in pilot corpus
datarank_citation_only_1hop_v6· scope data_onlyMethodology
Why this DataRank?

An author's DataRank is the sum of the DataRanks of all 1 indexed paper attributed to them. A prolific author with many moderate-impact papers can outrank one with a single high-impact paper.

Author scores recompute whenever paper DataRanks are refreshed, so this number lags the underlying paper scores by at most one batch run.

Read the full methodology →

Top data-sharing exemplar

The highest-impact dataset this researcher has shared, ranked by DataRank — the single contribution doing the most to lift their data-sharing standing.

Development of a repository of computable phenotype definitions using the clinical quality language

JAMIA Open(2021)10.1093/jamiaopen/ooab094
Top 27%
0.747DataRank
Top 27%12 citations

Jennifer A. Pacheco, Luke V. Rasmussen, Pascal Brandt

Papers

Driven by 7 papers — median percentile 73. Top paper: “Development of a repository of computable phenotype definitions using the clinical quality language”.

Development of a repository of computable phenotype definitions using the clinical quality language

JAMIA Open(2021)10.1093/jamiaopen/ooab094
Top 27%
0.747DataRank
Top 27%12 citations

Jennifer A. Pacheco, Luke V. Rasmussen, Pascal Brandt

Using Machine Learning to Predict Antidepressant Treatment Outcome From Electronic Health Records

Psychiatric Research and Clinical Practice(2023)10.1176/appi.prcp.20220015
N/A
0.725DataRank · unranked
17 citations

Veer Vekaria, Fei Wang, Judith Cukor, Chang Su, Prakash Adekkanattu +10 more

Toward cross‐platform electronic health record‐driven phenotyping using Clinical Quality Language

Learning Health Systems(2020)10.1002/lrh2.10233
N/A
0DataRank · unranked
24 citations

Richard C. Kiefer, Jennifer A. Pacheco, Prakash Adekkanattu, Evan Sholle, Faraz S. Ahmad +9 more

Prediction of left ventricular ejection fraction changes in heart failure patients using machine learning and electronic health records: a multi-site study

Scientific Reports(2023)10.1038/s41598-023-27493-8
N/A
0DataRank · unranked
17 citations

Luke V. Rasmussen, Jennifer A. Pacheco, Joseph Kabariti, Daniel J. Stone, Yue Yu +13 more

Comparing the effects of four common drug classes on the progression of mild cognitive impairment to dementia using electronic health records

Scientific Reports(2023)10.1038/s41598-023-35258-6
N/A
0DataRank · unranked
11 citations

Fei Wang, Chengxi Zang, Hao Zhang, Kellyann Niotis, Ava L. Liberman +14 more

Design and validation of a FHIR-based EHR-driven phenotyping toolbox

Journal of the American Medical Informatics Association(2022)10.1093/jamia/ocac063
N/A
0DataRank · unranked
12 citations

Jennifer A. Pacheco, Prakash Adekkanattu, Evan Sholle, Sajjad Abedian, Daniel J. Stone +11 more

Subphenotyping depression using machine learning and electronic health records

Learning Health Systems(2020)10.1002/lrh2.10241
N/A
0DataRank · unranked
30 citations

Fei Wang, Prakash Adekkanattu, Budhaditya Bose, Veer Vekaria, Pascal Brandt +9 more