Quantifying impairment and disease severity using AI models trained on healthy subjects.
| Published in: | NPJ Digital Medicine Vol. 7; no. 1; pp. 1 - 12 |
|---|---|
| Main Authors: | , , , , , , , , , |
| Format: | equations & formulas pictorial research tables/charts Journal Article |
| Published: |
Springer Nature
7/6/2024
|
| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=178293287&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 178293287 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23986352 LQV4 jtl: NPJ Digital Medicine issn: 23986352 maglogo: N pubinfo: dt: 7/6/2024 vid: 7 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 178293287 178293287 178293287 10.1038/s41746-024-01173-x 178293287 ppf: 1 ppct: 11 formats: tig: atl: Quantifying impairment and disease severity using AI models trained on healthy subjects. aug: au: Yu, Boyang Kaku, Aakash Liu, Kangning Parnandi, Avinash Fokas, Emily Venkatesan, Anita Pandit, Natasha Ranganath, Rajesh Schambra, Heidi Fernandez-Granda, Carlos affil: https://ror.org/0190ak572 Center for Data Science, New York University, 60 Fifth Ave, 10011, New York, NY, USA sug: subj: Severity of Disability Artificial Intelligence Utilization Prediction Models Education Research Subjects Human Male Female Adult Middle Age Aged Stroke Patients Wearable Sensors Protocols Descriptive Statistics Confidence Intervals Osteoarthritis, Knee Magnetic Resonance Imaging Pearson's Correlation Coefficient Clinical Assessment Tools Funding Source Adult: 19-44 years Middle Aged: 45-64 years Aged: 65+ years Male Female pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts Journal Article ougenre: Article ab: language: English refInfo: holdings: @attributes: islocal: N |
|---|