Morteza Noshad

Morteza Noshad

Stanford University

H-index: 10

North America-United States

About Morteza Noshad

Morteza Noshad, With an exceptional h-index of 10 and a recent h-index of 10 (since 2020), a distinguished researcher at Stanford University, specializes in the field of Machine Learning, Recommender Systems, Natural Language Processing.

His recent articles reflect a diverse array of research interests and contributions to the field:

KartalOl: a new deep neural network framework based on transfer learning for iris segmentation and localization task—new dataset for iris segmentation

Machine learning prediction of mild cognitive impairment and its progression to Alzheimer's disease

Graph-based clinical recommender: Predicting specialists procedure orders using graph representation learning

Mild Cognitive Impairment: Data-Driven Prediction, Risk Factors, and Workup

Team is brain: leveraging EHR audit log data for new insights into acute care processes

Personalized antibiograms for machine learning driven antibiotic selection

Signal from the noise: a mixed graphical and quantitative process mining approach to evaluate care pathways applied to emergency stroke care

A data value metric for quantifying information content and utility

Morteza Noshad Information

University

Position

Senior Research Scientist

Citations(all)

408

Citations(since 2020)

309

Cited By

202

hIndex(all)

10

hIndex(since 2020)

10

i10Index(all)

11

i10Index(since 2020)

11

Email

University Profile Page

Google Scholar

Morteza Noshad Skills & Research Interests

Machine Learning

Recommender Systems

Natural Language Processing

Top articles of Morteza Noshad

KartalOl: a new deep neural network framework based on transfer learning for iris segmentation and localization task—new dataset for iris segmentation

Iran Journal of Computer Science

2023/12

Machine learning prediction of mild cognitive impairment and its progression to Alzheimer's disease

Health Science Reports

2023/10

Graph-based clinical recommender: Predicting specialists procedure orders using graph representation learning

Journal of Biomedical Informatics

2023/7/1

Mild Cognitive Impairment: Data-Driven Prediction, Risk Factors, and Workup

AMIA Summits on Translational Science Proceedings

2023

Team is brain: leveraging EHR audit log data for new insights into acute care processes

Journal of the American Medical Informatics Association

2023/1/1

Personalized antibiograms for machine learning driven antibiotic selection

Communications medicine

2022/4/8

Signal from the noise: a mixed graphical and quantitative process mining approach to evaluate care pathways applied to emergency stroke care

Journal of Biomedical Informatics

2022/3/1

Morteza Noshad
Morteza Noshad

H-Index: 7

Jonathan H Chen
Jonathan H Chen

H-Index: 19

A data value metric for quantifying information content and utility

Journal of big Data

2021/6/5

Morteza Noshad
Morteza Noshad

H-Index: 7

Machine learning predictability of clinical next generation sequencing for hematologic malignancies to guide high-value precision medicine

AMIA annual symposium proceedings

2021

Clinical recommender algorithms to simulate digital specialty consultations

2021

Morteza Noshad
Morteza Noshad

H-Index: 7

Jonathan H Chen
Jonathan H Chen

H-Index: 19

KartalOl: Transfer learning using deep neural network for iris segmentation and localization: New dataset for iris segmentation

arXiv preprint arXiv:2112.05236

2021/12/9

Signal from the Noise: A Mixed Methods Process Mining Approach to Evaluate Care Pathways

medRxiv

2021/11/8

Morteza Noshad
Morteza Noshad

H-Index: 7

Jonathan H Chen
Jonathan H Chen

H-Index: 19

Clinical recommender system: Predicting medical specialty diagnostic choices with neural network ensembles

arXiv preprint arXiv:2007.12161

2020/7/23

Morteza Noshad
Morteza Noshad

H-Index: 7

Jonathan H Chen
Jonathan H Chen

H-Index: 19

Context is Key: Using the Audit Log to Capture Contextual Factors Affecting Stroke Care Processes.

AMIA Annual Symposium Proceedings

2020

See List of Professors in Morteza Noshad University(Stanford University)

Co-Authors

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