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Literature DB >> 30556825

Deep Learning in Medicine-Promise, Progress, and Challenges.

Fei Wang¹, Lawrence Peter Casalino¹, Dhruv Khullar^1,2.

Abstract

Mesh：

Year: 2019 PMID： 30556825 DOI： 10.1001/jamainternmed.2018.7117

Source DB: PubMed Journal: JAMA Intern Med ISSN： 2168-6106 Impact factor: 21.873

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66 in total

1. Deep neural network analyses of spirometry for structural phenotyping of chronic obstructive pulmonary disease.

Authors: Sandeep Bodduluri; Arie Nakhmani; Joseph M Reinhardt; Carla G Wilson; Merry-Lynn McDonald; Ramaraju Rudraraju; Byron C Jaeger; Nirav R Bhakta; Peter J Castaldi; Frank C Sciurba; Chengcui Zhang; Purushotham V Bangalore; Surya P Bhatt
Journal: JCI Insight Date: 2020-07-09

2. Challenge of Directly Comparing Imaging-Based Diagnoses Made by Machine Learning Algorithms With Those Made by Human Clinicians.

Authors: Aaron B Simon; Lucas K Vitzthum; Loren K Mell
Journal: J Clin Oncol Date: 2020-04-09 Impact factor: 44.544

3. Can Big Data guide prognosis and clinical decisions in epilepsy?

Authors: Xiaojin Li; Licong Cui; Guo-Qiang Zhang; Samden D Lhatoo
Journal: Epilepsia Date: 2021-02-02 Impact factor: 5.864

4. Applying Artificial Intelligence to Predict Self-Reported Poor Health Among Black and Hispanic Caregivers with Mild Cognitive Impairment.

Authors: Sunmoo Yoon; Nicole Davis; Michelle Odlum; Hwayoung Cho; Peter Broadwell; Maria Patrao; Michael Bales; Carmela Alcantara; Mary Mittelman
Journal: Stud Health Technol Inform Date: 2020-06-26

Deep Learning in Medicine-Promise, Progress, and Challenges.

1. Deep neural network analyses of spirometry for structural phenotyping of chronic obstructive pulmonary disease.

2. Challenge of Directly Comparing Imaging-Based Diagnoses Made by Machine Learning Algorithms With Those Made by Human Clinicians.

3. Can Big Data guide prognosis and clinical decisions in epilepsy?

4. Applying Artificial Intelligence to Predict Self-Reported Poor Health Among Black and Hispanic Caregivers with Mild Cognitive Impairment.

5. SynTEG: a framework for temporal structured electronic health data simulation.

6. Predicting complications of diabetes mellitus using advanced machine learning algorithms.

Review 7. Recent advances in biomedical literature mining.

8. Identifying risk of opioid use disorder for patients taking opioid medications with deep learning.

9. Public vs physician views of liability for artificial intelligence in health care.

10. Importance-aware personalized learning for early risk prediction using static and dynamic health data.