scispace - formally typeset
M

Marzieh Esmaeili

Researcher at Tehran University of Medical Sciences

Publications -  11
Citations -  426

Marzieh Esmaeili is an academic researcher from Tehran University of Medical Sciences. The author has contributed to research in topics: Computer science & Mammography. The author has an hindex of 3, co-authored 7 publications receiving 95 citations.

Papers
More filters
Journal ArticleDOI

The mental health of healthcare workers in the COVID-19 pandemic: A systematic review.

TL;DR: All research carried out on the mental health status of health care workers (HCWs) to bring policymakers and managers’ attention is reviewed to recommend the supportive, encouragement & motivational, protective, and training & educational interventions.
Journal ArticleDOI

Characteristics of Children With Kawasaki Disease-Like Signs in COVID-19 Pandemic: A Systematic Review

TL;DR: In this paper, a comprehensive search was carried out systematically through PubMed, Scopus, and Web of Science (WoS), medRxiv, and bioRXiv by two reviewers independently for all studies or preprints data on the demographic, laboratory, and clinical characteristics of children with K.D-like signs during the COVID-19 outbreak.
Journal ArticleDOI

Surgical Patients Follow-Up by Smartphone-Based Applications: A Systematic Literature Review.

TL;DR: It was showed that mHealth-based interventions have potential that may support better management of post-discharge systematic follow-up of surgery patients.
Journal ArticleDOI

Supporting colorectal cancer survivors using eHealth: a systematic review and framework suggestion.

TL;DR: In this article, the authors conducted a systematic review of eHealth systems for colorectal cancer survivors in the past two decades, and the search was limited to 20 years (from 19 January 1999 to 19 January 2019) and the results were tabulated and represented as a framework.
Journal ArticleDOI

Prediction of Breast Cancer using Machine Learning Approaches

TL;DR: Combining multiple risk factors in modeling for breast cancer prediction could help the early diagnosis of the disease with necessary care plans and also help the management of different data and intelligent systems based on multiple factors for predicting breast cancer are effective in disease management.