Potential value and impact of data mining and machine learning in clinical diagnostics
M Saberi-Karimian, Z Khorasanchi… - Critical reviews in …, 2021 - Taylor & Francis
Data mining involves the use of mathematical sciences, statistics, artificial intelligence, and
machine learning to determine the relationships between variables from a large sample of …
machine learning to determine the relationships between variables from a large sample of …
[HTML][HTML] Applied machine learning in cancer research: A systematic review for patient diagnosis, classification and prognosis
Artificial Intelligence (AI) has recently altered the landscape of cancer research and medical
oncology using traditional Machine Learning (ML) algorithms and cutting-edge Deep …
oncology using traditional Machine Learning (ML) algorithms and cutting-edge Deep …
Clinical and inflammatory features based machine learning model for fatal risk prediction of hospitalized COVID-19 patients: results from a retrospective cohort study
X Guan, B Zhang, M Fu, M Li, X Yuan, Y Zhu… - Annals of …, 2021 - Taylor & Francis
Objectives To appraise effective predictors for COVID-19 mortality in a retrospective cohort
study. Methods A total of 1270 COVID-19 patients, including 984 admitted in Sino French …
study. Methods A total of 1270 COVID-19 patients, including 984 admitted in Sino French …
Multimodal deep learning models for the prediction of pathologic response to neoadjuvant chemotherapy in breast cancer
The achievement of the pathologic complete response (pCR) has been considered a metric
for the success of neoadjuvant chemotherapy (NAC) and a powerful surrogate indicator of …
for the success of neoadjuvant chemotherapy (NAC) and a powerful surrogate indicator of …
Assessment and prediction of response to neoadjuvant chemotherapy in breast cancer: A comparison of imaging modalities and future perspectives
Simple Summary Nowadays patients affected by locally advanced breast cancer and
particular subtypes of early breast cancer may benefit from neoadjuvant chemotherapy …
particular subtypes of early breast cancer may benefit from neoadjuvant chemotherapy …
Artificial intelligence in medical imaging of the breast
YM Lei, M Yin, MH Yu, J Yu, SE Zeng, WZ Lv… - Frontiers in …, 2021 - frontiersin.org
Artificial intelligence (AI) has invaded our daily lives, and in the last decade, there have been
very promising applications of AI in the field of medicine, including medical imaging, in vitro …
very promising applications of AI in the field of medicine, including medical imaging, in vitro …
AI-enhanced breast imaging: Where are we and where are we heading?
A Bitencourt, ID Naranjo, RL Gullo… - European journal of …, 2021 - Elsevier
Significant advances in imaging analysis and the development of high-throughput methods
that can extract and correlate multiple imaging parameters with different clinical outcomes …
that can extract and correlate multiple imaging parameters with different clinical outcomes …
Breast MRI for evaluation of response to neoadjuvant therapy
B Reig, AA Lewin, L Du, L Heacock, HK Toth… - Radiographics, 2021 - pubs.rsna.org
Neoadjuvant therapy is increasingly being used to treat early-stage triple-negative and
human epidermal growth factor 2–overexpressing breast cancers, as well as locally …
human epidermal growth factor 2–overexpressing breast cancers, as well as locally …
Early prediction of neoadjuvant chemotherapy response by exploiting a transfer learning approach on breast DCE-MRIs
The dynamic contrast-enhanced MR imaging plays a crucial role in evaluating the
effectiveness of neoadjuvant chemotherapy (NAC) even since its early stage through the …
effectiveness of neoadjuvant chemotherapy (NAC) even since its early stage through the …
Multivariate machine learning analyses in identification of major depressive disorder using resting-state functional connectivity: A multicentral study
Diagnosis of major depressive disorder (MDD) using resting-state functional connectivity (rs-
FC) data faces many challenges, such as the high dimensionality, small samples, and …
FC) data faces many challenges, such as the high dimensionality, small samples, and …