Machine learning to predict pregnancy outcomes: a systematic review, synthesizing framework and future research agenda

MN Islam, SN Mustafina, T Mahmud… - BMC pregnancy and …, 2022 - Springer
Abstract Machine Learning (ML) has been widely used in predicting the mode of childbirth
and assessing the potential maternal risks during pregnancy. The primary aim of this review …

Re-focusing explainability in medicine

L Arbelaez Ossa, G Starke, G Lorenzini… - Digital …, 2022 - journals.sagepub.com
Using artificial intelligence to improve patient care is a cutting-edge methodology, but its
implementation in clinical routine has been limited due to significant concerns about …

A machine-learning-based prediction method for hypertension outcomes based on medical data

W Chang, Y Liu, Y Xiao, X Yuan, X Xu, S Zhang… - Diagnostics, 2019 - mdpi.com
The outcomes of hypertension refer to the death or serious complications (such as
myocardial infarction or stroke) that may occur in patients with hypertension. The outcomes …

T2g-former: organizing tabular features into relation graphs promotes heterogeneous feature interaction

J Yan, J Chen, Y Wu, DZ Chen, J Wu - Proceedings of the AAAI …, 2023 - ojs.aaai.org
Recent development of deep neural networks (DNNs) for tabular learning has largely
benefited from the capability of DNNs for automatic feature interaction. However, the …

Artificial intelligence in the IVF laboratory: overview through the application of different types of algorithms for the classification of reproductive data

EI Fernandez, AS Ferreira, MHM Cecílio… - Journal of assisted …, 2020 - Springer
Over the past years, the assisted reproductive technologies (ARTs) have been accompanied
by constant innovations. For instance, intracytoplasmic sperm injection (ICSI), time-lapse …

Machine learning predicts live-birth occurrence before in-vitro fertilization treatment

A Goyal, M Kuchana, KPR Ayyagari - Scientific reports, 2020 - nature.com
In-vitro fertilization (IVF) is a popular method of resolving complications such as
endometriosis, poor egg quality, a genetic disease of mother or father, problems with …

Danets: Deep abstract networks for tabular data classification and regression

J Chen, K Liao, Y Wan, DZ Chen, J Wu - Proceedings of the AAAI …, 2022 - ojs.aaai.org
Tabular data are ubiquitous in real world applications. Although many commonly-used
neural components (eg, convolution) and extensible neural networks (eg, ResNet) have …

Development and validation of a deep learning model for predicting treatment response in patients with newly diagnosed epilepsy

H Hakeem, W Feng, Z Chen, J Choong… - JAMA …, 2022 - jamanetwork.com
Importance Selection of antiseizure medications (ASMs) for epilepsy remains largely a trial-
and-error approach. Under this approach, many patients have to endure sequential trials of …

Making pre-trained language models great on tabular prediction

J Yan, B Zheng, H Xu, Y Zhu, D Chen, J Sun… - arXiv preprint arXiv …, 2024 - arxiv.org
The transferability of deep neural networks (DNNs) has made significant progress in image
and language processing. However, due to the heterogeneity among tables, such DNN …

Tabcaps: A capsule neural network for tabular data classification with bow routing

J Chen, KL Liao, Y Fang, D Chen… - The Eleventh International …, 2023 - openreview.net
Records in a table are represented by a collection of heterogeneous scalar features.
Previous work often made predictions for records in a paradigm that processed each feature …