AIMC Topic: Time Factors

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A machine learning algorithm can optimize the day of trigger to improve in vitro fertilization outcomes.

Fertility and sterility
OBJECTIVE: To determine whether a machine learning causal inference model can optimize trigger injection timing to maximize the yield of fertilized oocytes (2PNs) and total usable blastocysts for a given cohort of stimulated follicles.

Dynamical and static multisynchronization analysis for coupled multistable memristive neural networks with hybrid control.

Neural networks : the official journal of the International Neural Network Society
This paper investigates the dynamical multisynchronization (DMS) and static multisynchronization (SMS) problems for a class of delayed coupled multistable memristive neural networks (DCMMNNs) via a novel hybrid controller which includes delayed impul...

A clinical deep learning framework for continually learning from cardiac signals across diseases, time, modalities, and institutions.

Nature communications
Deep learning algorithms trained on instances that violate the assumption of being independent and identically distributed (i.i.d.) are known to experience destructive interference, a phenomenon characterized by a degradation in performance. Such a v...

Predefined-time synchronization of competitive neural networks.

Neural networks : the official journal of the International Neural Network Society
In this paper, the predefined-time synchronization of competitive neural networks (CNNs) is researched based on two different predefined-time stability theorems. In view of the bilayer structure of CNNs, we design two bilayer predefined-time controll...

Timesias: A machine learning pipeline for predicting outcomes from time-series clinical records.

STAR protocols
The prediction of outcomes is a critical part of the clinical surveillance for hospitalized patients. Here, we present Timesias, a machine learning pipeline which predicts outcomes from real-time sequential clinical data. The strategy implemented in ...

Development and Validation of an Artificial Intelligence System to Optimize Clinician Review of Patient Records.

JAMA network open
IMPORTANCE: Physicians are required to work with rapidly growing amounts of medical data. Approximately 62% of time per patient is devoted to reviewing electronic health records (EHRs), with clinical data review being the most time-consuming portion.

Equivalent-input-disturbance estimator-based event-triggered control design for master-slave neural networks.

Neural networks : the official journal of the International Neural Network Society
This paper investigates the robust synchronization problem for a class of master-slave neural networks (MSNNs) subject to network-induced delays, unknown time-varying uncertainty, and exogenous disturbances. An equivalent-input-disturbance (EID) esti...

Deep learning to predict long-term mortality in patients requiring 7 days of mechanical ventilation.

PloS one
BACKGROUND: Among patients with acute respiratory failure requiring prolonged mechanical ventilation, tracheostomies are typically placed after approximately 7 to 10 days. Yet half of patients admitted to the intensive care unit receiving tracheostom...

Machine learning enhances the performance of short and long-term mortality prediction model in non-ST-segment elevation myocardial infarction.

Scientific reports
Machine learning (ML) has been suggested to improve the performance of prediction models. Nevertheless, research on predicting the risk in patients with acute myocardial infarction (AMI) has been limited and showed inconsistency in the performance of...