AIMC Topic: Machine Learning

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Artificial intelligence and machine learning approaches for drug design: challenges and opportunities for the pharmaceutical industries.

Molecular diversity
The global spread of COVID-19 has raised the importance of pharmaceutical drug development as intractable and hot research. Developing new drug molecules to overcome any disease is a costly and lengthy process, but the process continues uninterrupted...

Manipulating cellular microRNAs and analyzing high-dimensional gene expression data using machine learning workflows.

STAR protocols
MicroRNAs (miRNAs) are elements of the gene regulatory network and manipulating their abundance is essential toward elucidating their role in patho-physiological conditions. We present a detailed workflow that identifies important miRNAs using a mach...

Improving the performance of machine learning models for biotechnology: The quest for deus ex machina.

Biotechnology advances
Machine learning is becoming an integral part of the Design-Build-Test-Learn cycle in biotechnology. Machine learning models learn from collected datasets such as omics data and predict a defined outcome, which has led to both production improvements...

The Potential Cost-Effectiveness of a Machine Learning Tool That Can Prevent Untimely Intensive Care Unit Discharge.

Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research
OBJECTIVES: The machine learning prediction model Pacmed Critical (PC), currently under development, may guide intensivists in their decision-making process on the most appropriate time to discharge a patient from the intensive care unit (ICU). Given...

Improved Accuracy in Predicting the Best Sensor Fusion Architecture for Multiple Domains.

Sensors (Basel, Switzerland)
Multi-sensor fusion intends to boost the general reliability of a decision-making procedure or allow one sensor to compensate for others' shortcomings. This field has been so prominent that authors have proposed many different fusion approaches, or "...

Augmenting BDI Agency with a Cognitive Service: Architecture and Validation in Healthcare Domain.

Journal of medical systems
Autonomous intelligent systems are starting to influence clinical practice, as ways to both readily exploit experts' knowledge when contextual conditions demand so, and harness the overwhelming amount of patient related data currently at clinicians' ...

Applying artificial neural network for early detection of sepsis with intentionally preserved highly missing real-world data for simulating clinical situation.

BMC medical informatics and decision making
PURPOSE: Some predictive systems using machine learning models have been developed to predict sepsis; however, they were mostly built with a low percent of missing values, which does not correspond with the actual clinical situation. In this study, w...

The IDeaS initiative: pilot study to assess the impact of rare diseases on patients and healthcare systems.

Orphanet journal of rare diseases
BACKGROUND: Rare diseases (RD) are a diverse collection of more than 7-10,000 different disorders, most of which affect a small number of people per disease. Because of their rarity and fragmentation of patients across thousands of different disorder...

Smart Cardiac Framework for an Early Detection of Cardiac Arrest Condition and Risk.

Frontiers in public health
Cardiovascular disease (CVD) is considered to be one of the most epidemic diseases in the world today. Predicting CVDs, such as cardiac arrest, is a difficult task in the area of healthcare. The healthcare industry has a vast collection of datasets f...

Efficient Prediction of Missed Clinical Appointment Using Machine Learning.

Computational and mathematical methods in medicine
Public health and its related facilities are crucial for thriving cities and societies. The optimum utilization of health resources saves money and time, but above all, it saves precious lives. It has become even more evident in the present as the pa...