AIMC Topic: Models, Statistical

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A recurrent neural network framework for flexible and adaptive decision making based on sequence learning.

PLoS computational biology
The brain makes flexible and adaptive responses in a complicated and ever-changing environment for an organism's survival. To achieve this, the brain needs to understand the contingencies between its sensory inputs, actions, and rewards. This is anal...

Network inference from population-level observation of epidemics.

Scientific reports
Using the continuous-time susceptible-infected-susceptible (SIS) model on networks, we investigate the problem of inferring the class of the underlying network when epidemic data is only available at population-level (i.e., the number of infected ind...

Machine learning prediction for mortality of patients diagnosed with COVID-19: a nationwide Korean cohort study.

Scientific reports
The rapid spread of COVID-19 has resulted in the shortage of medical resources, which necessitates accurate prognosis prediction to triage patients effectively. This study used the nationwide cohort of South Korea to develop a machine learning model ...

Natural language processing with machine learning to predict outcomes after ovarian cancer surgery.

Gynecologic oncology
OBJECTIVE: To determine if natural language processing (NLP) with machine learning of unstructured full text documents (a preoperative CT scan) improves the ability to predict postoperative complication and hospital readmission among women with ovari...

Learning molecular dynamics with simple language model built upon long short-term memory neural network.

Nature communications
Recurrent neural networks have led to breakthroughs in natural language processing and speech recognition. Here we show that recurrent networks, specifically long short-term memory networks can also capture the temporal evolution of chemical/biophysi...

Machine learning-based QSAR models to predict sodium ion channel (Na 1.5) blockers.

Future medicinal chemistry
Conventional experimental approaches used for the evaluation of the proarrhythmic potential of compounds in the drug discovery process are expensive and time consuming but an integral element in the safety profile required for a new drug to be appro...

Estimating Nonfatal Gunshot Injury Locations With Natural Language Processing and Machine Learning Models.

JAMA network open
IMPORTANCE: Nonfatal gunshot injuries are the most common firearm injury, but where they frequently occur remains unclear owing to data limitations. Natural language processing can be applied to medical text narratives of gunshot injury records to cl...

Advancing predictive modeling in archaeology: An evaluation of regression and machine learning methods on the Grand Staircase-Escalante National Monument.

PloS one
Predictive models are central to both archaeological research and cultural resource management. Yet, archaeological applications of predictive models are often insufficient due to small training data sets, inadequate statistical techniques, and a lac...

Biological batch normalisation: How intrinsic plasticity improves learning in deep neural networks.

PloS one
In this work, we present a local intrinsic rule that we developed, dubbed IP, inspired by the Infomax rule. Like Infomax, this rule works by controlling the gain and bias of a neuron to regulate its rate of fire. We discuss the biological plausibilit...

Alcoholic liver disease: A registry view on comorbidities and disease prediction.

PLoS computational biology
Alcoholic-related liver disease (ALD) is the cause of more than half of all liver-related deaths. Sustained excess drinking causes fatty liver and alcohol-related steatohepatitis, which may progress to alcoholic liver fibrosis (ALF) and eventually to...