AIMC Topic: Humans

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Natural language processing for cognitive therapy: Extracting schemas from thought records.

PloS one
The cognitive approach to psychotherapy aims to change patients' maladaptive schemas, that is, overly negative views on themselves, the world, or the future. To obtain awareness of these views, they record their thought processes in situations that c...

A new framework based on features modeling and ensemble learning to predict query performance.

PloS one
A query optimizer attempts to predict a performance metric based on the amount of time elapsed. Theoretically, this would necessitate the creation of a significant overhead on the core engine to provide the necessary query optimizing statistics. Mach...

Weakly supervised multitask learning models to identify symptom onset time of unclear-onset intracerebral hemorrhage.

International journal of stroke : official journal of the International Stroke Society
BACKGROUND: Approximately one-third of spontaneous intracerebral hemorrhage patients did not know the onset time and were excluded from studies about time-dependent treatments for hyperacute spontaneous intracerebral hemorrhage.

Predicting obesity and smoking using medication data: A machine-learning approach.

Pharmacoepidemiology and drug safety
PURPOSE: Administrative health datasets are widely used in public health research but often lack information about common confounders. We aimed to develop and validate machine learning (ML)-based models using medication data from Australia's Pharmace...

Public Perception and Reception of Robotic Applications in Public Health Emergencies Based on a Questionnaire Survey Conducted during COVID-19.

International journal of environmental research and public health
Various intelligent technologies have been applied during COVID-19, which has become a worldwide public health emergency and brought significant challenges to the medical systems around the world. Notably, the application of robots has played a role ...

Random survival forests for dynamic predictions of a time-to-event outcome using a longitudinal biomarker.

BMC medical research methodology
BACKGROUND: Risk prediction models for time-to-event outcomes play a vital role in personalized decision-making. A patient's biomarker values, such as medical lab results, are often measured over time but traditional prediction models ignore their lo...

Prospective comparative study of postoperative systemic inflammatory syndrome in robot-assisted vs. open kidney transplantation.

World journal of urology
PURPOSE: Robot-assisted kidney transplant (RAKT) recently proved to provide functional results similar to the preferred open kidney transplant (OKT), but with inferior wound morbidity. In a comparative prospective study, we explored the systemic infl...

Classification of amyotrophic lateral sclerosis by brain volume, connectivity, and network dynamics.

Human brain mapping
Emerging studies corroborate the importance of neuroimaging biomarkers and machine learning to improve diagnostic classification of amyotrophic lateral sclerosis (ALS). While most studies focus on structural data, recent studies assessing functional ...

Investigating the impact of sewer overflow on the environment: A comprehensive literature review paper.

Journal of environmental management
Sewer networks play a pivotal role in our everyday lives by transporting the stormwater and urban sewage away from the urban areas. In this regard, Sewer Overflow (SO) has been considered as a detrimental threat to our environment and health, which r...

Early prediction of severe acute pancreatitis using machine learning.

Pancreatology : official journal of the International Association of Pancreatology (IAP) ... [et al.]
BACKGROUND: Acute pancreatitis (AP) is one of the most common causes of gastrointestinal-related hospitalizations in the United States. Severe AP (SAP) is associated with a mortality rate of nearly 30% and is distinguished from milder forms of AP. Ri...