AIMC Topic: Machine Learning

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Accuracy and transportability of machine learning models for adolescent suicide prediction with longitudinal clinical records.

Translational psychiatry
Machine Learning models trained from real-world data have demonstrated promise in predicting suicide attempts in adolescents. However, their transportability, namely the performance of a model trained on one dataset and applied to different data, is ...

Advancements in prognostic markers and predictive models for intracerebral hemorrhage: from serum biomarkers to artificial intelligence models.

Neurosurgical review
Intracerebral hemorrhage (ICH) is a severe form of stroke with high morbidity and mortality, accounting for 10-15% of all strokes globally. Recent advancements in prognostic biomarkers and predictive models have shown promise in enhancing the predict...

Deep mutational scanning and machine learning for the analysis of antimicrobial-peptide features driving membrane selectivity.

Nature biomedical engineering
Many antimicrobial peptides directly disrupt bacterial membranes yet can also damage mammalian membranes. It is therefore central to their therapeutic use that rules governing the membrane selectivity of antimicrobial peptides be deciphered. However,...

Multitask learning of a biophysically-detailed neuron model.

PLoS computational biology
The human brain operates at multiple levels, from molecules to circuits, and understanding these complex processes requires integrated research efforts. Simulating biophysically-detailed neuron models is a computationally expensive but effective meth...

The changing landscape of text mining: a review of approaches for ecology and evolution.

Proceedings. Biological sciences
In ecology and evolutionary biology, the synthesis and modelling of data from published literature are commonly used to generate insights and test theories across systems. However, the tasks of searching, screening, and extracting data from literatur...

Artificial intelligence in the detection and treatment of depressive disorders: a narrative review of literature.

International review of psychiatry (Abingdon, England)
Modern psychiatry aims to adopt precision models and promote personalized treatment within mental health care. However, the complexity of factors underpinning mental disorders and the variety of expressions of clinical conditions make this task arduo...

Machine learning-derived prognostic signature for progression-free survival in non-metastatic nasopharyngeal carcinoma.

Head & neck
BACKGROUND: Early detection of high-risk nasopharyngeal carcinoma (NPC) recurrence is essential. We created a machine learning-derived prognostic signature (MLDPS) by combining three machine learning (ML) models to predict progression-free survival (...

Artificial intelligence to predict pre-clinical dental student academic performance based on pre-university results: A preliminary study.

Journal of dental education
PURPOSE/OBJECTIVES: Admission into dental school involves selecting applicants for successful completion of the course. This study aimed to predict the academic performance of Kulliyyah of Dentistry, International Islamic University Malaysia pre-clin...

Predicting Tracheostomy Need on Admission to the Intensive Care Unit-A Multicenter Machine Learning Analysis.

Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery
OBJECTIVE: It is difficult to predict which mechanically ventilated patients will ultimately require a tracheostomy which further predisposes them to unnecessary spontaneous breathing trials, additional time on the ventilator, increased costs, and fu...

Pollution loads in the middle-lower Yangtze river by coupling water quality models with machine learning.

Water research
Pollution control and environmental protection of the Yangtze River have received major attention in China. However, modeling the river's pollution load remains challenging due to limited monitoring and unclear spatiotemporal distribution of pollutio...