AIMC Topic: Algorithms

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Adaptive contrast weighted learning for multi-stage multi-treatment decision-making.

Biometrics
Dynamic treatment regimes (DTRs) are sequential decision rules that focus simultaneously on treatment individualization and adaptation over time. To directly identify the optimal DTR in a multi-stage multi-treatment setting, we propose a dynamic stat...

SWIFT-Review: a text-mining workbench for systematic review.

Systematic reviews
BACKGROUND: There is growing interest in using machine learning approaches to priority rank studies and reduce human burden in screening literature when conducting systematic reviews. In addition, identifying addressable questions during the problem ...

Robust Machine Learning-Based Correction on Automatic Segmentation of the Cerebellum and Brainstem.

PloS one
Automated segmentation is a useful method for studying large brain structures such as the cerebellum and brainstem. However, automated segmentation may lead to inaccuracy and/or undesirable boundary. The goal of the present study was to investigate w...

Lung sound classification using cepstral-based statistical features.

Computers in biology and medicine
Lung sounds convey useful information related to pulmonary pathology. In this paper, short-term spectral characteristics of lung sounds are studied to characterize the lung sounds for the identification of associated diseases. Motivated by the succes...

An Imbalanced Learning based MDR-TB Early Warning System.

Journal of medical systems
As a man-made disease, multidrug-resistant tuberculosis (MDR-TB) is mainly caused by improper treatment programs and poor patient supervision, most of which could be prevented. According to the daily treatment and inspection records of tuberculosis (...

Improved Prediction of Drug-Induced Torsades de Pointes Through Simulations of Dynamics and Machine Learning Algorithms.

Clinical pharmacology and therapeutics
The ventricular arrhythmia Torsades de Pointes (TdP) is a common form of drug-induced cardiotoxicity, but prediction of this arrhythmia remains an unresolved issue in drug development. Current assays to evaluate arrhythmia risk are limited by poor sp...

A kernel-based clustering method for gene selection with gene expression data.

Journal of biomedical informatics
Gene selection is important for cancer classification based on gene expression data, because of high dimensionality and small sample size. In this paper, we present a new gene selection method based on clustering, in which dissimilarity measures are ...

Representing higher-order dependencies in networks.

Science advances
To ensure the correctness of network analysis methods, the network (as the input) has to be a sufficiently accurate representation of the underlying data. However, when representing sequential data from complex systems, such as global shipping traffi...

Link-Prediction Enhanced Consensus Clustering for Complex Networks.

PloS one
Many real networks that are collected or inferred from data are incomplete due to missing edges. Missing edges can be inherent to the dataset (Facebook friend links will never be complete) or the result of sampling (one may only have access to a port...

A novel curvature-controllable steerable needle for percutaneous intervention.

Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine
Over the last few decades, flexible steerable robotic needles for percutaneous intervention have been the subject of significant interest. However, there still remain issues related to (a) steering the needle's direction with less damage to surroundi...