AIMC Topic: Machine Learning

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A Topic-modeling Based Framework for Drug-drug Interaction Classification from Biomedical Text.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Classification of drug-drug interaction (DDI) from medical literatures is significant in preventing medication-related errors. Most of the existing machine learning approaches are based on supervised learning methods. However, the dynamic nature of d...

Interpretable Deep Models for ICU Outcome Prediction.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Exponential surge in health care data, such as longitudinal data from electronic health records (EHR), sensor data from intensive care unit (ICU), etc., is providing new opportunities to discover meaningful data-driven characteristics and patterns of...

Recognizing Question Entailment for Medical Question Answering.

AMIA ... Annual Symposium proceedings. AMIA Symposium
With the increasing heterogeneity and specialization of medical texts, automated question answering is becoming more and more challenging. In this context, answering a given medical question by retrieving similar questions that are already answered b...

Monitoring Hitting Load in Tennis Using Inertial Sensors and Machine Learning.

International journal of sports physiology and performance
CONTEXT: Quantifying external workload is fundamental to training prescription in sport. In tennis, global positioning data are imprecise and fail to capture hitting loads. The current gold standard (manual notation) is time intensive and often not p...

Practical application of the Average Information Content Maximization (AIC-MAX) algorithm: selection of the most important structural features for serotonin receptor ligands.

Molecular diversity
The Average Information Content Maximization algorithm (AIC-MAX) based on mutual information maximization was recently introduced to select the most discriminatory features. Here, this methodology was applied to select the most significant bits from ...

Who's Who? Detecting and Resolving Sample Anomalies in Human DNA Sequencing Studies with Peddy.

American journal of human genetics
The potential for genetic discovery in human DNA sequencing studies is greatly diminished if DNA samples from a cohort are mislabeled, swapped, or contaminated or if they include unintended individuals. Unfortunately, the potential for such errors is...

An efficient incremental learning mechanism for tracking concept drift in spam filtering.

PloS one
This research manages in-depth analysis on the knowledge about spams and expects to propose an efficient spam filtering method with the ability of adapting to the dynamic environment. We focus on the analysis of email's header and apply decision tree...

Performance analysis of a machine learning flagging system used to identify a group of individuals at a high risk for colorectal cancer.

PloS one
Individuals with colorectal cancer (CRC) have a tendency to intestinal bleeding which may result in mild to severe iron deficiency anemia, but for many colon cancer patients hematological abnormalities are subtle. The fecal occult blood test (FOBT) i...

Autodelineation of cervical cancers using multiparametric magnetic resonance imaging and machine learning.

Acta oncologica (Stockholm, Sweden)
BACKGROUND: Tumour delineation is a challenging, time-consuming and complex part of radiotherapy planning. In this study, an automatic method for delineating locally advanced cervical cancers was developed using a machine learning approach.

Machine learning based compartment models with permeability for white matter microstructure imaging.

NeuroImage
Some microstructure parameters, such as permeability, remain elusive because mathematical models that express their relationship to the MR signal accurately are intractable. Here, we propose to use computational models learned from simulations to est...