AIMC Topic: Machine Learning

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Applying Machine Learning to Pediatric Critical Care Data.

Pediatric critical care medicine : a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies
OBJECTIVES: To explore whether machine learning applied to pediatric critical care data could discover medically pertinent information, we analyzed clinically collected electronic medical record data, after data extraction and preparation, using k-me...

Automatic recognition of self-acknowledged limitations in clinical research literature.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: To automatically recognize self-acknowledged limitations in clinical research publications to support efforts in improving research transparency.

Inclusion of Unstructured Clinical Text Improves Early Prediction of Death or Prolonged ICU Stay.

Critical care medicine
OBJECTIVES: Early prediction of undesired outcomes among newly hospitalized patients could improve patient triage and prompt conversations about patients' goals of care. We evaluated the performance of logistic regression, gradient boosting machine, ...

Machine-learned analysis of the association of next-generation sequencing-based human TRPV1 and TRPA1 genotypes with the sensitivity to heat stimuli and topically applied capsaicin.

Pain
Heat pain and its modulation by capsaicin varies among subjects in experimental and clinical settings. A plausible cause is a genetic component, of which TRPV1 ion channels, by their response to both heat and capsaicin, are primary candidates. Howeve...

Interactive medical word sense disambiguation through informed learning.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Medical word sense disambiguation (WSD) is challenging and often requires significant training with data labeled by domain experts. This work aims to develop an interactive learning algorithm that makes efficient use of expert's domain kno...

Using Smartphones and Machine Learning to Quantify Parkinson Disease Severity: The Mobile Parkinson Disease Score.

JAMA neurology
IMPORTANCE: Current Parkinson disease (PD) measures are subjective, rater-dependent, and assessed in clinic. Smartphones can measure PD features, yet no smartphone-derived rating score exists to assess motor symptom severity in real-world settings.