Comparison of machine learning methods in the early identification of vasculitides, myositides and glomerulonephritides.

Background: Rare disease diagnoses are often delayed by years, including multiple doctor visits, and potential imprecise or incorrect diagnoses before receiving the correct one. Machine learning could solve this problem by flagging potential patients that doctors should examine more closely.

Methods: Making the prediction situation as close as possible to real situation, we tested different masking sizes.

Data analytics for cardiac diseases.

In the present research we tackled the classification of seven genetic cardiac diseases and control subjects by using an extensive set of machine learning algorithms with their variations from simple K-nearest neighbor searching method to support vector machines. The research was based on calcium transient signals measured from induced pluripotent stem cell-derived cardiomyocytes. All in all, 55 different machine learning alternatives were used to model eight classes by applying the principle of 10-fold crossvalidation with the peak data of 1626 signals.

On computational classification of genetic cardiac diseases applying iPSC cardiomyocytes.

Background: Cardiomyocytes differentiated from human induced pluripotent stem cells (iPSC-CMs) can be used to study genetic cardiac diseases. In patients these diseases are manifested e.g.

Analysis of Drug Effects on iPSC Cardiomyocytes with Machine Learning.

Patient-specific induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) offer an attractive experimental platform to investigate cardiac diseases and therapeutic outcome. In this study, iPSC-CMs were utilized to study their calcium transient signals and drug effects by means of machine learning, a central part of artificial intelligence. Drug effects were assessed in six iPSC-lines carrying different mutations causing catecholaminergic polymorphic ventricular tachycardia (CPVT), a highly malignant inherited arrhythmogenic disorder.