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.

Separation of HCM and LQT Cardiac Diseases with Machine Learning of Ca2+ Transient Profiles.

Background:  Modeling human cardiac diseases with induced pluripotent stem cells not only enables to study disease pathophysiology and develop therapies but also, as we have previously showed, it can offer a tool for disease diagnostics. We previously observed that a few genetic cardiac diseases can be separated from each other and healthy controls by applying machine learning to Ca transient signals measured from iPSC-derived cardiomyocytes (CMs).

Objectives:  For the current research, 419 hypertrophic cardiomyopathy (HCM) transient signals and 228 long QT syndrome (LQTS) transient signals were measured.

Detection of genetic cardiac diseases by Ca transient profiles using machine learning methods.

Human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) have revolutionized cardiovascular research. Abnormalities in Ca transients have been evident in many cardiac disease models. We have shown earlier that, by exploiting computational machine learning methods, normal Ca transients corresponding to healthy CMs can be distinguished from diseased CMs with abnormal transients.

Internet-Based Self-Help for Ménière's Disease: Details and Outcome of a Single-Group Open Trial.

Purpose: In this article, we present the details and the pilot outcome of an Internet-based self-help program for Ménière's disease (MD).

Method: The Norton-Kaplan model is applied to construct a strategic, person-focused approach in the enablement process. The program assesses the disorder profile and diagnosis.

Internet-based peer support for Ménière's disease: a summary of web-based data collection, impact evaluation, and user evaluation.

Objective: This paper presents a summary of web-based data collection, impact evaluation, and user evaluations of an Internet-based peer support program for Ménière's disease (MD).

Design: The program is written in html-form. The data are stored in a MySQL database and uses machine learning in the diagnosis of MD.

Error-Correcting Output Codes in Classification of Human Induced Pluripotent Stem Cell Colony Images.

The purpose of this paper is to examine how well the human induced pluripotent stem cell (hiPSC) colony images can be classified using error-correcting output codes (ECOC). Our image dataset includes hiPSC colony images from three classes (bad, semigood, and good) which makes our classification task a multiclass problem. ECOC is a general framework to model multiclass classification problems.

Machine Learning Approach to Automated Quality Identification of Human Induced Pluripotent Stem Cell Colony Images.

The focus of this research is on automated identification of the quality of human induced pluripotent stem cell (iPSC) colony images. iPS cell technology is a contemporary method by which the patient's cells are reprogrammed back to stem cells and are differentiated to any cell type wanted. iPS cell technology will be used in future to patient specific drug screening, disease modeling, and tissue repairing, for instance.

On Biometrics With Eye Movements.

Eye movements are a relatively novel data source for biometric identification. When video cameras applied to eye tracking become smaller and more efficient, this data source could offer interesting opportunities for the development of eye movement biometrics. In this paper, we study primarily biometric identification as seen as a classification task of multiple classes, and secondarily biometric verification considered as binary classification.

Novel Analysis Software for Detecting and Classifying Ca2+ Transient Abnormalities in Stem Cell-Derived Cardiomyocytes.

Comprehensive functioning of Ca2+ cycling is crucial for excitation-contraction coupling of cardiomyocytes (CMs). Abnormal Ca2+ cycling is linked to arrhythmogenesis, which is associated with cardiac disorders and heart failure. Accordingly, we have generated spontaneously beating CMs from induced pluripotent stem cells (iPSC) derived from patients with catecholaminergic polymorphic ventricular tachycardia (CPVT), which is an inherited and severe cardiac disease.

Signal analysis and classification methods for the calcium transient data of stem cell-derived cardiomyocytes.

Calcium cycling is crucial in the excitation-contraction coupling of cardiomyocytes, and therefore has a key role in cardiac functionality. Cardiac disorders and different drugs alter the calcium transients of cardiomyocytes and can cause serious dysfunction of the heart. New insights into this biochemical phenomena can be achieved by studying and analyzing calcium transients.

Investigating local spatially-enhanced structural and textural descriptors for classification of iPSC colony images.

Induced pluripotent stem cells (iPSC) can be derived from fully differentiated cells of adult individuals and used to obtain any other cell type of the human body. This implies numerous prospective applications of iPSCs in regenerative medicine and drug development. In order to obtain valid cell culture, a quality control process must be applied to identify and discard abnormal iPSC colonies.

On computation of calcium cycling anomalies in cardiomyocytes data.

Induced pluripotent stem cell (iPSC) lines derived from skin fibroblasts of patients suffering from cardiac disorders were differentiated to cardiomyocytes and used to generate a data set of Ca(2+) transients of 136 recordings. The objective was to separate normal signals for later medical research from abnormal signals. We constructed a signal analysis procedure to detect peaks representing calcium cycling in signals and another procedure to classify them into either normal or abnormal peaks.

Two-dimensional signal analysis reduced from three-dimensional nystagmus eye movements.

Earlier we developed signal analysis for nystagmus measured from otoneurological patients suffering from vertigo and dizziness. It was based on three rotation directions of the eye: horizontal, vertical and torsional. However, nystagmus frequently appears only in two of the former directions.

Machine learning approach to an otoneurological classification problem.

In this paper we applied altogether 13 classification methods to otoneurological disease classification. The main point was to use Half-Against-Half (HAH) architecture in classification. HAH structure was used with Support Vector Machines (SVMs), k-Nearest Neighbour (k-NN) method and Naïve Bayes (NB) methods.

Clustering and summarising association rules mined from phenotype, genotype and environmental data concerning age-related hearing impairment.

In this study, we examine the applicability of association rules for analysing high-dimensional data concerning age-related hearing impairment (ARHI). The ARHI data of the study contain hundreds of variables concerning phenotype, genotype and environmental factors. The number of association rules produced from the data is too large for manual exploration in the raw and furthermore, the rules are overlapping.

Evaluation of the postural stability of elderly persons using time domain signal analysis.

A force platform is widely used in the evaluation of postural stability in man. Although an abundance of parameters are typically retrieved from force platform data, no uniform analysis of the data has been carried out. In general, the signal analysis does not analyze the underlying postural event, i.

Biometric verification of a subject through eye movements.

Matching digital fingerprint, face or iris images, biometric verification of persons has advanced. Notwithstanding the progress, this is no easy computational task because of great numbers of complicated data. Since the 1990s, eye movements previously only applied to various tests of medicine and psychology are also studied for the purpose of computer interfaces.

Suppression of spontaneous nystagmus during different visual fixation conditions.

Analysis of spontaneous nystagmus is important in the evaluation of dizzy patients. The aim was to measure how different visual conditions affect the properties of nystagmus using three-dimensional video-oculography (VOG). We compared prevalence, frequency and slow phase velocity (SPV) of the spontaneous nystagmus with gaze fixation allowed, with Frenzel's glasses, and in total darkness.

Prediction of a state of a subject on the basis of a stabilogram signal and video oculography test.

Postural stability decreases with ageing and may lead to accidental falls, isolation and a reduction in the quality of life. The age at the onset of postural derangement, its extent and the reason for deterioration are poorly known within an individual, but in general it becomes more severe with age. In order to prevent falls and avoid severe injuries the postural derangement has to be noticed by the person and the possible nursing personnel.

Applying one-vs-one and one-vs-all classifiers in k-nearest neighbour method and support vector machines to an otoneurological multi-class problem.

We studied how the splitting of a multi-class classification problem into multiple binary classification tasks, like One-vs-One (OVO) and One-vs-All (OVA), affects the predictive accuracy of disease classes. Classifiers were tested with an otoneurological data using 10-fold cross-validation 10 times with k-Nearest Neighbour (k-NN) method and Support Vector Machines (SVM). The results showed that the use of multiple binary classifiers improves the classification accuracies of disease classes compared to one multi-class classifier.

A wearable, wireless gaze tracker with integrated selection command source for human-computer interaction.

A light-weight, wearable, wireless gaze tracker with integrated selection command source for human-computer interaction is introduced. The prototype system combines head-mounted, video-based gaze tracking with capacitive facial movement detection that enable multimodal interaction by gaze pointing and making selections with facial gestures. The system is targeted mainly to disabled people with limited mobility over their hands.