COVID-19 is a viral infectious disease that has created a global pandemic, resulting in millions of deaths and disrupting the world order. Different machine learning and deep learning approaches were considered to detect it utilizing different medical data. Thermal imaging is a promising option for detecting COVID-19 as it is low-cost, non-invasive, and can be maintained remotely. This work explores the COVID-19 detection issue using the thermal image and associated tabular medical data obtained from a publicly available dataset. We incorporate a multi-modal machine learning approach where we investigate the different combinations of medical and data type modalities to get an improved result. We use different machine learning and deep learning methods, namely random forests, Extreme Gradient Boosting (XGBoost), Multilayer Perceptron (MLP), and Convolutional Neural Network (CNN). Overall multi-modal results outperform any single modalities, and it is observed that the thermal image is a crucial factor in achieving it. X G Boost provided the best result with the area under the receiver operating characteristic curve (AUROC) score of 0.91 and the area under the precision-recall curve (AUPRC) score of 0.81. We also report the average of leave-one-positive-instance-out cross- validation evaluation scores. This average score is consistent with the test evaluation score for random forests and XGBoost methods. Our results suggest that utilizing thermal image with associated tabular medical data could be a viable option to detect COVID-19, and it should be explored further to create and test a real-time, secure, private, and remote COVID-19 detection application in the future.

Although Machine Learning can be seen as a promising tool to improve clinical decision-making, it remains limited by access to healthcare data. Healthcare data is sensitive, requiring strict privacy practices, and typically stored in data silos, making traditional Machine Learning challenging. Federated Learning can counteract those limitations by training Machine Learning models over data silos while keeping the sensitive data localized. This study proposes a Federated Learning workflow for Intensive Care Unit mortality prediction. Hereby, the applicability of Federated Learning as an alternative to Centralized Machine Learning and Local Machine Learning is investigated by introducing Federated Learning to the binary classification problem of predicting Intensive Care Unit mortality. We extract multivariate time series data from the MIMIC-III database (lab values and vital signs), and benchmark the predictive performance of four deep sequential classifiers (FRNN, LSTM, GRU, and 1DCNN) varying the patient history window lengths (8h, 16h, 24h, and 48h) and the number of Federated Learning clients (2, 4, and 8). The experiments demonstrate that both Centralized Machine Learning and Federated Learning are comparable in terms of AUPRC and F1-score. Furthermore, the federated approach shows superior performance over Local Machine Learning. Thus, Federated Learning can be seen as a valid and privacy-preserving alternative to Centralized Machine Learning for classifying Intensive Care Unit mortality when the sharing of sensitive patient data between hospitals is not possible.

Visualization is a key point in data exploration. In this paper we have emphasis in adding dynamic features by constructing exploration animations. We use Principal Component Analysis (PCA) in dimensionality reduction and K-means clustering algorithm in defining states. In predicting state transitions, we use Hidden Markov Model (HMM). Analyzed physical data is got from self-healing autonomous data centers. Our research methodology is to animate state transitions for data exploration in modern computerized environment. We use Jupyter tool and Python 3 programming language in our experimental realization. As results we get PCA animations for exploration purposes. Our approach is based on state discovery, where it is possible to find some physical interpretations for the defined states and state transitions. State structure and behaviour depend strongly on analyzed data.

We present a clustering framework for identifying patient groups with Adverse Drug Reactions from Electronic Health Records (EHRs). The increased adoption of EHRs has brought changes in the way drug safety surveillance is carried out and plays an important role in effective drug regulation. Unsupervised machine learning methods using EHRs as their input can identify patients that share common meaningful information, without the need for expert input. In this work, we propose a generalized framework that exploits the strengths of different clustering algorithms and via clustering aggregation identifies consensus patient cluster profiles. Moreover, the inherent hierarchical structure of diagnoses and medication codes is exploited. We assess the statistical significance of the produced clusterings by applying a randomization technique that keeps the data distribution margins fixed, as we are interested in evaluating information that is not conveyed by the marginal distributions. The experimental findings suggest that the framework produces medically meaningful patient groups with regard to adverse drug events by investigating two use-cases, i.e., aplastic anaemia and drug-induced skin eruption.

Online changepoint detection is an important task for machine learning in changing environments, as it signals when the learning model needs to be updated. Presence of noise that can be mistaken for real changes makes it difficult to develop an effective approach that would have a low false alarm rate and being able to detect all the changes with a minimal delay. In this paper we study how performance of popular Bayesian online detectors can be improved in case of recurrent changes. Modelling recurrence allows us to anticipate future changepoints and predict their locations in time. We propose an approach for inducing and integrating recurrence information in the streaming settings, and demonstrate its effectiveness on synthetic and realworld human activity datasets.

Determining the minimal amount of resources needed to ensure minimal number of bottlenecks in the patient flow not only promotes patient satisfaction but also provides financial benefits to hospitals. The increase of data gathering by healthcare facilities in the last years have brought new opportunities to apply machine learning techniques to tackle this problem. This work makes use of data gathered from the Oulu University Hospital in Finland between 2011 and 2014 to study the effectiveness of machine learning techniques to predict resources usage. This work investigates the problem of resource frequency prediction and compares the performance of Nearest Neighbours and Random Forest. The application of data clustering as a preprocessing step is also explored as a way to improve the prediction accuracy of resources whose behavior change over time. The results indicate that 1) highly frequented resources can be predicted with higher accuracy than the lowly frequented resources, 2) the Random Forest have similar performance to the Nearest Neighbours although Random Forest performs better, 3) clustering improves the performance of the Nearest Neighbours but not of Random Forest, and 4) if averages are used to determine the resource frequency then cluster averages yields higher accuracy than all data averages.

Many studies have been performed in predicting periodontal diseases based on genetic information, dental images or patients habits but few have yet used dental visits records. This paper proposes a methodology based on Random Forest to classify the periodontal disease condition of patients and a way to assess the most important features that lead to a successful classification. We investigate three problematic issues found in dental care records: noise, class imbalance and concept drift and propose solutions to overcome them by respectively detecting and removing noise, under-sampling and only considering recent data. Experiments performed on records from Finnish public hospitals of two cities had good classification results and feature importance was able to detect dentists with poor performance with respect to diagnosis and treatment application.

We present "Hum-a-song", a system built for music retrieval, and particularly for the Query-By-Humming (QBH) application. According to QBH, the user is able to hum a part of a song that she recalls and would like to learn what this song is, or find other songs similar to it in a large music repository. We present a simple yet efficient approach that maps the problem to time series subsequence matching. The query and the database songs are represented as 2-dimensional time series conveying information about the pitch and the duration of the notes. Then, since the query is a short sequence and we want to find its best match that may start and end anywhere in the database, subsequence matching methods are suitable for this task. In this demo, we present a system that employs and exposes to the user a variety of state-of-the-art dynamic programming methods, including a newly proposed efficient method named SMBGT that is robust to noise and considers all intrinsic problems in QBH; it allows variable tolerance levels when matching elements, where tolerances are defined as functions of the compared sequences, gaps in both the query and target sequences, and bounds the matching length and (optionally) the minimum number of matched elements. Our system is intended to become open source, which is to the best of our knowledge the first non-commercial effort trying to solve QBH with a variety of methods, and that also approaches the problem from the time series perspective.

We study the interaction between global and local techniques in data mining. Specifically, we study the collections of frequent sets in clusters produced by a probabilistic clustering using mixtures of Bernoulli models. That is, we first analyze 0–1 datasets by a global technique (probabilistic clustering using the EM algorithm) and then do a local analysis (discovery of frequent sets) in each of the clusters. The results indicate that the use of clustering as a preliminary phase in finding frequent sets produces clusters that have significantly different collections of frequent sets. We also test the significance of the differences in the frequent set collections in the different clusters by obtaining estimates of the underlying joint density. To get from the local patterns in each cluster back to distributions, we use the maximum entropy technique [17] to obtain a local model for each cluster, and then combine these local models to get a mixture model. We obtain clear improvements to the approximation quality against the use of either the mixture model or the maximum entropy model.

Fraud detection refers to the attempt to detect illegitimate usage of a communication network. Three methods to detect fraud are presented. Firstly, a feed-forward neural network based on supervised learning is used to learn a discriminative function to classify subscribers using summary statistics. Secondly, a Gaussian mixture model is used to model the probability density of subscribers' past behavior so that the probability of current behavior can be calculated to detect any abnormalities from the past behavior. Lastly, Bayesian networks are used to describe the statistics of a particular user and the statistics of different fraud scenarios. The Bayesian networks can be used to infer the probability of fraud given the subscribers' behavior. The data features are derived from toll tickets. The experiments show that the methods detect over 85% of the fraudsters in our testing set without causing false alarms.