Search results for "tiedonlouhinta"
showing 10 items of 76 documents
Automatic Profiling of Open-Ended Survey Data on Medical Workplace Teaching
2019
On-the-job medical training is known to be challenging due to the fast-paced environment and strong vocational profile. It relies on on-site supervisors, mainly doctors and nurses with long practical experience, who coach and teach their less experienced colleagues, such as residents and healthcare students. These supervisors receive pedagogical training to ensure that their guidance and teaching skills are constantly improved. The aim of such training is to develop participants’ patient, collegiate and student guidance skills in a multiprofessional environment, and to expand their understanding of guidance as part of their work as supervisors of healthcare professionals. In this paper, we …
3D Matrix-Based Visualization System of Association Rules
2017
With the growing number of mining datasets, it becomes increasingly difficult to explore interesting rules because of the large number of resultant and its nature complexity. Studies on human perception and intuition show that graphical representation could be a better illustration of how to seek information from the data using the capabilities of human visual system. In this work, we present and implement a 3D matrix-based approach visualization system of association rules. The main visual representation applies the extended matrix-based approach with rule-to-items mapping to general transaction data set. A novel method merging rules and assigning weight is proposed in order to reduce the …
Predicting hospital associated disability from imbalanced data using supervised learning.
2019
Hospitalization of elderly patients can lead to serious adverse effects on their functional capability. Identifying the underlying factors leading to such adverse effects is an active area of medical research. The purpose of the current paper is to show the potential of artificial intelligence in the form of machine learning to complement the existing medical research. This is accomplished by studying the outcome of hospitalization of elderly patients as a supervised learning task. A rich set of features characterizing the medical and social situation of elderly patients is leveraged and using confusion matrices, association rule mining, and two different classes of supervised learning algo…
Combining conjunctive rule extraction with diffusion maps for network intrusion detection
2013
Network security and intrusion detection are important in the modern world where communication happens via information networks. Traditional signature-based intrusion detection methods cannot find previously unknown attacks. On the other hand, algorithms used for anomaly detection often have black box qualities that are difficult to understand for people who are not algorithm experts. Rule extraction methods create interpretable rule sets that act as classifiers. They have mostly been combined with already labeled data sets. This paper aims to combine unsupervised anomaly detection with rule extraction techniques to create an online anomaly detection framework. Unsupervised anomaly detectio…
Data-driven decision support to reduce "driving-under the influence of alcohol" offenses
2018
Extracting valuable knowledge from data to support decision making is a widely practiced trend. Data-driven decision support (DDDS) provides insight for decision makers by exploring and extracting underlying patterns within a dataset. This thesis covers the process of DDDS in reducing driving under the influence of alcohol (DUI) offenses by introducing proposed prison sentences. In this thesis, DDDS is applied to a DUI dataset by analyzing patterns in the dataset and by introducing proposed prison sentences for offenders to reduce the number of DUI cases. Background theories in data mining, machine learning, optimization and decision science that are related to the thesis project are also c…
A First Experiment on Including Text Literals in KGloVe
2018
Graph embedding models produce embedding vectors for entities and relations in Knowledge Graphs, often without taking literal properties into account. We show an initial idea based on the combination of global graph structure with additional information provided by textual information in properties. Our initial experiment shows that this approach might be useful, but does not clearly outperform earlier approaches when evaluated on machine learning tasks.
Classification of Heart Sounds Using Convolutional Neural Network
2020
Heart sounds play an important role in the diagnosis of cardiac conditions. Due to the low signal-to-noise ratio (SNR), it is problematic and time-consuming for experts to discriminate different kinds of heart sounds. Thus, objective classification of heart sounds is essential. In this study, we combined a conventional feature engineering method with deep learning algorithms to automatically classify normal and abnormal heart sounds. First, 497 features were extracted from eight domains. Then, we fed these features into the designed convolutional neural network (CNN), in which the fully connected layers that are usually used before the classification layer were replaced with a global averag…
Comparison of Internal Clustering Validation Indices for Prototype-Based Clustering
2017
Clustering is an unsupervised machine learning and pattern recognition method. In general, in addition to revealing hidden groups of similar observations and clusters, their number needs to be determined. Internal clustering validation indices estimate this number without any external information. The purpose of this article is to evaluate, empirically, characteristics of a representative set of internal clustering validation indices with many datasets. The prototype-based clustering framework includes multiple, classical and robust, statistical estimates of cluster location so that the overall setting of the paper is novel. General observations on the quality of validation indices and on t…
Data Analytics in Healthcare: A Tertiary Study
2022
AbstractThe field of healthcare has seen a rapid increase in the applications of data analytics during the last decades. By utilizing different data analytic solutions, healthcare areas such as medical image analysis, disease recognition, outbreak monitoring, and clinical decision support have been automated to various degrees. Consequently, the intersection of healthcare and data analytics has received scientific attention to the point of numerous secondary studies. We analyze studies on healthcare data analytics, and provide a wide overview of the subject. This is a tertiary study, i.e., a systematic review of systematic reviews. We identified 45 systematic secondary studies on data analy…
Talent identification in soccer using a one-class support vector machine
2019
Abstract Identifying potential future elite athletes is important in many sporting events. The successful identification of potential future elite athletes at an early age would help to provide high-quality coaching and training environments in which to optimize their development. However, a large variety of different skills and qualities are needed to succeed in elite sports, making talent identification generally a complex and multifaceted problem. Due to the rarity of elite athletes, datasets are inherently imbalanced, making classical statistical inference difficult. Therefore, we approach talent identification as an anomaly detection problem. We trained a nonlinear one-class support ve…