Techniky strojového učení jsou metody, které umožní vytvořit z trénovací množiny případů model pro kategorie dat tak, že mohou být nové (neznámé) případy zařazeny do jedné nebo více kategorií schématem odpovídajícím modelu. Pro tento typ analýzy jsou velmi vhodná data ze studií sledujících určitou skupinu osob s opakovaným sběrem dat stejného typu. K vyhledávání znalostí z medicínských dat bylo užito různých algoritmů strojového učení. Bylo testováno několik algoritmů tak, aby bylo možno pokrýt většinu způsobů učení s učitelem. Byly provedeny dva typy pokusů. Jeden hledal vztahy mezi atributy, druhý testoval predikci budoucích příhod. Pro pokusy v tomto sdělení byla užita data z dvacet let trvající longitudinální primárně preventivní studie rizikových faktorů (RF) aterosklerózy u mužů středního věku. Studie se nazývá STULONG (LONGitudinal STUdy). Výsledky ukazují, že některé metody předpovídají některé poruchy lépe než jiné a že je tedy vhodné použít všechny algoritmy najednou a posuzovat spolehlivost výsledku na základě známého trendu každé metody. Algoritmy strojového učení byly také použity k předpovědi příčiny úmrtí. V tomto případě byly výsledky nevalné, pravděpodobně pro malé množství informace ve vstupních položkách v datového souboru.

Machine learning techniques are methods that given a training set of examples infer a model for the categories of the data, so that new (unknown) examples could be assigned to one or more categories by pattern matching within the model. The data from follow-up studies with repeated collection of the same type of data are very suitable for this analysis. Machine learning algorithms belonging to a variety of paradigms have been applied to knowledge discovery on medical data. All the used algorithms belong to the supervised learning paradigm. Several algorithms have been tested, trying to cover most of the kinds of supervised learning. Two kinds of experiments have been carried out. The first is intended to discover associations between attributes. The second kind is intended to test prediction of future disorders. For the experiments in this paper the data used was from the twenty years lasting primary preventive longitudinal study of the risk factors (RF) of atherosclerosis in middle aged men. Study is named STULONG (LONGitudinal STUdy). The results show that some methods predict some disorders better than others, so it is interesting to use all the algorithms at a time and consider the result confidence based upon the known tendency of each method. The machine learning algorithms have been also used in the prediction of death cause, obtaining poor results in this case, maybe due to the small amount of information (entries) of this type in the dataset.

• Klíčová slova
• dobývání znalostí, strojové učení s učitelem, vytěžování z biomedicínských dat, rizikové faktory aterosklerózy,
• MeSH
• algoritmy MeSH
• ateroskleróza diagnóza   MeSH
• faktografické databáze MeSH
• financování organizované MeSH
• lidé středního věku MeSH
• lidé MeSH
• metody pro podporu rozhodování MeSH
• prognóza MeSH
• rizikové faktory MeSH
• systémy pro podporu klinického rozhodování MeSH
• ukládání a vyhledávání informací MeSH
• znalostní báze MeSH
• Check Tag
• lidé středního věku MeSH
• lidé MeSH
• mužské pohlaví MeSH

The goal of this research was to design a solution to detect non-reported incidents, especially severe incidents. To achieve this goal, we proposed a method to process electronic medical records and automatically extract clinical notes describing severe incidents. To evaluate the proposed method, we implemented a system and used the system. The system successfully detected a non-reported incident to the safety management department.

• MeSH
• automatizované zpracování dat metody   MeSH
• elektronické zdravotní záznamy * MeSH
• řízené strojové učení * MeSH
• řízení bezpečnosti MeSH
• statistika jako téma MeSH
• strojové učení MeSH
• support vector machine MeSH
• zdravotnické informační systémy MeSH

In response to our study, the commentary by Infanti et al. (2024) raised critical points regarding (i) the conceptualization and utility of the user-avatar bond in addressing gaming disorder (GD) risk, and (ii) the optimization of supervised machine learning techniques applied to assess GD risk. To advance the scientific dialogue and progress in these areas, the present paper aims to: (i) enhance the clarity and understanding of the concepts of the avatar, the user-avatar bond, and the digital phenotype concerning gaming disorder (GD) within the broader field of behavioral addictions, and (ii) comparatively assess how the user-avatar bond (UAB) may predict GD risk, by both removing data augmentation before the data split and by implementing alternative data imbalance treatment approaches in programming.

• MeSH
• avatar MeSH
• lidé MeSH
• netholismus * MeSH
• řízené strojové učení MeSH
• strojové učení * MeSH
• uživatelské rozhraní počítače MeSH
• videohry MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH

Breast cancer survival prediction can have an extreme effect on selection of best treatment protocols. Many approaches such as statistical or machine learning models have been employed to predict the survival prospects of patients, but newer algorithms such as deep learning can be tested with the aim of improving the models and prediction accuracy. In this study, we used machine learning and deep learning approaches to predict breast cancer survival in 4,902 patient records from the University of Malaya Medical Centre Breast Cancer Registry. The results indicated that the multilayer perceptron (MLP), random forest (RF) and decision tree (DT) classifiers could predict survivorship, respectively, with 88.2 %, 83.3 % and 82.5 % accuracy in the tested samples. Support vector machine (SVM) came out to be lower with 80.5 %. In this study, tumour size turned out to be the most important feature for breast cancer survivability prediction. Both deep learning and machine learning methods produce desirable prediction accuracy, but other factors such as parameter configurations and data transformations affect the accuracy of the predictive model.

• MeSH
• analýza přežití MeSH
• deep learning * MeSH
• demografie MeSH
• dospělí MeSH
• kalibrace MeSH
• lidé středního věku MeSH
• lidé MeSH
• mladý dospělý MeSH
• nádory prsu mortalita   MeSH
• neuronové sítě MeSH
• rozhodovací stromy MeSH
• senioři nad 80 let MeSH
• senioři MeSH
• support vector machine MeSH
• Check Tag
• dospělí MeSH
• lidé středního věku MeSH
• lidé MeSH
• mladý dospělý MeSH
• senioři nad 80 let MeSH
• senioři MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH

Práce s big daty vyžaduje použití prostředků umělé inteligence. Přináší to možnost transformace laboratorních výsledků do formy strojového učení-machine learning (ML). Od něho se očekává aktivace dat, přinášející zlepšení diagnostických možností laboratorních vyšetření. Jde o posuv od použití počítačů, sloužících z části jako skladiště mrtvých dat, k aktivnějšímu využití jejich potenciálu pro diagnostiku, management, edukaci, výzkum a další. Zejména pak k predikci stavu chorob a k precizní medicíně v onkologii i jinde. Důsledkem by měl být integrovaný mezioborový přístup k diagnostice a reálné dosažení efektivní personalizace při diagnostice a terapii pacientů. Sdělení je pokusem o pomoc při zavádění práce s big daty a umělou inteligencí v klinických laboratořích. Vychází z faktu obrovské akcelerace tohoto přístupu, zdaleka nejen pouze v laboratorní medicíně.

Working the big data needs using of artificial intelligence tools. This approach introduced currently into practice by large velocity leads to machine learning. Machine learning should be a strong way namely for the prediction of patient's state, for precision medicine in oncology and many more cases. For example for aiming the real personalisation of patients in dese of their diagnosis and therapy. This work can be a helpful tool for the introduction of artificial intelligence in routine clinical laboratories.

• MeSH
• big data * MeSH
• klinické laboratoře * MeSH
• klinické laboratorní techniky MeSH
• laboratorní informační systémy MeSH
• lidé MeSH
• strojové učení MeSH
• umělá inteligence * MeSH
• využití lékařské informatiky MeSH
• Check Tag
• lidé MeSH

BACKGROUND: Untargeted tandem mass spectrometry serves as a scalable solution for the organization of small molecules. One of the most prevalent techniques for analyzing the acquired tandem mass spectrometry data (MS/MS) - called molecular networking - organizes and visualizes putatively structurally related compounds. However, a key bottleneck of this approach is the comparison of MS/MS spectra used to identify nearby structural neighbors. Machine learning (ML) approaches have emerged as a promising technique to predict structural similarity from MS/MS that may surpass the current state-of-the-art algorithmic methods. However, the comparison between these different ML methods remains a challenge because there is a lack of standardization to benchmark, evaluate, and compare MS/MS similarity methods, and there are no methods that address data leakage between training and test data in order to analyze model generalizability. RESULT: In this work, we present the creation of a new evaluation methodology using a train/test split that allows for the evaluation of machine learning models at varying degrees of structural similarity between training and test sets. We also introduce a training and evaluation framework that measures prediction accuracy on domain-inspired annotation and retrieval metrics designed to mirror real-world applications. We further show how two alternative training methods that leverage MS specific insights (e.g., similar instrumentation, collision energy, adduct) affect method performance and demonstrate the orthogonality of the proposed metrics. We especially highlight the role that collision energy plays in prediction errors. Finally, we release a continually updated version of our dataset online along with our data cleaning and splitting pipelines for community use. CONCLUSION: It is our hope that this benchmark will serve as the basis of development for future machine learning approaches in MS/MS similarity and facilitate comparison between models. We anticipate that the introduced set of evaluation metrics allows for a better reflection of practical performance.

• MeSH
• algoritmy MeSH
• strojové učení * MeSH
• tandemová hmotnostní spektrometrie * metody   MeSH
• Publikační typ
• časopisecké články MeSH

Long QT syndrome (LQTS) presents a group of inheritable channelopathies with prolonged ventricular repolarization, leading to syncope, ventricular tachycardia, and sudden death. Differentiating LQTS genotypes is crucial for targeted management and treatment, yet conventional genetic testing remains costly and time-consuming. This study aims to improve the distinction between LQTS genotypes, particularly LQT3, through a novel electrocardiogram (ECG)-based approach. Patients with LQT3 are at elevated risk due to arrhythmia triggers associated with rest and sleep. Employing a database of genotyped long QT syndrome E-HOL-03-0480-013 ECG signals, we introduced two innovative parameterization techniques-area under the ECG curve and wave transformation into the unit circle-to classify LQT3 against LQT1 and LQT2 genotypes. Our methodology utilized single-lead ECG data with a 200 Hz sampling frequency. The support vector machine (SVM) model demonstrated the ability to discriminate LQT3 with a recall of 90% and a precision of 81%, achieving an F1-score of 0.85. This parameterization offers a potential substitute for genetic testing and is practical for low frequencies. These single-lead ECG data could enhance smartwatches' functionality and similar cardiovascular monitoring applications. The results underscore the viability of ECG morphology-based genotype classification, promising a significant step towards streamlined diagnosis and improved patient care in LQTS.

• MeSH
• dospělí MeSH
• elektrokardiografie * metody   MeSH
• genotyp MeSH
• lidé MeSH
• strojové učení * MeSH
• support vector machine MeSH
• syndrom dlouhého QT * genetika   diagnóza   patofyziologie   MeSH
• Check Tag
• dospělí MeSH
• lidé MeSH
• mužské pohlaví MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• srovnávací studie MeSH

The scarcity of high-quality annotations in many application scenarios has recently led to an increasing interest in devising learning techniques that combine unlabeled data with labeled data in a network. In this work, we focus on the label propagation problem in multilayer networks. Our approach is inspired by the heat diffusion model, which shows usefulness in machine learning problems such as classification and dimensionality reduction. We propose a novel boundary-based heat diffusion algorithm that guarantees a closed-form solution with an efficient implementation. We experimentally validated our method on synthetic networks and five real-world multilayer network datasets representing scientific coauthorship, spreading drug adoption among physicians, two bibliographic networks, and a movie network. The results demonstrate the benefits of the proposed algorithm, where our boundary-based heat diffusion dominates the performance of the state-of-the-art methods.

• MeSH
• algoritmy MeSH
• řízené strojové učení * MeSH
• strojové učení MeSH
• vysoká teplota * MeSH
• Publikační typ
• časopisecké články MeSH

Analysis of massive knowledge within the study of however life and drugs work along and health field has driven the requirement of recent effective analysis technology. Deep learning could be a powerful machine learning technique. With the providing of quick computer-based power improvement, it's turning into a promising method of doing things to form new data, clarification and gain understandings of deep things from high-throughout, cluster totally different of various things mixed along and sophisticated the study of however life and drugs work along knowledge from different sources, like X-rays, MRIs, etc., medicine-based study of the little chemical directions among cells, and electronic health records. This paper presents an outline of the utilization of deep learning approach within the study of however life and drugs work along IP. Initial we tend to introduce the event of synthetic brain-related network and deep learning, and then chiefly target the researches applying deep learning within the study of however life and drugs work along within the medical specialty IP field. We tend to additionally discuss the challenges for future improvement, like knowledge quality explains ability.

• MeSH
• deep learning * MeSH
• digitální zdraví MeSH
• lidé MeSH
• výpočetní biologie metody   MeSH
• Check Tag
• lidé MeSH

During the past years, the increase in scientific knowledge and the massive data production have caused an exponential growth in databases and repositories. Biomedical domain represents one of the rich data domains. An extensive amount of biomedical data is currently available, ranging from details of clinical symptoms to various types of biochemical data and outputs of imaging devices. Manually extracting biomedical patterns from data and transforming them into machine-understandable knowledge is a difficult task because biomedical domain comprises huge, dynamic, and complicated knowledge. Data mining is capable of improving the quality of extracting biomedical patterns. In this research, an overview of the applications of data mining on the management of diseases is presented. The main focus is to investigate machine learning techniques (MLT) which are widely used to predict, prognose and treat important frequent diseases such as cancers, hepatitis and heart diseases. The techniques namely Artificial Neural Network, K-Nearest Neighbour, Decision Tree, and Associative Classification are illustrated and analyzed. This survey provides a general analysis of the current status of management of diseases using MLT. The achieved accuracy of the various applications ranged from 70% to 100% according to the disease, the solved problem, and the used data and technique.

• Klíčová slova
• rozhodovací strom, Algoritmus k-nejbližších sousedů, associative classification,
• MeSH
• algoritmy MeSH
• data mining MeSH
• databáze jako téma MeSH
• hepatitida * diagnóza   klasifikace   MeSH
• kardiovaskulární nemoci * diagnóza   klasifikace   MeSH
• lékařská informatika MeSH
• lidé MeSH
• management nemoci MeSH
• nádory * diagnóza   klasifikace   MeSH
• neuronové sítě MeSH
• prognóza MeSH
• správnost dat MeSH
• strojové učení * klasifikace   MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• přehledy MeSH