Digitalizace postupně proniká do velké části medicínských oblastí včetně patologie. Společně s digitálním zpracováním dat přichází aplikace metod umělé inteligence za účelem zjednodušení rutinních procesů, zvýšení bezpečnosti apod. Ačkoliv se obecné povědomí o metodách umělé inteligence zvyšuje, stále není pravidlem, že by odborníci z netechnických oborů měli detailní představu o tom, jak takové systémy fungují a jak se učí. Cílem tohoto textu je přístupnou formou vysvětlit základy strojového učení s využitím příkladů a ilustrací z oblasti digitální patologie. Nejedná se samozřejmě o ucelený přehled ani o představení nejmodernějších metod. Držíme se spíše úplných základů a představujeme fundamentální myšlenky, které stojí za většinou učících systémů, s použitím nejjednodušších modelů. V textu se věnujeme zejména rozhodovacím stromům, jejichž funkce je snadno vysvětlitelná, a elementárním neuronovým sítím, které jsou hlavním modelem používaným v dnešní umělé inteligenci. Pokusíme se také popsat postup spolupráce mezi lékaři, kteří dodávají data, a informatiky, kteří s jejich pomocí vytvářejí učící systémy. Věříme, že tento text pomůže překlenout rozdíly mezi znalostmi lékařů a informatiků a tím přispěje k efektivnější mezioborové spolupráci.

Digitalization has gradually made its way into many areas of medicine, including pathology. Along with digital data processing comes the application of artificial intelligence methods to simplify routine processes, enhance safety, etc. Although general awareness of artificial intelligence methods is increasing, it is still not common for professionals from non-technical fields to have a detailed understanding of how such systems work and learn. This text aims to explain the basics of machine learning in an accessible way using examples and illustrations from digital pathology. This is not intended to be a comprehensive overview or an introduction to cutting-edge methods. Instead, we use the simplest models to focus on fundamental concepts behind most learning systems. The text concentrates on decision trees, whose functionality is easy to explain, and basic neural networks, the primary models used in today’s artificial intelligence. We also attempt to describe the collaborative process between medical specialists, who provide the data, and computer scientists, who use this data to develop learning systems. This text will help bridge the knowledge gap between medical professionals and computer scientists, contributing to more effective interdisciplinary collaboration.

• MeSH
• Humans MeSH
• Pathology * trends   MeSH
• Machine Learning * trends   MeSH
• Artificial Intelligence trends   MeSH
• Check Tag
• Humans MeSH

Digitalizace laboratoří, aplikace big dat a automatizovaná strojová diagnostika ("machine learning") jsou nástroji pro vznik a fungování toho, co se označuje jako precizní medicína. Genomika, její dominantní metody (qPCR, dPCR, ddPCR, NGS), produkující obrovská kvanta dat (big data) a schopnosti počítačových systémů tyto soubory dat využívat v diagnostice a terapii za významného přispění "umělé inteligence" se označují jako strojová automatizovaná diagnostika - machine learning respektive deep learning). Tyto postupy pronikají z průmyslu a výzkumu do rutinní medicíny včetně medicíny laboratorní. Zvládnutí technických a personálních problémů těchto změn bude stát značné úsilí, srovnatelné s před lety realizovanou přeměnou manuální laboratorní práce na automatizovanou činnost a s přeměnou papírové dokumentace výsledků na laboratorní a nemocniční informační systémy. Lze předpokládat nejen zásadní změny metod laboratorní práce, ale i změny požadavků na odbornost personálu laboratoří a rovněž lze předpokládat nevyhnutelnost radikálního ovlivnění činnosti klinických laboratoří. Etický rozměr nastávajících změn bude stejně závažný, jako ten technický a bude možné očekávat nejen významný progres v diagnostice e prognostice chorob, ale i vzestup rizika zdravotní péče v případě chyb a neprofesionality. Automatická strojová aplikace big dat a používání umělé inteligence jsou náročné, je s nimi v medicíně málo zkušeností, ale vyhnout se jim nebude možné.

Digitalization of clinical laboratories, application of big data and methods of machine learning re contemporary tools for precision medicine. Precision medicine is based mainly on the genomic methods, namely of dominant PCR and NGS methods. These methods produces enormous number of dates (big data) and can be explored by means of artificial intelligence in processes called machine learning. Machine learning was primarily used in industry and research and now contemporary penetrates into medicine and also to laboratory medicine. Methods based on the big data and artificial intelligence with exploration of big data is certainly very important factor of future of medicine. It will be needs large requirements not only on high-technology equipment, but also for new type of young laboratory Professional used basically new methods of work and mind. Machine learning, part of precision medicine, necessary namely for oncology and prediction of patients state crettemeans also lot of new types of ethical problems. These ethical questions and problems should be soluted immediately, parallel with introduction of machine learning to laboratory practice.

• MeSH
• Big Data MeSH
• Diagnosis, Computer-Assisted * methods   MeSH
• Precision Medicine MeSH
• Humans MeSH
• Machine Learning MeSH
• Artificial Intelligence MeSH
• Check Tag
• Humans MeSH

TransCelerate reports on the results of 2019, 2020, and 2021 member company (MC) surveys on the use of intelligent automation in pharmacovigilance processes. MCs increased the number and extent of implementation of intelligent automation solutions throughout Individual Case Safety Report (ICSR) processing, especially with rule-based automations such as robotic process automation, lookups, and workflows, moving from planning to piloting to implementation over the 3 survey years. Companies remain highly interested in other technologies such as machine learning (ML) and artificial intelligence, which can deliver a human-like interpretation of data and decision making rather than just automating tasks. Intelligent automation solutions are usually used in combination with more than one technology being used simultaneously for the same ICSR process step. Challenges to implementing intelligent automation solutions include finding/having appropriate training data for ML models and the need for harmonized regulatory guidance.

• MeSH
• Automation MeSH
• Pharmacovigilance * MeSH
• Humans MeSH
• Machine Learning MeSH
• Technology MeSH
• Artificial Intelligence * MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't 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
• Electronic Data Processing methods   MeSH
• Electronic Health Records * MeSH
• Supervised Machine Learning * MeSH
• Safety Management MeSH
• Statistics as Topic MeSH
• Machine Learning MeSH
• Support Vector Machine MeSH
• Health Information Systems 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
• Survival Analysis MeSH
• Deep Learning * MeSH
• Demography MeSH
• Adult MeSH
• Calibration MeSH
• Middle Aged MeSH
• Humans MeSH
• Young Adult MeSH
• Breast Neoplasms mortality   MeSH
• Neural Networks, Computer MeSH
• Decision Trees MeSH
• Aged, 80 and over MeSH
• Aged MeSH
• Support Vector Machine MeSH
• Check Tag
• Adult MeSH
• Middle Aged MeSH
• Humans MeSH
• Young Adult MeSH
• Aged, 80 and over MeSH
• Aged MeSH
• Female MeSH
• Publication type
• Journal Article 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
• Humans MeSH
• Internet Addiction Disorder * MeSH
• Supervised Machine Learning MeSH
• Machine Learning * MeSH
• User-Computer Interface MeSH
• Video Games MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article 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
• Adult MeSH
• Electrocardiography * methods   MeSH
• Genotype MeSH
• Humans MeSH
• Machine Learning * MeSH
• Support Vector Machine MeSH
• Long QT Syndrome * genetics   diagnosis   physiopathology   MeSH
• Check Tag
• Adult MeSH
• Humans MeSH
• Male MeSH
• Female MeSH
• Publication type
• Journal Article MeSH
• Comparative Study MeSH

The search for non-invasive, fast, and low-cost diagnostic tools has gained significant traction among many researchers worldwide. Dielectric properties calculated from microwave signals offer unique insights into biological tissue. Material properties, such as relative permittivity (εr) and conductivity (σ), can vary significantly between healthy and unhealthy tissue types at a given frequency. Understanding this difference in properties is key for identifying the disease state. The frequency-dependent nature of the dielectric measurements results in large datasets, which can be postprocessed using artificial intelligence (AI) methods. In this work, the dielectric properties of liver tissues in three mouse models of liver disease are characterized using dielectric spectroscopy. The measurements are grouped into four categories based on the diets or disease state of the mice, i.e., healthy mice, mice with non-alcoholic steatohepatitis (NASH) induced by choline-deficient high-fat diet, mice with NASH induced by western diet, and mice with liver fibrosis. Multi-class classification machine learning (ML) models are then explored to differentiate the liver tissue groups based on dielectric measurements. The results show that the support vector machine (SVM) model was able to differentiate the tissue groups with an accuracy up to 90%. This technology pipeline, thus, shows great potential for developing the next generation non-invasive diagnostic tools.

• MeSH
• Liver Cirrhosis MeSH
• Liver pathology   MeSH
• Mice, Inbred C57BL MeSH
• Mice MeSH
• Non-alcoholic Fatty Liver Disease * diagnosis   pathology   MeSH
• Machine Learning MeSH
• Artificial Intelligence MeSH
• Animals MeSH
• Check Tag
• Mice MeSH
• Animals MeSH
• Publication type
• Journal Article MeSH

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.

• Keywords
• dobývání znalostí, strojové učení s učitelem, vytěžování z biomedicínských dat, rizikové faktory aterosklerózy,
• MeSH
• Algorithms MeSH
• Atherosclerosis diagnosis   MeSH
• Databases, Factual MeSH
• Financing, Organized MeSH
• Middle Aged MeSH
• Humans MeSH
• Decision Support Techniques MeSH
• Prognosis MeSH
• Risk Factors MeSH
• Decision Support Systems, Clinical MeSH
• Information Storage and Retrieval MeSH
• Knowledge Bases MeSH
• Check Tag
• Middle Aged MeSH
• Humans MeSH
• Male MeSH

• MeSH
• Essential Tremor * MeSH
• Voice * MeSH
• Humans MeSH
• Voice Disorders * MeSH
• Reproducibility of Results MeSH
• Machine Learning MeSH
• Check Tag
• Humans MeSH
• Publication type
• Letter MeSH
• Comment MeSH
• Research Support, Non-U.S. Gov't MeSH