Combining theory-driven and data-driven approaches, this study used both self-reported and observational measures to examine: (1) the joint contributions of students' self-reported undergraduates' motivation and emotion in their self-regulated learning, their observed online learning interactions, and their academic success in blended course designs; and (2) the extent to which the self-reported and observational measures were consistent with each other. The participants in the study were 54 social sciences undergraduates in the Czech Republic. The participants' self-reported self-efficacy, intrinsic goals, and anxiety were assessed using a Czech version of three scales from the Motivated Strategies for Learning Questionnaire. Their online engagement was represented by students' observed frequency of interactions with the six online learning activities recorded in the learning management system. The results of a hierarchical regression analysis showed that the self-reported and observational measures together could explain 71% of variance in academic success, significantly improving explanatory power over using self-reported measures alone. Departing from the theory-driven approach, students were clustered as better and poorer self-regulated learners by their self-reports, and one-way ANOVAs showed that better self-regulated learners had significantly more frequent online interactions with four out of six online learning activities and better final exam results. Departing from the data-driven approach, students were clustered as higher and lower online-engaged learners by the observed frequency of their interaction with online learning activities. One-way ANOVAs showed that higher online-engaged learners also reported having higher self-efficacy and lower anxiety. Furthermore, the strong association between the students' profiles in both self-reported measures and observational measures in cross-tabulation analyses showed that the majority of better self-regulated learners by self-reporting also had higher online engagement by observation, whereas the majority of poorer self-regulated learners by self-reporting were lower online-engaged learners, demonstrating consistency between theory-driven and data-driven approaches.
- Publication type
- Journal Article MeSH
In recent years, expanding uses of artificial intelligence (AI) and machine learning have revolutionized pharmaceutical research and development, allowing us to harness multi-dimensional biological and clinical data from experimental to real-world settings (ML). Precision medicine discovery and development, from target validation to medication optimization, is driven by patient-centered iterative forward and reverse translation. As evidenced by deep characterizations of the genome, transcriptome, proteome, metabolome, microbiome, and exposome, the integration of advanced analytics into the practise of Translational Medicine is now a critical enabler for fully exploiting information contained in diverse sources of big data sets such as “omics” data. In this article, we provide an overview of machine learning (ML) applications in drug discovery and development, aligned with the three strategic pillars of Translational Medicine (target, patient, and dose), and discuss how they can alter the science and practise of the discipline. Model-informed drug discovery and development will be revolutionised if ML approaches are integrated into the science of pharmacometrics. Finally, we believe that cross-functional team activities involving clinical pharmacology, bioinformatics, and biomarker technology experts are critical to realising the promise of AI/ML-enabled Translational and Precision Medicine.
OBJECTIVES: The International Society for Bipolar Disorders Big Data Task Force assembled leading researchers in the field of bipolar disorder (BD), machine learning, and big data with extensive experience to evaluate the rationale of machine learning and big data analytics strategies for BD. METHOD: A task force was convened to examine and integrate findings from the scientific literature related to machine learning and big data based studies to clarify terminology and to describe challenges and potential applications in the field of BD. We also systematically searched PubMed, Embase, and Web of Science for articles published up to January 2019 that used machine learning in BD. RESULTS: The results suggested that big data analytics has the potential to provide risk calculators to aid in treatment decisions and predict clinical prognosis, including suicidality, for individual patients. This approach can advance diagnosis by enabling discovery of more relevant data-driven phenotypes, as well as by predicting transition to the disorder in high-risk unaffected subjects. We also discuss the most frequent challenges that big data analytics applications can face, such as heterogeneity, lack of external validation and replication of some studies, cost and non-stationary distribution of the data, and lack of appropriate funding. CONCLUSION: Machine learning-based studies, including atheoretical data-driven big data approaches, provide an opportunity to more accurately detect those who are at risk, parse-relevant phenotypes as well as inform treatment selection and prognosis. However, several methodological challenges need to be addressed in order to translate research findings to clinical settings.
- MeSH
- Big Data * MeSH
- Bipolar Disorder epidemiology therapy MeSH
- Data Science MeSH
- Phenotype MeSH
- Risk Assessment MeSH
- Clinical Decision-Making * MeSH
- Humans MeSH
- Advisory Committees MeSH
- Prognosis MeSH
- Suicidal Ideation * MeSH
- Machine Learning * MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
- Review MeSH
x, 179 stran : ilustrace, grafy, schémata, portréty
V okamžiku rozšíření Covid-19 infekční nemoci se umělá inteligence (UI), strojové učení (ML, machine learning) a věda o datech staly významným pomocníkem v boji proti viru SARS-CoV-2. Metody se využívají při diagnóze, k vývoji nových léků a očkovacích látek, k modelování a předpovědi šíření a k monitorování výskytu nemoci v populaci a v logistice zdravotnictví. Covid-19 pandemie zvýšila snahy členů komunity vědců z oblastí UI, ML a vědy o datech v hledání řešení problémů, které pandemie vyvolala. Objem literatury o aplikacích UI, ML a vědy o datech se stále zvětšuje. V našem příspěvku podáváme přehled hlavních oblastí aplikací a informujeme o literatuře a některých výsledcích snah při zvládání COVID-19 pandemie.
Open Science is an umbrella term encompassing multiple concepts as open access to publications, open data, open education and citizen science that aim to make science more open and transparent. Citizen science, an important facet of Open Science, actively involves non-scientists in the research process, and can potentially be beneficial for multiple actors, such as scientists, citizens, policymakers and society in general. However, the reasons that motivate different segments of the public to participate in research are still understudied. Therefore, based on data gathered from a survey conducted in Czechia, Germany, Italy, Spain, Sweden, and the UK (N = 5,870), this study explores five types of incentives that can motivate individuals to become involved in life sciences research. The results demonstrate that men and younger individuals are more persuaded by extrinsic motives (external benefits or rewards), as compared with women and older people, who are driven by intrinsic motives (that originates from within an individual). This paper shows that specific strata of the population are differentially motivated to engage in research, thereby providing relevant knowledge for effectively designing public involvement activities that target various groups of the public in research projects.
- MeSH
- Biological Science Disciplines statistics & numerical data MeSH
- Adult MeSH
- Middle Aged MeSH
- Humans MeSH
- Adolescent MeSH
- Motivation * MeSH
- Citizen Science statistics & numerical data MeSH
- Aged MeSH
- Sex Factors MeSH
- Educational Status MeSH
- Age Factors MeSH
- Check Tag
- Adult MeSH
- Middle Aged MeSH
- Humans MeSH
- Adolescent MeSH
- Male MeSH
- Aged MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
The May 2022 proposal from the European commission for a 'European health data space' envisages advantages for health from exploiting the growing mass of health data in Europe. However, key stakeholders have identified aspects that demand clarification to ensure success. Data will need to be freed from traditional silos to flow more easily and to cross artificial borders. Wide engagement will be necessary among healthcare professionals, researchers, and the patients and citizens that stand to gain the most but whose trust must be won if they are to allow use or transfer of their data. This paper aims to alert the wider scientific community to the impact the ongoing discussions among lawmakers will have. Based on the literature and the consensus findings of an expert multistakeholder panel organised by the European Alliance for Personalised Medicine (EAPM) in June 2022, it highlights the key issues at the intersection of science and policy, and the potential implications for health research for years, perhaps decades, to come.
- Publication type
- Journal Article MeSH
1 online zdroj
- MeSH
- Big Data MeSH
- Medical Informatics * MeSH
- Health Equity MeSH
- Public Health MeSH
- Publication type
- Periodical MeSH
- Conspectus
- Veřejné zdraví a hygiena
- NML Fields
- lékařská informatika
- veřejné zdravotnictví
376 stran
A major prospect of Medical Informatics are methods, tools and infrastructures to support data-driven medical care and biomedical research. We need new concepts for application systems that also care for transparency which data from which sources in which quality build the evidence base for individual decisions. These systems have to be able to také into account patient generated data, patient preferences, and environmental contexts for an individualized decision that really meets the patients’ expectations, values and needs. Currently, these soft factors for medical decision support are not sufficiently understood and researched inall its dimensions.
- MeSH
- Medical Informatics * methods trends education MeSH
- Medical Informatics Computing MeSH
- Publication type
- Autobiography MeSH
- Geographicals
- Germany MeSH
