Introduction: Effective coping mechanisms and available support systems are essential to managing the disease and maintaining the mental health of women with breast cancer. It has been found that spirituality and religion can be an important supportive element in coping with cancer and its consequences. Aim: To analyse spiritual and religious (s/r) interventions provided by health care professionals to breast cancer patients, and their relationship to physical health, psychosocial, and spiritual outcomes. Methods: Integrative literature review of systematic literature reviews and/or meta-analysis was chosen. PubMed and Web of Science databases for the period 2013-2023 after entering the keywords "spiritual, religious, existential, positive psychology, mindfulness, interventions, breast cancer, cancer" in English were searched. Results: The review included 13 systematic reviews and/or meta-analyses (SRMA). Effect of mindfulness intervention was most frequently analysed. S/r interventions significantly associated with improvements in spiritual and existential well-being, quality of life and personal well-being, hope, optimism, cognitive functions and reductions of anxiety, depression, hopelessness, stress, and fatigue. Two SRMA found that s/r interventions were associated with improvements in cortisol levels, inflammatory cytokine activity, and lymphocyte function. Conclusion: The analysed studies showed that s/r interventions are associated with improved biological, psychosocial and spiritual outcomes, which supports the application of these interventions in clinical practice.
- MeSH
- Complementary Therapies classification methods MeSH
- Quality of Life MeSH
- Humans MeSH
- Religion and Psychology MeSH
- Breast Neoplasms * psychology therapy MeSH
- Review Literature as Topic MeSH
- Psychotherapy methods MeSH
- Spiritual Therapies * classification methods MeSH
- Statistics as Topic MeSH
- Information Storage and Retrieval methods statistics & numerical data MeSH
- Mindfulness methods MeSH
- Check Tag
- Humans MeSH
- Female MeSH
- Publication type
- Review MeSH
- MeSH
- Data Management * methods standards organization & administration MeSH
- Diagnostic Imaging history methods instrumentation MeSH
- Medical Informatics * MeSH
- International Classification of Diseases MeSH
- Risk MeSH
- Information Storage and Retrieval history methods standards statistics & numerical data MeSH
- Artificial Intelligence MeSH
Background: Classifying diseases into ICD codes has mainly relied on human reading a large amount of written materials, such as discharge diagnoses, chief complaints, medical history, and operation records as the basis for classification. Coding is both laborious and time consuming because a disease coder with professional abilities takes about 20 minutes per case in average. Therefore, an automatic code classification system can significantly reduce the human effort. Objectives: This paper aims at constructing a machine learning model for ICD-10 coding, where the model is to automatically determine the corresponding diagnosis codes solely based on free-text medical notes. Methods: In this paper, we apply Natural Language Processing (NLP) and Recurrent Neural Network (RNN) architecture to classify ICD-10 codes from natural language texts with supervised learning. Results: In the experiments on large hospital data, our predicting result can reach F1-score of 0.62 on ICD-10-CM code. Conclusion: The developed model can significantly reduce manpower in coding time compared with a professional coder.
- MeSH
- Electronic Data Processing methods MeSH
- Deep Learning * MeSH
- Electronic Health Records MeSH
- International Classification of Diseases * MeSH
- Neural Networks, Computer MeSH
- Machine Learning MeSH
- Information Storage and Retrieval methods statistics & numerical data MeSH
- Data Visualization MeSH
- Natural Language Processing MeSH
- Publication type
- Research Support, Non-U.S. Gov't MeSH
- MeSH
- Acetabulum * surgery pathology MeSH
- Humans MeSH
- Orthopedic Procedures classification methods adverse effects statistics & numerical data MeSH
- Reoperation methods statistics & numerical data MeSH
- Information Storage and Retrieval methods statistics & numerical data MeSH
- Treatment Outcome MeSH
- Plastic Surgery Procedures classification methods adverse effects statistics & numerical data MeSH
- Check Tag
- Humans MeSH
- Publication type
- Meta-Analysis MeSH
Aim:To summarize relevant information on the creation and validation of the Quality of Nursing Diagnoses, Interventions and Outcomes (Q-DIO) assessment instrument. Design:A literature review. Methods: To search for studies, the first two steps ofastandard evidence-based healthcare approach were used: 1) formulation of a search question and 2) structured documented search including assessment of the relevance of abstractsand full text of studies to the search question and inclusion criteria. Inrelevant studies, the level of evidence was evaluated using the Joanna Briggs Institute categories. Results: Out of 212results of two-step search in scholarly databases and grey literature sources, only three literature resources were relevant. The newly developed Q-DIO instrument was assessed by its authors using content and face validation (100% consensus of experts and 88.25% agreement), intrarater (r = 0.98; p <0.0001;κ=0.95;p< 0.0001) and interrater reliability (r=0.99; p< 0.0001, κ=0.95;p< 0.0001) and internal consistency of the concepts of the instrument (Cα = 0.83; Cα = 0.98; Cα = 0.90;Cα=0.99). Item analysis was carried out to determine both difficulty and discriminative validity of the items. Conclusion: Q-DIO was by the authors marked as valid assessment instrument by its authors. However, since the assessed instrument items are formulatedwithout identifying currently valid NNN nomenclature terminology, they should be specified prior to a particular assessment ofthe quality of nursing documentation.
Od září 2015 provozuje Evropská léková agentura systém Medical Literature Monitoring (MLM) na sledování literatury pro 300 farmaceutických a 100 rostlinných látek a jejich kombinací. Byla hodnocena úplnost záznamů z českých farmaceutických časopisů v hlavní zdrojové databázi EMBASE a následně v MLM. Určité nedostatky jsou uvedeny na konkrétních příkladech. Některé hlásitelné účinné látky v kazuistikách chybí jak v EMBASE (používající pouze autorský souhrn a nikoli plný text článku s popisem případu), tak i v MLM (omezujícím navíc výběr pouze na 300 látek).
Since September 2015, European Medicines Agency has been providing Medical Literature Monitoring (MLM) for selected 300 pharmaceutical and 100 herbal substances and their combinations. Completeness of the records from Czech pharmaceutical journals in the main source database EMBASE and subsequently in MLM was analyzed. Some deficiencies are given on concrete examples. Some reportable active substances in case reports are missing in both EMBASE (using only author abstract and not the full article text describing the case report) and MLM (limiting in addition the choice to 300 substances only).
- Keywords
- Medical Literature Monitoring, Evropská léková agentura, EMBASE, monitorování literatury,
- MeSH
- Databases as Topic statistics & numerical data MeSH
- Pharmacovigilance * MeSH
- International Agencies MeSH
- Publications MeSH
- Statistics as Topic MeSH
- Information Storage and Retrieval methods statistics & numerical data MeSH
- Legislation, Drug MeSH
- Health Information Systems * statistics & numerical data MeSH
- MeSH
- Medical Records Systems, Computerized economics organization & administration manpower trends utilization MeSH
- Confidentiality legislation & jurisprudence MeSH
- Health Care Economics and Organizations MeSH
- Electronic Prescribing MeSH
- Electronic Health Records economics organization & administration manpower instrumentation statistics & numerical data utilization MeSH
- Patient Identification Systems economics methods trends utilization MeSH
- Information Systems trends MeSH
- Delivery of Health Care, Integrated economics methods organization & administration statistics & numerical data utilization MeSH
- Medical Informatics methods organization & administration manpower instrumentation statistics & numerical data MeSH
- Medical Informatics Computing economics statistics & numerical data trends utilization MeSH
- Humans MeSH
- Hospital Records standards legislation & jurisprudence MeSH
- Delivery of Health Care economics standards legislation & jurisprudence MeSH
- Information Storage and Retrieval economics methods statistics & numerical data utilization MeSH
- Check Tag
- Humans MeSH
- Publication type
- Newspaper Article MeSH
V oblasti onkologie běží v České republice tři screeningové programy. Nejstarším je mamografický screening karcinomu prsu, druhým screening kolorektálního karcinomu a nejmladším je screening karcinomu děložního čípku. Institut biostatistiky a analýz Masarykovy univerzity (IBA) zajišťuje IT a analytickou podporu všech těchto programů, která spočívá především v elektronickém sběru a vyhodnocování dat s cílem monitorovat efektivnost programu a dodávat podklady pro akreditaci zúčastněných center. Jelikož každý program má svá specifika, bylo pro každý z nich navrženo specifické řešení sběru a centralizace dat. Centrální databáze obsahuje k únoru 2014 anonymní data o více než 4,5 milionech screeningových mamografiích, 130 tisících kolonoskopiích a o 11 milionech cytologických vyšetřeních. Podrobnosti o screeningových programech včetně analytických výstupů jsou uveřejňovány na webových portálech. Výzvou zůstává zvyšování kvality sbíraných dat a zefektivnění procesu sběru dat.
- MeSH
- Databases, Factual * MeSH
- Colorectal Neoplasms epidemiology MeSH
- Humans MeSH
- Uterine Cervical Neoplasms epidemiology MeSH
- Breast Neoplasms MeSH
- Mass Screening * methods organization & administration statistics & numerical data MeSH
- Registries * statistics & numerical data MeSH
- Data Collection * methods MeSH
- Information Storage and Retrieval methods statistics & numerical data MeSH
- Check Tag
- Humans MeSH
