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Association among cumulative risk as well as protective components together with mental hardship amid women army husband and wife.
OBJECTIVE To investigate the impact of a history of previous naturally conceived tubal ectopic pregnancy (TEP) on subsequent IVF/intracytoplasmic sperm injection (ICSI) pregnancy and perinatal outcomes. DESIGN Retrospective cohort study. SETTING Reproductive medicine center in a tertiary hospital. PATIENT(S) A total of 2,892 women with tubal infertility undergoing the first fresh IVF/ICSI cycle. INTERVENTION(S) Women were stratified into three groups according to the type of previous naturally conceived pregnancy TEP, intrauterine pregnancy (IUP), and no pregnancy. MAIN OUTCOMES MEASURE(S) Pregnancy and neonatal outcomes were analyzed for each cohort and stratified into the following categories based on female age less then 30 years, 30-35 years, and ≥35 years. RESULT(S) Of the 2,892 patients with tubal factor infertility, 511 (17.7%) women had a history of TEP, 1,044 (36.1%) had prior IUP, and 1,337 (46.2%) had never been pregnant. Couples with an initial TEP tended to be younger and had experienced a shorter duration of infertility. Across the whole cohort, the optimal live birth rate decreased in older age groups. Live birth rates stratified by maternal age ( less then 30, 30-35, ≥35 years) did not differ between the TEP group (59.9%, 53.7%, 45.5%) and the IUP (62.0%, 53.8%, 40.6%) and no pregnancy group (56.7%, 54.4%, 45.6%). This did not change after adjusting for confounders such as age and years of infertility. Previous treatment of TEP with salpingectomy, salpingostomy, or medical treatment did not significantly affect subsequent fertility outcomes. The rates of preterm and low birth weight after TEP were also not significantly higher than in women with a previous IUP. CONCLUSION(S) Fertility history, including previous TEP, does not influence the probability of live birth after IVF/ICSI in women with tubal factor infertility. BACKGROUND Cardiovascular disease and sustained high blood glucose (prediabetes) are established concurrent diagnoses. People with these concomitant conditions carry out self-care which is overt (e.g., daily weighing or taking a specific diet), plus there are also concealed facets of self-care (e.g., accessing information about diet or medications). Also of note is the need to 'work' to achieve a self-determined level of self-care. The 'work' put into self-care is currently under-reported when people discuss their progress with health professionals. OBJECTIVE Our research aimed to demonstrate that aspects of self-care are typically concealed. A further objective was to reveal the extent of 'work' dedicated to self-care. DESIGN Interviews were conducted with 23 participants to reveal their experiences of long-term conditions, cardiovascular disease and prediabetes. Interpretive description underpinned the development of a thematic representation of the data. SETTING AND PARTICIPANTS Recruitment was from a tertwork' of self-care when assessing, planning and implementing health care in any clinical setting. A important recommendation for nurses is to support people-as-patients, by encouraging self-determination and working with the preferences patients have for self-care, in order to enhance their quality of life while living with ill-health. AIM A new automatic method for detecting specific points and lines (straight and curves) in dental panoramic radiographies (orthopantomographies) is proposed, where the human knowledge is mapped to the automatic system. The goal is to compute relevant mandibular indices (Mandibular Cortical Width, Panoramic Mandibular Index, Mandibular Ratio, Mandibular Cortical Index) in order to detect the thinning and deterioration of the mandibular bone. Data can be stored for posterior massive analysis. METHODS Panoramic radiographies are intrinsically complex, including artificial structures, unclear limits in bony structures, jawbones with irregular curvatures and intensity levels, irregular shapes and borders of the mental foramen, irregular teeth alignments or missing dental pieces. An intelligent sequence of linked imaging segmentation processes is proposed to cope with the above situations towards the design of the automatic segmentation, making the following contributions (i) Fuzzy K-means classification for identth statistical studies based on the analysis of deterioration of bone structures with different levels of osteoporosis. All indices are computed inside two regions of interest, which tolerate flexibility in sizes and locations, making this process robust enough. CONCLUSIONS The proposed approach provides an automatic procedure able to process with efficiency and reliability panoramic X-Ray images for early osteoporosis detection. BACKGROUND The accuracy of a prognostic prediction model has become an essential aspect of the quality and reliability of the health-related decisions made by clinicians in modern medicine. Unfortunately, individual institutions often lack sufficient samples, which might not provide sufficient statistical power for models. One mitigation is to expand data collection from a single institution to multiple centers to collectively increase the sample size. However, sharing sensitive biomedical data for research involves complicated issues. Machine learning models such as random forests (RF), though they are commonly used and achieve good performances for prognostic prediction, usually suffer worse performance under multicenter privacy-preserving data mining scenarios compared to a centrally trained version. METHODS AND MATERIALS In this study, a multicenter random forest prognosis prediction model is proposed that enables federated clinical data mining from horizontally partitioned datasets. By using a novel datance. Various AI models are increasingly being considered as part of clinical decision-support tools. However, the trustworthiness of such models is rarely considered. Clinicians are more likely to use a model if they can understand and trust its predictions. Key to this is if its underlying reasoning can be explained. A Bayesian network (BN) model has the advantage that it is not a black-box and its reasoning can be explained. In this paper, we propose an incremental explanation of inference that can be applied to 'hybrid' BNs, i.e. those that contain both discrete and continuous nodes. The key questions that we answer are (1) which important evidence supports or contradicts the prediction, and (2) through which intermediate variables does the information flow. The explanation is illustrated using a real clinical case study. A small evaluation study is also conducted. Knee contact force (KCF) is an important factor to evaluate the knee joint function for the patients with knee joint impairment. However, the KCF measurement based on the instrumented prosthetic implants or inverse dynamics analysis is limited due to the invasive, expensive price and time consumption. In this work, we propose a KCF prediction method by integrating the Artificial Fish Swarm and the Random Forest algorithm. First, we train a Random Forest to learn the nonlinear relation between gait parameters (input) and contact pressures (output) based on a dataset of three patients instrumented with knee replacement. Then, we use the improved artificial fish group algorithm to optimize the main parameters of the Random Forest based KCF prediction model. The extensive experiments verify that our method can predict the medial knee contact force both before and after the intervention of gait patterns, and the performance outperforms the classical multi-body dynamics analysis and artificial neural network model.Modern computer technology sheds light on new ways of innovating Traditional Chinese Medicine (TCM). One method that gets increasing attention is the quantitative research method, which makes use of data mining and artificial intelligence technology as well as the mathematical principles in the research on rationales, academic viewpoints of famous doctors of TCM, dialectical treatment by TCM, clinical technology of TCM, the patterns of TCM prescriptions, clinical curative effects of TCM and other aspects. This paper reviews the methods, means, progress and achievements of quantitative research on TCM. In the core database of the Web of Science, "Traditional Chinese Medicine", "Computational Science" and "Mathematical Computational Biology" are selected as the main retrieval fields, and the retrieval time interval from 1999 to 2019 is used to collect relevant literature. It is found that researchers from China Academy of Chinese Medical Sciences, Zhejiang University, Chinese Academy of Sciences and other institutes have opened up new methods of research on TCM since 2009, with quantitative methods and knowledge presentation models. The adopted tools mainly consist of text mining, knowledge discovery, technologies of the TCM database, data mining and drug discovery through TCM calculation, etc. In the future, research on quantitative models of TCM will focus on solving the heterogeneity and incompleteness of big data of TCM, establishing standardized treatment systems, and promoting the development of modernization and internationalization of TCM. Auscultation of the lung is a conventional technique used for diagnosing chronic obstructive pulmonary diseases (COPDs) and lower respiratory infections and disorders in patients. In most of the earlier works, wavelet transforms or spectrograms have been used to analyze the lung sounds. However, an accurate prediction model for respiratory disorders has not been developed so far. In this paper, a pre-trained optimized Alexnet Convolutional Neural Network (CNN) architecture is proposed for predicting respiratory disorders. The proposed approach models the segmented respiratory sound signal into Bump and Morse scalograms from several intrinsic mode functions (IMFs) using empirical mode decomposition (EMD) method. From the extracted intrinsic mode functions, the percentage energy calculated for each wavelet coefficient in the form of scalograms are computed. Subsequently, these scalograms are given as input to the pre-trained optimized CNN model for training and testing. Stochastic gradient descent with momentum (SGDM) and adaptive data momentum (ADAM) optimization algorithms were examined to check the prediction accuracy on the dataset comprising of four classes of lung sounds, normal, crackles (coarse and fine), wheezes (monophonic & polyphonic) and low-pitched wheezes (Rhonchi). selleck chemical On comparison to the baseline method of standard Bump and Morse wavelet transform approach which produced 79.04 % and 81.27 % validation accuracy, an improved accuracy of 83.78 % is achieved by the virtue of scalogram representation of various IMFs of EMD. Hence, the proposed approach achieves significant performance improvement in accuracy compared to the existing state-of- the-art techniques in literature. Tracking symptoms progression in the early stages of Parkinson's disease (PD) is a laborious endeavor as the disease can be expressed with vastly different phenotypes, forcing clinicians to follow a multi-parametric approach in patient evaluation, looking for not only motor symptomatology but also non-motor complications, including cognitive decline, sleep problems and mood disturbances. Being neurodegenerative in nature, PD is expected to inflict a continuous degradation in patients' condition over time. The rate of symptoms progression, however, is found to be even more chaotic than the vastly different phenotypes that can be expressed in the initial stages of PD. In this work, an analysis of baseline PD characteristics is performed using machine learning techniques, to identify prognostic factors for early rapid progression of PD symptoms. Using open data from the Parkinson's Progression Markers Initiative (PPMI) study, an extensive set of baseline patient evaluation outcomes is examined to isolate determinants of rapid progression within the first two and four years of PD.
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