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  1. 37441
    “…RESULTS: We obtained the highest area under the curve (0.796) in medical visit prediction with our random forests model and daywise features. Ablating feature categories one at a time showed that the model performance worsened the most when location features were dropped. …”
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    Impact of Predicting Health Care Utilization Via Web Search Behavior: A Data-Driven Analysis
  2. 37442
    “…No specific instructions were given to the participants regarding phone placement. We used random forest classifiers to develop both personalized and global predictors of sleep state from the phone sensor data. …”
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    Scalable Passive Sleep Monitoring Using Mobile Phones: Opportunities and Obstacles
  3. 37443
    “…For An. albimanus mosquitoes (from the Pacific coast, Mexican gulf and Lacandon Forest lowlands), these two parameters were higher in specimens infected with P. vivax Vk210/Pvs25-A versus Vk210/Pvs25-B or Vk247/Pvs25-B (P < 0.001). …”
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    Plasmodium vivax CSP-Pvs25 variants from southern Mexico produce distinct patterns of infectivity for Anopheles albimanus versus An. pseudopunctipennis, in each case independent of geographical origin
  4. 37444
    “…The relationship between HbA1c and bone biochemical markers was analyzed by multivariate regression, forest plot and fitted curve. RESULTS: Bone formation markers including N-MID osteocalcin and procollagen type 1 amino-terminal pro-peptide (PINP) were decreased in postmenopausal women with T2DM compared to controls (17.42 ± 9.50 vs 23.67 ± 7.58, p < 0.001; 48.47 ± 27.27 vs 65.86 ± 21.06, p < 0.001, respectively), but the bone resorption markers β-crossLaps (β-CTX) was no difference between the two groups (0.57 ± 0.28 vs 0.55 ± 0.21, p = 0.868). …”
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    Association between glycosylated hemoglobin A1c and bone biochemical markers in type 2 diabetic postmenopausal women: a cross-sectional study
  5. 37445
    “…Passive digital phenotypes were processed into 130 features based on circadian rhythms, and a mood prediction algorithm was developed by random forest. RESULTS: The mood state prediction accuracies for the next 3 days in all patients, MDD patients, BD I patients, and BD II patients were 65%, 65%, 64%, and 65% with 0.7, 0.69, 0.67, and 0.67 area under the curve (AUC) values, respectively. …”
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    Mood Prediction of Patients With Mood Disorders by Machine Learning Using Passive Digital Phenotypes Based on the Circadian Rhythm: Prospective Observational Cohort Study
  6. 37446
    “…The machine learning models adopted convolutional neural network (CNN), random forest, support vector machine, and logistic regression. …”
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    Natural Language Processing for the Identification of Silent Brain Infarcts From Neuroimaging Reports
  7. 37447
    “…METHODS: Patient-level diagnostic, behavioral, demographic, and past visit history data extracted from structured datasets were merged with outcome variables extracted from unstructured free-text datasets and were used to train random forest decision models that predicted the need of advanced care for depression across (1) the overall patient population and (2) various subsets of patients at higher risk for depression-related adverse events; patients with a past diagnosis of depression; patients with a Charlson comorbidity index of ≥1; patients with a Charlson comorbidity index of ≥2; and all unique patients identified across the 3 above-mentioned high-risk groups. …”
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    Identification of Patients in Need of Advanced Care for Depression Using Data Extracted From a Statewide Health Information Exchange: A Machine Learning Approach
  8. 37448
    “…We used random-effect models for the pooled analysis and assessed statistical heterogeneity by visual inspection of a forest plot and calculation of the I(2) statistic. …”
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    Health Professions Digital Education on Antibiotic Management: Systematic Review and Meta-Analysis by the Digital Health Education Collaboration
  9. 37449
    por Gessain, Antoine
    Publicado 2013
    “…In this review, after an introduction on emerging viruses, we will briefly present the results of a large epidemiological study performed in groups of Bantus and Pygmies living in villages and settlements located in the rain forest of the South region of Cameroon. These populations are living nearby the habitats of several monkeys and apes, often naturally infected by different retroviruses including SIV, STLV and simian foamy virus. …”
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    Mécanismes d’émergence virale et transmission interespèces : l’exemple des rétrovirus Foamy simiens chezl’Homme en Afrique Centrale
  10. 37450
    “…Standard error, upper and lower confidence intervals at 95% confidence interval for the risk were obtained using STATA Version 15 which was also used to generate forest plots for pooled analysis. The random or fixed effect model was applied depending on the heterogeneity (I(2)). …”
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    Risk of rebleeding from gastroesophageal varices after initial treatment with cyanoacrylate; a systematic review and pooled analysis
  11. 37451
    por Atabaki-Pasdar, Naeimeh, Ohlsson, Mattias, Viñuela, Ana, Frau, Francesca, Pomares-Millan, Hugo, Haid, Mark, Jones, Angus G., Thomas, E. Louise, Koivula, Robert W., Kurbasic, Azra, Mutie, Pascal M., Fitipaldi, Hugo, Fernandez, Juan, Dawed, Adem Y., Giordano, Giuseppe N., Forgie, Ian M., McDonald, Timothy J., Rutters, Femke, Cederberg, Henna, Chabanova, Elizaveta, Dale, Matilda, Masi, Federico De, Thomas, Cecilia Engel, Allin, Kristine H., Hansen, Tue H., Heggie, Alison, Hong, Mun-Gwan, Elders, Petra J. M., Kennedy, Gwen, Kokkola, Tarja, Pedersen, Helle Krogh, Mahajan, Anubha, McEvoy, Donna, Pattou, Francois, Raverdy, Violeta, Häussler, Ragna S., Sharma, Sapna, Thomsen, Henrik S., Vangipurapu, Jagadish, Vestergaard, Henrik, ‘t Hart, Leen M., Adamski, Jerzy, Musholt, Petra B., Brage, Soren, Brunak, Søren, Dermitzakis, Emmanouil, Frost, Gary, Hansen, Torben, Laakso, Markku, Pedersen, Oluf, Ridderstråle, Martin, Ruetten, Hartmut, Hattersley, Andrew T., Walker, Mark, Beulens, Joline W. J., Mari, Andrea, Schwenk, Jochen M., Gupta, Ramneek, McCarthy, Mark I., Pearson, Ewan R., Bell, Jimmy D., Pavo, Imre, Franks, Paul W.
    Publicado 2020
    “…We applied LASSO (least absolute shrinkage and selection operator) to select features from the different layers of omics data and random forest analysis to develop the models. The prediction models included clinical and omics variables separately or in combination. …”
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    Predicting and elucidating the etiology of fatty liver disease: A machine learning modeling and validation study in the IMI DIRECT cohorts
  12. 37452
    “…Algorithms were developed using classification trees, random forests, and rule-based methods, using electronic medical record (EMR) data, administrative data, or both. …”
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    Validation of a type 1 diabetes algorithm using electronic medical records and administrative healthcare data to study the population incidence and prevalence of type 1 diabetes in Ontario, Canada
  13. 37453
    “…Candidate machine learning models (random forest, support vector machine, adaptive boosting, extreme gradient boosting, and shallow neural network) were compared in 3 patient groups to evaluate the classification performance for predicting the subtherapeutic, normal therapeutic, and supratherapeutic patient states. …”
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    Toward Optimal Heparin Dosing by Comparing Multiple Machine Learning Methods: Retrospective Study
  14. 37454
    “…Qualitative variables were summarized with frequencies, whereas quantitative variables with central and variability indicators depending on their parametric distribution. Forest plots were used to describe point estimates and in-between studies variability. …”
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    COVID-19 in pregnant women: A systematic review and meta-analysis
  15. 37455
    “…Comparison analysis showed that nonlinear models (K-nearest neighbor AUC 0.908, random forest AUC 0.938) outperform linear models (logistic regression AUC 0.865) on the same datasets, and machine-learning methods significantly surpassed traditional risk scales or fixed models (eg, Framingham cardiovascular disease risk models). …”
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    Accurate Prediction of Coronary Heart Disease for Patients With Hypertension From Electronic Health Records With Big Data and Machine-Learning Methods: Model Development and Performance Evaluation
  16. 37456
    “…R version 3.4.3 software was used for the meta-analysis. A forest plot and I(2) test were done to assess heterogeneity. …”
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    Magnitude and trend of perinatal mortality and its relationship with inter-pregnancy interval in Ethiopia: a systematic review and meta-analysis
  17. 37457
    “…The estimates of the different proportions in each analysis group that were visually summarized in a forest plot showed the odds ratio (OR) and 95% confidence interval (CI). …”
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    Summary of discordant results between rapid diagnosis tests, microscopy, and polymerase chain reaction for detecting Plasmodium mixed infection: a systematic review and meta-analysis
  18. 37458
    “…Data will be analysed using statistical software and presented in evidence tables and in meta-analytic forest plots. DISCUSSION: This protocol is developed to systematically review the literature on the prevalence and severity of anaemia, risk factors and outcomes in pregnant women in South Africa. …”
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    The prevalence, risk factors and outcomes of anaemia in South African pregnant women: a protocol for a systematic review and meta-analysis
  19. 37459
    “…The optimized random forest classifier was able to distinguish between low-grade and high-grade fibrosis with excellent cross-validated accuracy in both the first and second analysis (AUC = 0.90, CI = 0.85–0.95 vs. …”
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    Three-dimensional CT texture analysis of anatomic liver segments can differentiate between low-grade and high-grade fibrosis
  20. 37460
    “…RESULTS: The areas under the receiver operating characteristic curve (AUROC) of external validation dataset for support vector machine (SVM), random forest, AdaBoost, k-nearest neighbors (kNN), naive Bayes (NB), decision tree, logistic regression (LR), eXtreme gradient boosting (XGBoost), and gradient boosting decision tree (GBDT) were 0.780, 0.657, 0.736, 0.669, 0.774, 0.614, 0.769, 0.742, and 0.757, respectively. …”
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    An Innovative Artificial Intelligence–Based App for the Diagnosis of Gestational Diabetes Mellitus (GDM-AI): Development Study
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