Optimising coronary imaging decisions with machine learning: an external validation study by Floor Groepenhoff, Leonard Hofstra, Sophie Heleen Bots, Saskia Haitjema, Imo Hoefer, L. Malin Overmars, Bram van Es, Mark C. H. De Groot, G. Aernout Somsen, I. Igor Tulevski, Hester M. den Ruijter, Wouter W. van Solinge

“…The outcome was defined as the absence of coronary stenosis, identified through text mining of radiology report conclusions, and predictive performance was assessed by negative predictive values (NPVs) and specificities.Results On the training cohort (9298 men (median age 55 years, 73% no coronary stenosis) and 5376 women (median age 59 years, 83% no coronary stenosis)), the algorithms showed NPVs and specificities of 0.95 and 0.14 in men and 0.93 and 0.26 in women, respectively. …”
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Optimization of chlorophyll extraction from mulberry leaves using response surface methodology by CHEN Shao-yuan, LÜ Zhen-er, DONG Feng-li, MAO Bi-zeng

“…Under the optimal extraction condition, the chlorophyll productivity was 5.376 mg/g, which was in consistent with the predicted value of 5.451 mg/g.In conclusion, the optimization of chlorophyll extraction by RSM is convenient, feasible and highly efficient.…”
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Lung Ultrasound Is Accurate for the Diagnosis of High-Altitude Pulmonary Edema: A Prospective Study by Weibo Yang, Yuliang Wang, Zewu Qiu, Xuewen Huang, Maoxia Lv, Bin Liu, Dingzhou Yang, Zhenhan Yang, Tingshan Xie

“…., their sensitivity, specificity, and positive and negative predictive values) were assessed, and the results were compared. …”
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ATRIAL FIBRILLATION AND FLUTTER IN HYPERTROPHIC CARDIOMYOPATHY by N. S. Krylova, A. E. Demkina, F. M. Khashieva, E. A. Kovalevskaya, N. G. Poteshkina

“…With the method of binary logistic regression 3 main variables were selected as the most of predictable value for AF: age, LA size, PGmax in OTLV. …”
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Prediction of caesarean section birth using machine learning algorithms among pregnant women in a district hospital in Ghana by Frederick Osei Owusu, Helena Addai-Manu, Esther Serwah Agbedinu, Emmanuel Konadu, Lydia Asenso, Mercy Addae, Joseph Osarfo, Brenda Abena Ampah, Douglas Aninng Opoku

“…Measures such as accuracy, sensitivity, specificity, negative and positive predictive values and area under the receiver operating characteristics curve (AUC-ROC) were used for the model performance. …”
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Relationship between diabetes-related clinical characteristics and preserved ratio impaired spirometry (PRISm): findings from NHANES 2007–2012 by Ting Yang, Chen Wang, Ke Huang, TingTing Huang, Xingyao Tang, Yanan Cui, Xu Chu, Yaodie Peng

“…PRISm was defined as having a forced expiratory volume in 1 s (FEV1) to forced vital capacity (FVC) ratio ≥0.7 and an FEV1 predicted value <80%. We examined the relationship between diabetes duration, fasting plasma glucose (FPG), glycated haemoglobin (HbA1c), log-transformed homeostasis model assessment for insulin resistance, C reactive protein and the number of comorbidities with PRISm in the entire population. …”
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Clinical impact of combined assessment of myocardial inflammation and fibrosis using myocardial biopsy in patients with dilated cardiomyopathy: a multicentre, retrospective cohort... by Toshihisa Anzai, Yasuo Sugano, Tetsuro Yokokawa, Yoshihiko Ikeda, Hatsue Ishibashi-Ueda, Kazufumi Nakamura, Hiromitsu Kanamori, Michiaki Hiroe, Kaoru Dohi, Takafumi Nakayama, Keiko Ohta Ogo, Kinta Hatakeyama, Yoshihiro Seo, Kyoko Imanaka-Yoshida

“…Here, we investigated the combined prognostic value of these two factors, as evaluated using myocardial biopsy samples.Methods This retrospective and multicentre study included patients with DCM—defined as LVEF of ≤45% and left diastolic diameter of >112% of predicted value, without evidence of secondary or ischaemic cardiomyopathy. …”
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Validation of an Automated High-Throughput Multiplex Real-Time PCR Assay for Detection of Enteric Protozoa by Rachel Lau, Jason Kwan, Kimberley Marks-Beaubrun, Ruben Cudiamat, Min Qun Ellen Chen, Krista Orejana, Filip Ralevski, Andrea K. Boggild

“…<b>Results</b>: Among 461 unpreserved fecal specimens, sensitivity, specificity, positive predictive and negative predictive values of the enteric multiplex for fresh specimens were as follows: 93%, 98.3%, 85.1%, 99.3% for Bh; 100% for all measures in <i>Cryptosporidium</i> and Cc; 100%, 99.3%, 88.5%, 100% for Df; 33.3%, 100%, 100%, 99.6% for Eh; and 100%, 98.9%, 68.8%, 100% for Gl, respectively. …”
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Improving prediction accuracy of hospital arrival vital signs using a multi-output machine learning model: a retrospective study of JSAS-registry data by Yasuyuki Kawai, Koji Yamamoto, Keisuke Tsuruta, Keita Miyazaki, Hideki Asai, Hidetada Fukushima

“…Model performance was assessed by comparing the predicted values with the actual hospital arrival measurements using mean absolute error, R² score, residual standard deviation, and Spearman’s correlation coefficient. …”
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Multicenter development of a deep learning radiomics and dosiomics nomogram to predict radiation pneumonia risk in non-small cell lung cancer by Xun Wang, Aiping Zhang, Huipeng Yang, Guqing Zhang, Junli Ma, Shucheng Ye, Shuang Ge

“…The calibration curve showed that the predicted value of DLRDN was in good agreement with the actual value. …”
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Analysis of factors affecting the accuracy of 18F-fluorodeoxyglucose positron emission tomography combined with computed tomography in case of suspected prosthetic valve infective... by I. P. Aslanidi, E. Z. Golukhova, D. M. Pursanova, O. V. Mukhortova, I. V. Shurupova, I. V. Ekaeva, T. A. Katunina, T. A. Trifonova

“…Thus, the sensitivity, specificity and diagnostic accuracy of PET/CT in the diagnosis of PVE were 92%, 67% and 84%, respectively; positive and negative predictive values — 85% and 80%. The analysis of the odds ratio did not reveal the relationship of low inflammatory activity, the interval between surgery and PET/CT from 3 to 6 months, and long-term ABT before PET/CT with false PET/CT results (p&gt;0,05). …”
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Improving chest pain risk assessment: validation of HEART, TIMI, GRACE, EDACS-ADP, and HET for MACE prediction in the emergency department by Mehdi Nasr Isfahani, Hamidreza Mohseni, Elahe Nasri Nasrabadi, Nizal Sarrafzadegan

“…Diagnostic performance of the risk scores was evaluated using receiver operating characteristic (ROC) curve analysis, including calculation of sensitivity, specificity, positive and negative predictive values, and likelihood ratios at clinically relevant cut-off thresholds. …”
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Building a risk prediction model for anastomotic leakage postoperative low rectal cancer based on Lasso-Logistic regression by Zhenhao Quan, Lin Lin, Renwei Huang, Kaiyu Sun, Feipeng Xu

“…H-L goodness of fit test showed that there was no significant difference between the predicted value of the model and the actual observed value (χ 2  = 6.438, P = 0.598). …”
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Cerebrospinal Fluid Leakage Combined with Blood Biomarkers Predicts Poor Wound Healing After Posterior Lumbar Spinal Fusion: A Machine Learning Analysis by Pang Z, Ou Y, Liang J, Huang S, Chen J, Huang S, Wei Q, Liu Y, Qin H, Chen Y

“…Calibration curve analysis showed good consistency between nomogram-predicted values and actual measurements.Conclusion: SLSI, albumin, postoperative glucose, CSFL, NEU and CRP were identified as significant risk factors for PWH after posterior lumbar spinal fusion. …”
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Validation of a novel questionnaire for assessing occupational exposure to organophosphate pesticides in Chilean agricultural workers by Liliana Zúñiga-Venegas, Liliana Zúñiga-Venegas, Natalia Landeros, Natalia Landeros, Floria Pancetti, Sandra Cortés, Sandra Cortés, Boris Lucero, Ana M. Brito, Ian S. Acuña-Rodríguez, María Teresa Muñoz-Quezada

“…Sensitivity, specificity, predictive values, and Receiver Operating Characteristic (ROC) curve analyses were performed to assess the accuracy.ResultsUrinary DAP levels and AChE inhibition increased in T1 (from 6.54 ± 4.66 to 12.39 ± 9.88 μg/g creatinine, p = 0.004, and from 2.26E-3±6.53E-4 to 1.44E-3±2.73E-4 mmol/min-1*mgProt-1, p &lt; 0.001, respectively), with AChE inhibition (30.99%) exceeding Chilean regulatory threshold. …”
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Effectiveness of 2 Just-in-Time Adaptive Interventions for Reducing Stress and Stabilizing Cardiac Autonomic Function: Microrandomized Trials by Andreas Richard Schwerdtfeger, Josef Martin Tatschl, Christian Rominger

“…This metric is quantified by regressing bodily movement on the root mean square of successive differences and identifying reductions <0.5 SD of the predicted value in real time in everyday life. …”
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Evaluation of the predictors of successful sperm retrieval of micro-TESE in cases with mosaic Klinefelter versus cases with non-mosaic Klinefelter: a prospective case series study by Amr Elahwany, Fatma A. Elrefaey, Hisham Alahwany, Hesham Torad, Sameh Fayek GamalEl Din, Rashad Mohammed Saeed Dawood, Mohamed Wael Ragab, Ahmed Fawzy Megawer

“…Moreover, the cutoff point and sensitivity and specificity and positive and negative predictive values for Rt and Lt testicular volumes were as follows 2 ml, 73.1%, 61.4%, 52.78, 79.41, 2 ml, 76.9%, 57.8%, 51.28 and 80.65, respectively. …”
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Improvement in genetic evaluation of quantitative traits in sheep by enriching genetic model with dominance effects by Farhad Ghafouri-Kesbi, Morteza Mokhtari, Mohsen Gholizadeh

“…The predictive ability of models was measured using the mean squared error of prediction (MSE) and Pearson’s correlation coefficient between the real and predicted values of records (r( $$\:y$$ , $$\:\widehat{y}$$ )). …”
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ARIMA Markov Model and Its Application of China’s Total Energy Consumption by Chingfei Luo, Chenzi Liu, Chen Huang, Meilan Qiu, Dewang Li

“…These states are then combined with a Markov transition probability matrix to determine the final predicted values. The ARIMAMKM model is validated using China’s energy consumption data, achieving high prediction accuracy as evidenced by metrics such as mean absolute percentage error (MAPE), root mean square error (RMSE), <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi mathvariant="normal">S</mi><mi mathvariant="normal">T</mi><mi mathvariant="normal">D</mi></mrow></semantics></math></inline-formula>, and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mrow><mi mathvariant="normal">R</mi></mrow><mrow><mn>2</mn></mrow></msup></mrow></semantics></math></inline-formula>. …”
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Prediction of soil chemical properties using multispectral satellite images and wavelet transforms methods by Chaitanya B. Pande, Sunil A. Kadam, Rajesh Jayaraman, Sunil Gorantiwar, Mukund Shinde

“…In this study, the neural network wavelet model was used to predicted values related to soil chemical properties in the semi-arid region. …”
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