Search Articles

View query in Help articles search

Search Results (1 to 10 of 10 Results)

Download search results: CSV END BibTex RIS


Predicting Overall Survival in Patients with Male Breast Cancer: Nomogram Development and External Validation Study

Predicting Overall Survival in Patients with Male Breast Cancer: Nomogram Development and External Validation Study

First, variables that had a significance value of P The performance of the nomogram was evaluated through internal and external validations. Bootstrapping was used to perform 1000 instances of resampling to internally validate the predictive performance of the nomogram to ensure the stability and reliability of the model’s performance. The discrimination of the nomogram was assessed using the Concordance index (C-index) and receiver operating characteristic (ROC) curve.

Wen-Zhen Tang, Shu-Tian Mo, Yuan-Xi Xie, Tian-Fu Wei, Guo-Lian Chen, Yan-Juan Teng, Kui Jia

JMIR Cancer 2025;11:e54625

Development and Validation of a Predictive Model Based on Serum Silent Information Regulator 6 Levels in Chinese Older Adult Patients: Cross-Sectional Descriptive Study

Development and Validation of a Predictive Model Based on Serum Silent Information Regulator 6 Levels in Chinese Older Adult Patients: Cross-Sectional Descriptive Study

In this study, we aimed to develop a nomogram for predicting outcomes in older adults presenting with clinical symptoms suggestive of CAD. We hypothesized that a combination of baseline SIRT6 levels with clinical parameters could improve the evidence-based selection of candidates for this marker and facilitate clinical decision-making, resulting in its potential implementation in clinical trials.

Yuzi You, Wei Liang, Yajie Zhao

JMIR Aging 2025;8:e64374

Building Dual AI Models and Nomograms Using Noninvasive Parameters for Aiding Male Bladder Outlet Obstruction Diagnosis and Minimizing the Need for Invasive Video-Urodynamic Studies: Development and Validation Study

Building Dual AI Models and Nomograms Using Noninvasive Parameters for Aiding Male Bladder Outlet Obstruction Diagnosis and Minimizing the Need for Invasive Video-Urodynamic Studies: Development and Validation Study

LR models and nomograms were developed using R (version 4.1.1; R Core Team) with the “glmnet” and “rms” packages for model fitting and nomogram visualization [19]. Python’s “scikit-learn” library (version 0.24.2) was used for supplementary ML and validation tasks [20]. In addition, SPSS Statistics (version 25; IBM Corp) was used for statistical testing [21].

Chung-You Tsai, Jing-Hui Tian, Chien-Cheng Lee, Hann-Chorng Kuo

J Med Internet Res 2024;26:e58599

Predicting the Transition to Metabolically Unhealthy Obesity Among Young Adults With Metabolically Healthy Obesity in South Korea: Nationwide Population-Based Study

Predicting the Transition to Metabolically Unhealthy Obesity Among Young Adults With Metabolically Healthy Obesity in South Korea: Nationwide Population-Based Study

This study identified risk factors and developed a screening tool with a nomogram to prevent the transition from MHO to MUO. A nomogram, a graphical tool that can perform a complicated approximation calculation, provided a clear interpretation of which predictors could be more critical factors [13]. The use of this scale also allows the general population to intuitively and easily assess the likelihood of transitioning to MUO.

HyunHae Lee, Ji-Su Kim, Hyerine Shin

JMIR Public Health Surveill 2024;10:e52103

Clinical Timing-Sequence Warning Models for Serious Bacterial Infections in Adults Based on Machine Learning: Retrospective Study

Clinical Timing-Sequence Warning Models for Serious Bacterial Infections in Adults Based on Machine Learning: Retrospective Study

Additionally, to enhance practical usability for health care professionals globally and enable open validation by peers, we have implemented the 2 nomogram tools online using Dyn Nom [23,24]. Through this platform, the SBI risk score can be automatically generated and visually displayed. In this study, a total of 945 patients clinically diagnosed with infective fever and possessing complete clinical data were included. Table 1 presents the demographic characteristics of these patients.

Jian Liu, Jia Chen, Yongquan Dong, Yan Lou, Yu Tian, Huiyao Sun, Yuqing Jin, Jingsong Li, Yunqing Qiu

J Med Internet Res 2023;25:e45515

Pilot Project for a Web-Based Dynamic Nomogram to Predict Survival 1 Year After Hip Fracture Surgery: Retrospective Observational Study

Pilot Project for a Web-Based Dynamic Nomogram to Predict Survival 1 Year After Hip Fracture Surgery: Retrospective Observational Study

One example is the Dyn Nom package [7] in R that predisplays the results of statistical models as a dynamic nomogram and readily allows individual prediction with 95% CI. Anesthetic guidelines and protocols increasingly drive standardization of practice [10]. However, we believe that individual identification of risk is more likely to improve outcomes [11].

Graeme McLeod, Iain Kennedy, Eilidh Simpson, Judith Joss, Katriona Goldmann

Interact J Med Res 2022;11(1):e34096

A Risk Prediction Model Based on Machine Learning for Cognitive Impairment Among Chinese Community-Dwelling Elderly People With Normal Cognition: Development and Validation Study

A Risk Prediction Model Based on Machine Learning for Cognitive Impairment Among Chinese Community-Dwelling Elderly People With Normal Cognition: Development and Validation Study

In addition, if logistic regression performs well compared with the other three methods, we will formulate a nomogram based on the result of logistic regression for practical use. The nomogram works by proportionally converting each regression coefficient into a 0 to 100-point scale, with 100 points being the highest β coefficient. The points across each independent variable are added to derive total points, which are translated to predicted probabilities [28].

Mingyue Hu, Xinhui Shu, Gang Yu, Xinyin Wu, Maritta Välimäki, Hui Feng

J Med Internet Res 2021;23(2):e20298

Nomogram for Predicting COVID-19 Disease Progression Based on Single-Center Data: Observational Study and Model Development

Nomogram for Predicting COVID-19 Disease Progression Based on Single-Center Data: Observational Study and Model Development

Nomogram, calibration, and receiver operating characteristic (ROC) curves were established using R version 3.6.1 (R Project). The risk score was calculated using multivariate Cox regression. The cutoff values of independent risk factors were based on the maximum Youden index. A 2-sided α All 175 patients in this study were confirmed to be infected with SARS-Co V-2. The average age of the 175 patients was 46.9 years (SD 17.33).

Tao Fan, Bo Hao, Shuo Yang, Bo Shen, Zhixin Huang, Zilong Lu, Rui Xiong, Xiaokang Shen, Wenyang Jiang, Lin Zhang, Donghang Li, Ruyuan He, Heng Meng, Weichen Lin, Haojie Feng, Qing Geng

JMIR Med Inform 2020;8(9):e19588

Diagnostic Model for In-Hospital Bleeding in Patients with Acute ST-Segment Elevation Myocardial Infarction: Algorithm Development and Validation

Diagnostic Model for In-Hospital Bleeding in Patients with Acute ST-Segment Elevation Myocardial Infarction: Algorithm Development and Validation

We constructed the nomogram (Figure 3) using the development database based on an independent prognostic marker (age) and a rank variable (Killip classification). To use the nomogram, the patient’s age is found on the AGE axis, and a straight line is then drawn upward to the Points axis to determine how many points toward progression the patient receives for their age. The steps are repeated for the other axes, with a straight line drawn upward each time toward the points axis.

Yong Li

JMIR Med Inform 2020;8(8):e20974