JMIR Formative Research

View query in Help articles search

Field

e.g. mhealth

Operator

Field

Criteria

e.g. mhealth

Search Results (1 to 10 of 239 Results)

Download search results: CSV END BibTex RIS

Sort by:

Machine Learning Models for Frailty Classification of Older Adults in Northern Thailand: Model Development and Validation Study

To further explain model performance, we also created model calibration plots and calculated secondary metrics of prediction models, including the confusion matrix and specificity, sensitivity, and predictive values. There was only missing data in participants' age in the internal validation dataset (4/2228, 0.18%); therefore, a complete case analysis was performed on the dataset.

Natthanaphop Isaradech, Wachiranun Sirikul, Nida Buawangpong, Penprapa Siviroj, Amornphat Kitro

JMIR Aging 2025;8:e62942

Download Citation: END BibTex RIS

Download PDF Download XML

Large-Scale Evaluation and Liver Disease Risk Prediction in Finland’s National Electronic Health Record System: Feasibility Study Using Real-World Data

Due to the seemingly complex and unclear nature of BMI and WHR interaction, we decided not to pursue WHR prediction for now. For the analysis purpose, we simulated WHR data effect on the CLiv D score risk model results in a few different scenarios. The first scenario involved obtaining the exact WHR data from the Kanta PDR, the second scenario involved the individual requesting their WHR data using the WHR groups, and the last scenario involved the individual measuring their exact WHR.

Viljami Männikkö, Janne Tommola, Emmi Tikkanen, Olli-Pekka Hätinen, Fredrik Åberg

JMIR Med Inform 2025;13:e62978

Download Citation: END BibTex RIS

Download PDF Download XML

Predicting Clinical Outcomes at the Toronto General Hospital Transitional Pain Service via the Manage My Pain App: Machine Learning Approach

The confusion matrix of the prediction results can be seen in Table 3. On average, the algorithm selected a mean of 88 (SD 13) out of the 245 features. The receiver operating characteristic curve is shown in Figure 3. The AUC was 0.82. Confusion matrix of the prediction results. The receiver operating characteristic curve. The area under the receiver operating characteristic curve (AUC) is 0.82.

James Skoric, Anna M Lomanowska, Tahir Janmohamed, Heather Lumsden-Ruegg, Joel Katz, Hance Clarke, Quazi Abidur Rahman

JMIR Med Inform 2025;13:e67178

Download Citation: END BibTex RIS

Download PDF Download XML

Personalized Predictions for Changes in Knee Pain Among Patients With Osteoarthritis Participating in Supervised Exercise and Education: Prognostic Model Study

To support shared decision-making, the acceptance, and participation rate of programs like GLA:D, we aimed to validate an existing model and introduce an updated concise personalized prediction model that estimates changes in knee pain intensity for patients with OA considering participation in the GLA:D program [20]. This paper follows the guidelines of TRIPOD (Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis) [21].

Mahdie Rafiei, Supratim Das, Mohammad Bakhtiari, Ewa Maria Roos, Søren T Skou, Dorte T Grønne, Jan Baumbach, Linda Baumbach

JMIR Rehabil Assist Technol 2025;12:e60162

Download Citation: END BibTex RIS

Download PDF Download XML

Enhancing Patient Outcome Prediction Through Deep Learning With Sequential Diagnosis Codes From Structured Electronic Health Record Data: Systematic Review

We used 4 main groups of keywords centered around DL techniques, EHRs, sequence, and prediction.

Tuankasfee Hama, Mohanad M Alsaleh, Freya Allery, Jung Won Choi, Christopher Tomlinson, Honghan Wu, Alvina Lai, Nikolas Pontikos, Johan H Thygesen

J Med Internet Res 2025;27:e57358

Download Citation: END BibTex RIS

Download PDF Download XML

A Hybrid Deep Learning–Based Feature Selection Approach for Supporting Early Detection of Long-Term Behavioral Outcomes in Survivors of Cancer: Cross-Sectional Study

The second stage was a DDN that replaces a wrapper method, where it further selects features from the ones selected by the multimetric, majority-voting filter to maximize prediction performance in ML classifiers.

Tracy Huang, Chun-Kit Ngan, Yin Ting Cheung, Madelyn Marcotte, Benjamin Cabrera

JMIR Bioinform Biotech 2025;6:e65001

Download Citation: END BibTex RIS

Download PDF Download XML

Predicting Readmission Among High-Risk Discharged Patients Using a Machine Learning Model With Nursing Data: Retrospective Study

We constructed unexpected readmission prediction models by dividing them into Model 1 and Model 2. Model 1 implemented an early readmission prediction model based on data from the first day of hospitalization to predict readmission early. Model 2 implemented a model to supplement data that should have been included in Model 1 based on all the data.

Eui Geum Oh, Sunyoung Oh, Seunghyeon Cho, Mir Moon

JMIR Med Inform 2025;13:e56671

Download Citation: END BibTex RIS

Download PDF Download XML

Machine Learning–Based Prediction of Substance Use in Adolescents in Three Independent Worldwide Cohorts: Algorithm Development and Validation Study

By identifying key predictors of adolescent substance use that remain consistent across diverse cultural contexts, this study aims to develop a prediction model adaptable to global public health initiatives. To validate its generalizability, the model was tested using datasets from two additional countries, highlighting its adaptability to diverse sociocultural environments [8].

Soeun Kim, Hyejun Kim, Seokjun Kim, Hojae Lee, Ahmed Hammoodi, Yujin Choi, Hyeon Jin Kim, Lee Smith, Min Seo Kim, Guillaume Fond, Laurent Boyer, Sung Wook Baik, Hayeon Lee, Jaeyu Park, Rosie Kwon, Selin Woo, Dong Keon Yon

J Med Internet Res 2025;27:e62805

Download Citation: END BibTex RIS

Download PDF Download XML

Development and Validation of a Machine Learning Algorithm for Predicting Diabetes Retinopathy in Patients With Type 2 Diabetes: Algorithm Development Study

Developing these models can allow for a better prediction of DR risk and improved screening efficiency. DR progression can be prevented using a DR management strategy that focuses on high-risk individuals. This retrospective study used data from 2 independent longitudinal cohorts as part of an observational study. Data were collected from a hospital between January 1, 2008, and December 31, 2022.

Sunyoung Kim, Jaeyu Park, Yejun Son, Hojae Lee, Selin Woo, Myeongcheol Lee, Hayeon Lee, Hyunji Sang, Dong Keon Yon, Sang Youl Rhee