e.g. mhealth
Search Results (1 to 10 of 73 Results)
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Skip search results from other journals and go to results- 20 Journal of Medical Internet Research
- 7 JMIR AI
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To develop and evaluate prediction models capable of predicting the number of short-term discharges (ie, in the next 1 to 4 hours) in near real time, we must first decide the granularity level we want to predict. The literature has considered 2 main types of models: patient-level models [7-11] and hospital-level models [12,13]. Hospital-level models typically use a time series approach to directly predict the number of discharges in an entire hospital or a hospital unit such as the ED [12,13].
J Med Internet Res 2025;27:e63765
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Predictive Modeling of Hypertension-Related Postpartum Readmission: Retrospective Cohort Analysis
We used a cost-sensitive random forest method to predict which patient would experience a hypertension-related postpartum readmission [14]. Since the data set was imbalanced (only 170 readmissions out of 32,645 participants), the use of class weights that penalize false negatives significantly higher than false positives was necessary to avoid ML models that predict every sample as the negative class.
JMIR AI 2024;3:e48588
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First, prediction modeling validation studies using machine learning methods, statistical models, and biomarkers to predict postoperative infections were identified. Second, a separate search was performed to identify studies on automated surveillance for postoperative and other hospital-acquired infections (Figure 1). Surveillance studies focusing on surgical populations often only investigate SSIs.
JMIR Med Inform 2024;12:e57195
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The second review investigates studies integrating machine learning with RPM to predict AECOPD. This comprehensive approach enables us to provide a novel understanding of digitally enabled AECOPD interventions. We review the evidence and concepts behind RPM and machine learning; discuss the strengths, limitations, and clinical applications of available systems; and generate recommendations to enhance patient and health care system outcomes.
J Med Internet Res 2024;26:e52143
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In this study of Cook County, IL, we sought to enable a neighborhood-focused prevention approach by creating a semiautomated method to precisely describe where SUID occurred in the recent past (2015‐2019) and to predict where SUID would occur in the near future (2021‐2025) while pointing to social vulnerability indicators as explanatory variables.
JMIR Public Health Surveill 2024;10:e48825
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Predicting Long COVID in the National COVID Cohort Collaborative Using Super Learner: Cohort Study
Given this heterogeneity, multisite evaluations including large sample sizes and high-dimensional covariate information can provide opportunities to build models that can accurately predict PASC risk.
Due to the broad range of factors associated with PASC, the high dimensionality of the large EHR databases, and the unknown determinants of PASC, modeling methods for predicting PASC must be highly flexible.
JMIR Public Health Surveill 2024;10:e53322
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Understanding the patterns of and changes in social vulnerability that predict an individual’s capability to comply with the WHO advice in these countries will provide beneficial information to guide COVID-19 prevention, especially among vulnerable populations.
JMIR Public Health Surveill 2024;10:e54383
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The study’s outcomes were considered promising, as the developed model demonstrated the ability to predict severe AEs induced by antineoplastic drugs in patients with cancer. The researchers concluded that the use of automated triggers and ML is a promising approach to identifying risk factors for severe events, in contrast to the manual and retrospective method which aims to track events that have already occurred.
JMIR Res Protoc 2024;13:e55466
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We develop an explainable, machine-learning model to predict pediatric patients with T1 D who are at risk of DKA hospitalization postdiagnosis using a time-series of routinely collected, EHR data. We evaluate the predictive performance of our gradient-boosted decision tree model (XGBoost) on one of the largest cohorts of pediatric patients with T1 D.
JMIR Diabetes 2024;9:e53338
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Predictive Data Analytics in Telecare and Telehealth: Systematic Scoping Review
Using larger and more diverse “real world” data will enable models to be built that have less bias, can predict more accurately, and could be adapted more widely within other telecare or telehealth settings. Ultimately, appropriate consideration of these factors could lead us to more predictive and preventative data driven models of telecare and telehealth.
Online J Public Health Inform 2024;16:e57618
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