And heterogeneity

The gap

Evidence profile

Sourced from the limitations of the source papers, classified as general, spanning 3 journals. Those papers have been cited 1 times in total.

Supporting evidence — 3 representative gaps

• Development and validation of an artificial intelligence-based model for predicting teicoplanin plasma concentrations in intensive care unit patients with pulmonary infections: a retrospective study (2026) · International Journal of Clinical Pharmacy · doi

  This study utilized a large real-world dataset and incorpo- rated both internal and external validations, reinforcing the model’s reliability and generalizability. Multiple ML algo- rithms were systematically compared, and feature interpret- ability was achieved using the SHAP analysis to ensure transparency and clinical relevance. Nonetheless, this study has several limitations. First, its retrospective design and modest sample size may limit external generalizability. Second, missing data were imputed using the RF algorithm, which could have introduced resid- ual bias. Although RF can handle some non-random miss- ing data, they still implicitly assume the hypothesis that "given the observed data, the missing values are random". If the missing mechanism is very complex and related to unobserved factors (non-negligible missing data), the fill- ing values may be biased. Therefore, we suggest that the results after filling be regarded as the best estimate under specific assumptions (that the missing data can be explained by the observed variables), and its robustness can be fur- ther verified through sensitivity analysis in future studies. Third, time-varying clinical covariates, such as dynamic renal function or medication changes, were not modeled and pharmacogenomic data were unavailable, potentially limit- ing the predictive scope. Finally, the clinical effectiveness of the model in improving outcomes requires prospective validation. In the future, the decision support function of the prediction tools needs to be further optimized, includ- ing: (1) Clearly define the target blood concentration range for teicoplanin treatment for ICU patients with pulmonary infections (set the target concentration based on the severity of infection, the type of pathogen, and the patient's renal function status); (2) Incorporate a dose calculation module, based on the difference between the predicted concentration and the target concentration, combined with patient weight, renal function and other parameters, automatically gener- ate individualized medication recommendation suggestions (such as dose adjustment range, optimization of administra- tion interval); (3) Conduct multi-center prospective clini- cal trials to verify the application effect of the tool in real clinical scenarios, and evaluate its impact on the infection cure rate of patients, the incidence of nephrotoxicity and the efficiency of antibiotic use.

  generallimitationsevidence 5/5
  Keywords: missing clinical function concentration renal target real external model generalizability using limit random observed values
• Prediction of in-hospital mortality risk in cardiac arrest patients using machine learning models: a study based on the MIMIC-IV database with external validation from Yunnan University Affiliated Hospital (2026) · BMC Medical Informatics and Decision Making · cited 1× · doi

  A major strength of this study is its use of a large, well- curated database for model development and the inclu- sion of an independent dataset for validation, ensuring robust and generalizable findings. The incorporation of diverse clinical variables and rigorous statistical analysis further strengthens the reliability of our results. How- ever, several limitations warrant consideration. First, the retrospective nature of the study may introduce bias, as it relies on the availability and accuracy of recorded data. Second, despite efforts to address missing data through imputation, residual data quality issues may still impact the models’ performance. Finally, the relatively small sample size of the external validation cohort may limit the applicability of the findings to broader populations.

  generallimitationsevidence 5/5
  Keywords: validation major strength large well curated database model development inclu sion independent dataset ensuring robust
• Development and validation of machine learning-based prediction for in-hospital mortality in ICU patients with severe community-acquired pneumonia and respiratory failure (2026) · Frontiers in Medicine · doi

  and heterogeneity. record data may be subject In addition, net performance on the training set compared with the validation set suggests that residual overfitting cannot be fully excluded. to Third, electronic medical missingness benefit estimates at very high threshold probabilities may be less stable in a small validation cohort due to sparse observations at extreme predicted risks. Future studies should include larger multicenter prospective cohorts for external validation, further calibration refinement (especially at probability extremes), and evaluation of real-world effectiveness when integrated into clinical workflows.

  generallimitationsevidence 4/5
  Keywords: validation heterogeneity record subject addition performance training compared suggests residual tting cannot fully excluded third

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