Clinical Decision Support System for Diabetic Patients by Predicting Type 2 Diabetes Using Machine Learning Algorithms

Effectiveness of decision aids on critically ill patients' outcomes and family members' knowledge, anxiety, depression and decisional conflict: A systematic review and meta-analysis

BACKGROUND

Decision aids (DAs) have been proposed to support patients and families with disease information processing and decision-making, but their effectiveness for critically ill patients and their families is incompletely understood.

AIM

To systematically synthesize evidence on the effectiveness of the DAs on the prognosis of critically ill patients and knowledge, anxiety, depression and decisional conflict of their family members.

STUDY DESIGN

Systematic review and meta-analysis. We conducted a systematic search of literature using PubMed, Embase, Cochrane Library, Web of Science, Cumulative Index to Nursing and Allied Health Literature database, Scopus, PsycNet, CNKI and Wanfang Database from the inception of the databases until May 2023 to identify randomized clinical trials (RCTs) describing DAs interventions targeted at adult intensive care unit (ICU) patients or their families. We also searched grey literature in four databases: Chinese Clinical Trials Registry, Chinese Cochrane Center, Open Grey and GreyNet International.

RESULTS

Seven RCTs were included in the review. Meta-analysis identified longer hospital length of stay (LOS) among all patients compared with usual care (mean difference [MD] = 5.64 days, 95% confidence interval, CI [0.29, 10.98], p = .04), but not in surviving patients (MD = 2.09 days, 95% CI [-3.70, 7.89], p = .48). However, there was no evidence of an effect of DAs on hospital mortality (RR = 1.25, 95% CI [0.92, 1.70], p = .15), ICU LOS (MD = 3.77 days, 95% CI [-0.17, 7.70], p = .06) and length of mechanical ventilation (MD = 0.88 days, 95% CI [-2.22, 3.97], p = .58). DAs led to a statistically significant improvement in family members' knowledge (standard mean difference = 0.84, 95% CI [0.12, 1.56], p = .02). We found no significant effect of DAs on anxiety, depression, post-traumatic stress disorder, decisional conflict and quality of communication of family members.

CONCLUSIONS

This review provides effective evidence that DAs can potentially improve the knowledge level of family members while prolonging the hospital LOS among critically ill patients.

RELEVANCE TO CLINICAL PRACTICE

Well-designed large-scale studies with DAs tailored to the individuals' preferences and existing cultural values are warranted.

Clinical decision support system based on deep learning for evaluating implantable collamer lens size and vault after implantable collamer lens surgery: a retrospective study

OBJECTIVES

To aid doctors in selecting the optimal preoperative implantable collamer lens (ICL) size and to enhance the safety and surgical outcomes of ICL procedures, a clinical decision support system (CDSS) is proposed in our study.

DESIGN

A retrospective study of patients after ICL surgery.

SETTING

China Tertiary Myopia Prevention and Control Center.

PARTICIPANTS

2772 eyes belonging to 1512 patients after ICL surgery. Data were collected between 2018 and 2022.

OUTCOME MEASURES

A CDSS is constructed and used to predict vault at 1 month postoperatively and preoperative ICL dimensions using various artificial intelligence methods. Accuracy metrics as well as area under curve (AUC) parameters are used to determine the CDSS prediction methods.

RESULTS

Among the ICL size prediction models, conventional neural networks (CNNs) achieve the best prediction accuracy at 91.37% and exhibit the highest AUC of 0.842. Regarding the prediction model for vault values 1 month after surgery, CNN surpasses the other methods with an accuracy of 85.27%, which has the uppermost AUC of 0.815. Thus, we select CNN as the prediction algorithm for the CDSS.

CONCLUSIONS

This study introduces a CDSS to assist doctors in selecting the optimal ICL size for patients while improving the safety and postoperative outcomes of ICL surgery.