JMIR Medical Informatics最新文献

Background: Prolonged hospital stays can lead to inefficiencies in health care delivery and unnecessary consumption of medical resources.

Objective: This study aimed to identify key clinical variances associated with prolonged length of stay (PLOS) in clinical pathways using a machine learning model trained on real-world data from the ePath system.

Methods: We analyzed data from 480 patients with lung cancer (age: mean 68.3, SD 11.2 years; n=263, 54.8% men) who underwent video-assisted thoracoscopic surgery at a university hospital between 2019 and 2023. PLOS was defined as a hospital stay exceeding 9 days after video-assisted thoracoscopic surgery. The variables collected between admission and 4 days after surgery were examined, and those that showed a significant association with PLOS in univariate analyses (P<.01) were selected as predictors. Predictive models were developed using sparse linear regression methods (Lasso, ridge, and elastic net) and decision tree ensembles (random forest and extreme gradient boosting). The data were divided into derivation (earlier study period) and testing (later period) cohorts for temporal validation. The model performance was assessed using the area under the receiver operating characteristic curve, Brier score, and calibration plots. Counterfactual analysis was used to identify key clinical factors influencing PLOS.

Results: A 3D heatmap illustrated the temporal relationships between clinical factors and PLOS based on patient demographics, comorbidities, functional status, surgical details, care processes, medications, and variances recorded from admission to 4 days after surgery. Among the 5 algorithms evaluated, the ridge regression model demonstrated the best performance in terms of both discrimination and calibration. Specifically, it achieved area under the receiver operating characteristic curve values of 0.84 and 0.82 and Brier scores of 0.16 and 0.17 in the derivation and test cohorts, respectively. In the final model, a range of variables, including blood tests, care, patient background, procedures, and clinical variances, were associated with PLOS. Among these, particular emphasis was placed on clinical variances. Counterfactual analysis using the ridge regression model identified 6 key variables strongly linked to PLOS. In order of impact, these were abnormal respiratory sounds, postoperative fever, arrhythmia, impaired ambulation, complications after drain removal, and pulmonary air leaks.

Conclusions: A machine learning-based model using ePath data effectively identified critical variances in the clinical pathways associated with PLOS. This automated tool may enhance clinical decision-making and improve patient management.

Unlabelled: Academic global health informatics (GHI) projects are impactful collaborations between institutions in high-income and low- and middle-income countries (LMICs) and play a crucial role in enhancing health care services and access in LMICs using eHealth practices. Researchers across all involved organizations bring unique expertise to these collaborations. However, these projects often face significant obstacles, including cultural and linguistic barriers, resource limitations, and sustainability issues. The lack of representation from LMIC researchers in knowledge generation and the high costs of open-access publications further complicate efforts to ensure inclusive, accessible, and collaborative scholarship. This viewpoint describes present gaps in the literature on academic GHI collaborations and describes a path forward for future research directions and successful research community development. Key recommendations include centering community-based participatory research, developing post-growth solutions, and creating sustainable funding models. Addressing these challenges is essential for fostering effective, scalable, and equitable GHI interventions that improve global health outcomes.

Background: Patients with acute coronary syndrome (ACS) who undergo percutaneous coronary intervention (PCI) remain at high risk for major adverse cardiovascular events (MACE). Conventional risk scores may not capture dynamic or nonlinear changes in postdischarge MACE risk, whereas machine learning (ML) approaches can improve predictive performance. However, few ML models have incorporated time-to-event analysis to reflect changes in MACE risk over time.

Objective: This study aimed to develop a time-to-event ML model for predicting MACE after PCI in patients with ACS and to identify the risk factors with time-varying contributions.

Methods: We analyzed electronic health records of 3159 patients with ACS who underwent PCI at a tertiary hospital in South Korea between 2008 and 2020. Six time-to-event ML models were developed using 54 variables. Model performance was evaluated using the time-dependent concordance index and Brier score. Variable importance was assessed using permutation importance and visualized with partial dependence plots to identify variables contributing to MACE risk over time.

Results: During a median follow-up of 3.8 years, 626 (19.8%) patients experienced MACE. The best-performing model achieved a time-dependent concordance index of 0.743 at day 30 and 0.616 at 1 year. Time-dependent Brier scores increased and remained stable across all ML models. Key predictors included contrast volume, age, medication adherence, coronary artery disease severity, and glomerular filtration rate. Contrast volume ≥300 mL, age ≥60 years, and medication adherence score ≥30 were associated with early postdischarge risk, whereas coronary artery disease severity and glomerular filtration rate became more influential beyond 60 days.

Conclusions: The proposed time-to-event ML model effectively captured dynamic risk patterns after PCI and identified key predictors with time-varying effects. These findings may support individualized postdischarge management and early intervention strategies to prevent MACE in high-risk patients.

Background: Developing computable phenotypes (CP) based on electronic health records (EHR) data requires "gold-standard" labels for the outcome of interest. To generate these labels, clinicians typically chart-review a subset of patient charts. Charts to be reviewed are most often randomly sampled from the larger set of patients of interest. However, random sampling may fail to capture the diversity of the patient population, particularly if smaller subpopulations exist among those with the condition of interest. This can lead to poorly performing and biased CPs.

Objective: This study aimed to propose an unsupervised sampling approach designed to better capture a diverse patient cohort and improve the information coverage of chart review samples.

Methods: Our coverage sampling method starts by clustering by the patient population of interest. We then perform a stratified sampling from each cluster to ensure even representation for the chart review sample. We introduce a novel metric, nearest neighbor distance, to evaluate the coverage of the generated sample. To evaluate our method, we first conducted a simulation study to model and compare the performance of random versus our proposed coverage sampling. We varied the size and number of subpopulations within the larger cohort. Finally, we apply our approach to a real-world data set to develop a CP for hospitalization due to COVID-19. We evaluate the different sampling strategies based on the information coverage as well as the performance of the learned CP, using the area under the receiver operator characteristic curve.

Results: Our simulation studies show that the unsupervised coverage sampling approach provides broader coverage of patient populations compared to random sampling. When there are no underlying subpopulations, both random and coverage perform equally well for CP development. When there are subgroups, coverage sampling achieves area under the receiver operating characteristic curve gains of approximately 0.03-0.05 over random sampling. In the real-world application, the approach also outperformed random sampling, generating both a more representative sample and an area under the receiver operating characteristic curve improvement of 0.02 (95% CI -0.08 to 0.04).

Conclusions: The proposed coverage sampling method is an easy-to-implement approach that produces a chart review sample that is more representative of the source population. This allows one to learn a CP that has better performance both for subpopulations and the overall cohort. Studies that aim to develop CPs should consider alternative strategies other than randomly sampling patient charts.

Background: The Computerized Digit Vigilance Test (CDVT) is a well-established measure of sustained attention. However, the CDVT only measures the total reaction time and response accuracy and fails to capture other crucial attentional features such as the eye blink rate, yawns, head movements, and eye movements. Omitting such features might provide an incomplete representative picture of sustained attention.

Objective: This study aimed to develop an artificial intelligence (AI)-based Computerized Digit Vigilance Test (AI-CDVT) for older adults.

Methods: Participants were assessed by the CDVT with video recordings capturing their head and face. The Montreal Cognitive Assessment (MoCA), Stroop Color Word Test (SCW), and Color Trails Test (CTT) were also administered. The AI-CDVT was developed in three steps: (1) retrieving attentional features using OpenFace AI software (CMU MultiComp Lab), (2) establishing an AI-based scoring model with the Extreme Gradient Boosting regressor, and (3) assessing the AI-CDVT's validity by Pearson r values and test-retest reliability by intraclass correlation coefficients (ICCs).

Results: In total, 153 participants were included. Pearson r values of the AI-CDVT with the MoCA were -0.42, -0.31 with the SCW, and 0.46-0.61 with the CTT. The ICC of the AI-CDVT was 0.78.

Conclusions: We developed an AI-CDVT, which leveraged AI to extract attentional features from video recordings and integrated them to generate a comprehensive attention score. Our findings demonstrated good validity and test-retest reliability for the AI-CDVT, suggesting its potential as a reliable and valid tool for assessing sustained attention in older adults.

Background: Heart failure (HF) is a public health concern with a wider impact on quality of life and cost of care. One of the major challenges in HF is the higher rate of unplanned readmissions and suboptimal performance of models to predict the readmissions. Hence, in this study, we implemented embeddings-based approaches to generate features for improving model performance.

Objective: The objective of this study was to evaluate and compare the effectiveness of different feature embedding approaches for improving the prediction of unplanned readmissions in patients with heart failure.

Methods: We compared three embedding approaches including word2vec on terminology codes and concept unique identifier (CUIs) and BERT on descriptive text of concept with baseline (one hot-encoding). We compared area under the receiver operating characteristic (AUROC) and F1-scores for the logistic regression, eXtream gradient-boosting (XGBoost) and artificial neural network (ANN) models using these embedding approaches. The model was tested on the heart failure cohort (N=21,031) identified using least restrictive phenotyping methods from MIMIC-IV dataset.

Results: We found that the embedding approaches significantly improved the performance of the prediction models. The XGBoost performed better for all approaches. The word2vec embeddings (0.65) trained on the dataset outperformed embeddings from pre-trained BERT model (0.59) using descriptive text.

Conclusions: Embedding methods, particularly word2vec trained on electronic health record data, can better discriminate HF readmission cases compared to both one-hot encoding and pre-trained BERT embeddings on concept descriptions making it a viable approach of automation feature selection. The observed AUROC improvement (0.65 vs 0.54) may support more effective risk stratification and targeted clinical interventions.

Background: Electronic health records (EHRs) contain comprehensive information regarding diagnoses, clinical procedures, and prescribed medications. This makes them a valuable resource for developing automated hypertension medication recommendation systems. Within this field, existing research has used machine learning approaches, leveraging demographic characteristics and basic clinical indicators, or deep learning techniques, which extract patterns from EHR data, to predict optimal medications or improve the accuracy of recommendations for common antihypertensive medication categories. However, these methodologies have significant limitations. They rarely adequately characterize the synergistic relationships among heterogeneous medical entities, such as the interplay between comorbid conditions, laboratory results, and specific antihypertensive agents. Furthermore, given the chronic and fluctuating nature of hypertension, effective medication recommendations require dynamic adaptation to disease progression over time. However, current approaches either lack rigorous temporal modeling of EHR data or fail to effectively integrate temporal dynamics with interentity relationships, resulting in the generation of recommendations that are not clinically appropriate due to the neglect of these critical factors.

Objective: This study aims to overcome the challenges in existing methods and introduce a novel model for hypertension medication recommendation that leverages the synergy and selectivity of heterogeneous medical entities.

Methods: First, we used patient EHR data to construct both heterogeneous and homogeneous graphs. The interentity synergies were captured using a multihead graph attention mechanism to enhance entity-level representations. Next, a bidirectional temporal selection mechanism calculated selective coefficients between current and historical visit records and aggregated them to form refined visit-level representations. Finally, medication recommendation probabilities were determined based on these comprehensive patient representations.

Results: Experimental evaluations on the real-world datasets Medical Information Mart for Intensive Care (MIMIC)-III v1.4 and MIMIC-IV v2.2 demonstrated that the proposed model achieved Jaccard similarity coefficients of 58.01% and 55.82%, respectively; areas under the curve of precision-recall of 83.56% and 80.69%, respectively; and F1-scores of 68.95% and 64.83%, respectively, outperforming the baseline models.

Conclusions: The findings indicate the superior efficacy of the introduced model in medication recommendation, highlighting its potential to enhance clinical decision-making in the management of hypertension. The code for the model has been released on GitHub.