Predictive Analytics for Glaucoma Using Data From the All of Us Research Program.

PURPOSE: To (1) use All of Us (AoU) data to validate a previously published single-center model predicting the need for surgery among individuals with glaucoma, (2) train new models using AoU data, and (3) share insights regarding this novel data source for ophthalmic research. DESIGN: Development and evaluation of machine learning models. METHODS: Electronic health record data were extracted from AoU for 1,231 adults diagnosed with primary open-angle glaucoma. The single-center model was applied to AoU data for external validation. AoU data were then used to train new models for predicting the need for glaucoma surgery using multivariable logistic regression, artificial neural networks, and random forests. Five-fold cross-validation was performed. Model performance was evaluated based on area under the receiver operating characteristic curve (AUC), accuracy, precision, and recall. RESULTS: The mean (standard deviation) age of the AoU cohort was 69.1 (10.5) years, with 57.3% women and 33.5% black, significantly exceeding representation in the single-center cohort (P = .04 and P < .001, respectively). Of 1,231 participants, 286 (23.2%) needed glaucoma surgery. When applying the single-center model to AoU data, accuracy was 0.69 and AUC was only 0.49. Using AoU data to train new models resulted in superior performance: AUCs ranged from 0.80 (logistic regression) to 0.99 (random forests). CONCLUSIONS: Models trained with national AoU data achieved superior performance compared with using single-center data. Although AoU does not currently include ophthalmic imaging, it offers several strengths over similar big-data sources such as claims data. AoU is a promising new data source for ophthalmic research.

The anatomy of a distributed predictive modeling framework: online learning, blockchain network, and consensus algorithm.

OBJECTIVE: Cross-institutional distributed healthcare/genomic predictive modeling is an emerging technology that fulfills both the need of building a more generalizable model and of protecting patient data by only exchanging the models but not the patient data. In this article, the implementation details are presented for one specific blockchain-based approach, ExplorerChain, from a software development perspective. The healthcare/genomic use cases of myocardial infarction, cancer biomarker, and length of hospitalization after surgery are also described. MATERIALS AND METHODS: ExplorerChain's 3 main technical components, including online machine learning, metadata of transaction, and the Proof-of-Information-Timed (PoINT) algorithm, are introduced in this study. Specifically, the 3 algorithms (ie, core, new network, and new site/data) are described in detail. RESULTS: ExplorerChain was implemented and the design details of it were illustrated, especially the development configurations in a practical setting. Also, the system architecture and programming languages are introduced. The code was also released in an open source repository available at https://github.com/tsungtingkuo/explorerchain. DISCUSSION: The designing considerations of semi-trust assumption, data format normalization, and non-determinism was discussed. The limitations of the implementation include fixed-number participating sites, limited join-or-leave capability during initialization, advanced privacy technology yet to be included, and further investigation in ethical, legal, and social implications. CONCLUSION: This study can serve as a reference for the researchers who would like to implement and even deploy blockchain technology. Furthermore, the off-the-shelf software can also serve as a cornerstone to accelerate the development and investigation of future healthcare/genomic blockchain studies.

Machine Learning-Based Predictive Modeling of Surgical Intervention in Glaucoma Using Systemic Data From Electronic Health Records.

PURPOSE: To predict the need for surgical intervention in patients with primary open-angle glaucoma (POAG) using systemic data in electronic health records (EHRs). DESIGN: Development and evaluation of machine learning models. METHODS: Structured EHR data of 385 POAG patients from a single academic institution were incorporated into models using multivariable logistic regression, random forests, and artificial neural networks. Leave-one-out cross-validation was performed. Mean area under the receiver operating characteristic curve (AUC), sensitivity, specificity, accuracy, and Youden index were calculated for each model to evaluate performance. Systemic variables driving predictions were identified and interpreted. RESULTS: Multivariable logistic regression was most effective at discriminating patients with progressive disease requiring surgery, with an AUC of 0.67. Higher mean systolic blood pressure was associated with significantly increased odds of needing glaucoma surgery (odds ratio [OR] = 1.09, P < .001). Ophthalmic medications (OR = 0.28, P < .001), non-opioid analgesic medications (OR = 0.21, P = .002), anti-hyperlipidemic medications (OR = 0.39, P = .004), macrolide antibiotics (OR = 0.40, P = .03), and calcium blockers (OR = 0.43, P = .03) were associated with decreased odds of needing glaucoma surgery. CONCLUSIONS: Existing systemic data in the EHR has some predictive value in identifying POAG patients at risk of progression to surgical intervention, even in the absence of eye-specific data. Blood pressure-related metrics and certain medication classes emerged as predictors of glaucoma progression. This approach provides an opportunity for future development of automated risk prediction within the EHR based on systemic data to assist with clinical decision-making.