Contrastive diagnostic embedding (CDE) model for automated coding - A case study using emergency department encounters.

BACKGROUND: Billing codes are utilized for medical reimbursement, clinical quality metric valuation and for epidemiologic purposes to report and follow disease trends and outcomes. The current paradigm of manual coding can be expensive, time-consuming, and subject to human error. Though automation of the billing codes has been widely reported in the literature via rule-based and supervised approaches, existing strategies lack generalizability and robustness towards large and constantly changing ICD hierarchical structure. METHOD: We propose a weakly supervised training strategy by leveraging contrastive learning, contrastive diagnosis embedding (CDE) to capture the fine semantic variations between the diagnosis codes. The approach consists of a two-phase contrastive training for generating the semantic embedding space adapted to incorporate hierarchical information of ICD-10 vocabulary and a weakly supervised retrieval scheme. Core strength of the proposed method is that it puts no limit on the 70 K ICD-10 codes set and can handle all rare codes for coding the diagnosis. RESULTS: Our CDE model outperformed string-based partial matching and ClinicalBERT embedding on three test cases (a retrospective testset, a prospective testset, and external testset) and produced an accurate prediction of rare and newly introduced diagnosis codes. A detailed ablation study showed the importance of each phase of the proposed multi-phase training. Each successive phase of training - ICD-10 group sensitive training (phase 1.1), ICD-10 subgroup sensitive training (phase 1.2), free-text diagnosis description-based training (phase 2) - improved performance beyond the previous phase of training. The model also outperformed existing supervised models like CAML and PLM-ICD and produced satisfactory performance on the rare codes. CONCLUSION: Compared to the existing rule-based and supervised models, the proposed weakly supervised contrastive learning overcomes the limitations in terms of generalization capability and increases the robustness of the automated billing. Such a model will allow flexibility through accurate billing code automation for practice convergence and gains efficiencies in a value-based care payment environment.

Graph convolutional network-based fusion model to predict risk of hospital acquired infections.

OBJECTIVE: Hospital acquired infections (HAIs) are one of the top 10 leading causes of death within the United States. While current standard of HAI risk prediction utilizes only a narrow set of predefined clinical variables, we propose a graph convolutional neural network (GNN)-based model which incorporates a wide variety of clinical features. MATERIALS AND METHODS: Our GNN-based model defines patients' similarity based on comprehensive clinical history and demographics and predicts all types of HAI rather than focusing on a single subtype. An HAI model was trained on 38 327 unique hospitalizations while a distinct model for surgical site infection (SSI) prediction was trained on 18 609 hospitalization. Both models were tested internally and externally on a geographically disparate site with varying infection rates. RESULTS: The proposed approach outperformed all baselines (single-modality models and length-of-stay [LoS]) with achieved area under the receiver operating characteristics of 0.86 [0.84-0.88] and 0.79 [0.75-0.83] (HAI), and 0.79 [0.75-0.83] and 0.76 [0.71-0.76] (SSI) for internal and external testing. Cost-effective analysis shows that the GNN modeling dominated the standard LoS model strategy on the basis of lower mean costs ($1651 vs $1915). DISCUSSION: The proposed HAI risk prediction model can estimate individualized risk of infection for patient by taking into account not only the patient's clinical features, but also clinical features of similar patients as indicated by edges of the patients' graph. CONCLUSIONS: The proposed model could allow prevention or earlier detection of HAI, which in turn could decrease hospital LoS and associated mortality, and ultimately reduce the healthcare cost.

Predicting 30-day all-cause hospital readmission using multimodal spatiotemporal graph neural networks.

Reduction in 30-day readmission rate is an important quality factor for hospitals as it can reduce the overall cost of care and improve patient post-discharge outcomes. While deep-learning-based studies have shown promising empirical results, several limitations exist in prior models for hospital readmission prediction, such as: (a) only patients with certain conditions are considered, (b) do not leverage data temporality, (c) individual admissions are assumed independent of each other, which ignores patient similarity, (d) limited to single modality or single center data. In this study, we propose a multimodal, spatiotemporal graph neural network (MM-STGNN) for prediction of 30-day all-cause hospital readmission, which fuses in-patient multimodal, longitudinal data and models patient similarity using a graph. Using longitudinal chest radiographs and electronic health records from two independent centers, we show that MM-STGNN achieved an area under the receiver operating characteristic curve (AUROC) of 0.79 on both datasets. Furthermore, MM-STGNN significantly outperformed the current clinical reference standard, LACE+ (AUROC=0.61), on the internal dataset. For subset populations of patients with heart disease, our model significantly outperformed baselines, such as gradient-boosting and Long Short-Term Memory models (e.g., AUROC improved by 3.7 points in patients with heart disease). Qualitative interpretability analysis indicated that while patients' primary diagnoses were not explicitly used to train the model, features crucial for model prediction may reflect patients' diagnoses. Our model could be utilized as an additional clinical decision aid during discharge disposition and triaging high-risk patients for closer post-discharge follow-up for potential preventive measures.