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Objective: Surgical outcome prediction is challenging but necessary for postoperative management. Current machine learning models utilize pre- and post-op data, excluding intraoperative information in surgical notes. Current models also usually predict binary outcomes even when surgeries have multiple outcomes that require different postoperative management. This study addresses these gaps by incorporating intraoperative information into multimodal models for multiclass glaucoma surgery outcome prediction., Materials and Methods: We developed and evaluated multimodal deep learning models for multiclass glaucoma trabeculectomy surgery outcomes using both structured EHR data and free-text operative notes. We compare those to baseline models that use structured EHR data exclusively, or neural network models that leverage only operative notes., Results: The multimodal neural network had the highest performance with a macro AUROC of 0.750 and F1 score of 0.583. It outperformed the baseline machine learning model with structured EHR data alone (macro AUROC of 0.712 and F1 score of 0.486). Additionally, the multimodal model achieved the highest recall (0.692) for hypotony surgical failure, while the surgical success group had the highest precision (0.884) and F1 score (0.775)., Discussion: This study shows that operative notes are an important source of predictive information. The multimodal predictive model combining perioperative notes and structured pre- and post-op EHR data outperformed other models. Multiclass surgical outcome prediction can provide valuable insights for clinical decision-making., Conclusions: Our results show the potential of deep learning models to enhance clinical decision-making for postoperative management. They can be applied to other specialties to improve surgical outcome predictions., (© The Author(s) 2023. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Objectives: Collider bias is a common threat to internal validity in clinical research but is rarely mentioned in informatics education or literature. Conditioning on a collider, which is a variable that is the shared causal descendant of an exposure and outcome, may result in spurious associations between the exposure and outcome. Our objective is to introduce readers to collider bias and its corollaries in the retrospective analysis of electronic health record (EHR) data., Target Audience: Collider bias is likely to arise in the reuse of EHR data, due to data-generating mechanisms and the nature of healthcare access and utilization in the United States. Therefore, this tutorial is aimed at informaticians and other EHR data consumers without a background in epidemiological methods or causal inference., Scope: We focus specifically on problems that may arise from conditioning on forms of healthcare utilization, a common collider that is an implicit selection criterion when one reuses EHR data. Directed acyclic graphs (DAGs) are introduced as a tool for identifying potential sources of bias during study design and planning. References for additional resources on causal inference and DAG construction are provided., (© The Author(s) 2023. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

Objective: The aim of this study was to collect and synthesize evidence regarding data quality problems encountered when working with variables related to social determinants of health (SDoH)., Materials and Methods: We conducted a systematic review of the literature on social determinants research and data quality and then iteratively identified themes in the literature using a content analysis process., Results: The most commonly represented quality issue associated with SDoH data is plausibility (n = 31, 41%). Factors related to race and ethnicity have the largest body of literature (n = 40, 53%). The first theme, noted in 62% (n = 47) of articles, is that bias or validity issues often result from data quality problems. The most frequently identified validity issue is misclassification bias (n = 23, 30%). The second theme is that many of the articles suggest methods for mitigating the issues resulting from poor social determinants data quality. We grouped these into 5 suggestions: avoid complete case analysis, impute data, rely on multiple sources, use validated software tools, and select addresses thoughtfully., Discussion: The type of data quality problem varies depending on the variable, and each problem is associated with particular forms of analytical error. Problems encountered with the quality of SDoH data are rarely distributed randomly. Data from Hispanic patients are more prone to issues with plausibility and misclassification than data from other racial/ethnic groups., Conclusion: Consideration of data quality and evidence-based quality improvement methods may help prevent bias and improve the validity of research conducted with SDoH data., (© The Author(s) 2021. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

Both emotion and visual processing deficits are documented in schizophrenia, and preferential magnocellular visual pathway dysfunction has been reported in several studies. This study examined the contribution to emotion-processing deficits of magnocellular and parvocellular visual pathway function, based on stimulus properties and shape of contrast response functions. Experiment 1 examined the relationship between contrast sensitivity to magnocellular- and parvocellular-biased stimuli and emotion recognition using the Penn Emotion Recognition (ER-40) and Emotion Differentiation (EMODIFF) tests. Experiment 2 altered the contrast levels of the faces themselves to determine whether emotion detection curves would show a pattern characteristic of magnocellular neurons and whether patients would show a deficit in performance related to early sensory processing stages. Results for experiment 1 showed that patients had impaired emotion processing and a preferential magnocellular deficit on the contrast sensitivity task. Greater deficits in ER-40 and EMODIFF performance correlated with impaired contrast sensitivity to the magnocellular-biased condition, which remained significant for the EMODIFF task even when nonspecific correlations due to group were considered in a step-wise regression. Experiment 2 showed contrast response functions indicative of magnocellular processing for both groups, with patients showing impaired performance. Impaired emotion identification on this task was also correlated with magnocellular-biased visual sensory processing dysfunction. These results provide evidence for a contribution of impaired early-stage visual processing in emotion recognition deficits in schizophrenia and suggest that a bottom-up approach to remediation may be effective.