Identify and mitigate bias in electronic phenotyping: A comprehensive study from computational perspective.

Electronic phenotyping is a fundamental task that identifies the special group of patients, which plays an important role in precision medicine in the era of digital health. Phenotyping provides real-world evidence for other related biomedical research and clinical tasks, e.g., disease diagnosis, drug development, and clinical trials, etc. With the development of electronic health records, the performance of electronic phenotyping has been significantly boosted by advanced machine learning techniques. In the healthcare domain, precision and fairness are both essential aspects that should be taken into consideration. However, most related efforts are put into designing phenotyping models with higher accuracy. Few attention is put on the fairness perspective of phenotyping. The neglection of bias in phenotyping leads to subgroups of patients being underrepresented which will further affect the following healthcare activities such as patient recruitment in clinical trials. In this work, we are motivated to bridge this gap through a comprehensive experimental study to identify the bias existing in electronic phenotyping models and evaluate the widely-used debiasing methods' performance on these models. We choose pneumonia and sepsis as our phenotyping target diseases. We benchmark 9 kinds of electronic phenotyping methods spanning from rule-based to data-driven methods. Meanwhile, we evaluate the performance of the 5 bias mitigation strategies covering pre-processing, in-processing, and post-processing. Through the extensive experiments, we summarize several insightful findings from the bias identified in the phenotyping and key points of the bias mitigation strategies in phenotyping.

Phthalate exposure and blood pressure in U.S. children aged 8-17 years (NHANES 2013-2018).

BACKGROUND: Current evidence from epidemiologic studies suggested that phthalate metabolites might be associated with blood pressure (BP) changes. However, the special relationship between phthalate metabolites and BP changes in children has not been clearly elucidated in existing researches. OBJECTIVES: We investigated the links between phthalate metabolites and various BP parameters, including systolic/diastolic BP, mean arterial pressure (MAP), and the presence of hypertension. METHODS: The population sample consisted of 1036 children aged 8 to 17 years from the 2013-2018 NHANES in the United States. High performance liquid chromatography-electrospray ionization-tandem mass spectrometry was used to measure urinary concentrations of 19 phthalate metabolites. Systolic/diastolic BP were derived from the average of three valid measurements, and MAP was calculated as (systolic BP + 2 × diastolic BP)/3. Hypertension was defined as mean systolic BP and/or diastolic BP that was ≥ 95th percentile for gender, age, and height reference. Linear regression, logistic regression, and weighted quantile sum (WQS) regression models were employed to assess the associations between phthalate exposure and systolic/diastolic BP, MAP, and hypertension. RESULTS: Ten of 19 phthalate metabolites including MCNP, MCOP, MECPP, MBP, MCPP, MEP, MEHHP, MiBP, MEOHP, and MBzP had detection frequencies > 85% with samples more than 1000. MCNP, MCOP, MECPP, MBP, MCPP, MEHHP, MiBP, MEOHP, and MBzP were generally negatively associated with systolic/diastolic BP and MAP, but not protective factors for hypertension. These associations were not modified by age (8-12 and 13-17 years) or sex (boys and girls). The above-mentioned associations were further confirmed by the application of the WQS analysis, and MCOP was identified as the chemical with the highest weight. CONCLUSION: Phthalate metabolites were associated with modest reductions in systolic/diastolic BP, and MAP in children, while appeared not protective factors for hypertension. Given the inconsistent results among existing studies, our findings should be confirmed by other cohort studies.

Multi-task learning with dynamic re-weighting to achieve fairness in healthcare predictive modeling.

The emphasis on fairness in predictive healthcare modeling has increased in popularity as an approach for overcoming biases in automated decision-making systems. The aim is to guarantee that sensitive characteristics like gender, race, and ethnicity do not influence prediction outputs. Numerous algorithmic strategies have been proposed to reduce bias in prediction results, mitigate prejudice toward minority groups and promote prediction fairness. The goal of these strategies is to ensure that model prediction performance does not exhibit significant disparity among sensitive groups. In this study, we propose a novel fairness-achieving scheme based on multitask learning, which fundamentally differs from conventional fairness-achieving techniques, including altering data distributions and constraint optimization through regularizing fairness metrics or tampering with prediction outcomes. By dividing predictions on different sub-populations into separate tasks, we view the fairness problem as a task-balancing problem. To ensure fairness during the model-training process, we suggest a novel dynamic re-weighting approach. Fairness is achieved by dynamically modifying the gradients of various prediction tasks during neural network back-propagation, and this novel technique applies to a wide range of fairness criteria. We conduct tests on a real-world use case to predict sepsis patients' mortality risk. Our approach satisfies that it can reduce the disparity between subgroups by 98% while only losing less than 4% of prediction accuracy.

Multi-Task Learning for Post-transplant Cause of Death Analysis: A Case Study on Liver Transplant.

Organ transplant is the essential treatment method for some end-stage diseases, such as liver failure. Analyzing the post-transplant cause of death (CoD) after organ transplant provides a powerful tool for clinical decision making, including personalized treatment and organ allocation. However, traditional methods like Model for End-stage Liver Disease (MELD) score and conventional machine learning (ML) methods are limited in CoD analysis due to two major data and model-related challenges. To address this, we propose a novel framework called CoD-MTL leveraging multi-task learning to model the semantic relationships between various CoD prediction tasks jointly. Specifically, we develop a novel tree distillation strategy for multi-task learning, which combines the strength of both the tree model and multi-task learning. Experimental results are presented to show the precise and reliable CoD predictions of our framework. A case study is conducted to demonstrate the clinical importance of our method in the liver transplant.

Towards Fair Patient-Trial Matching via Patient-Criterion Level Fairness Constraint.

Clinical trials are indispensable in developing new treatments, but they face obstacles in patient recruitment and retention, hindering the enrollment of necessary participants. To tackle these challenges, deep learning frameworks have been created to match patients to trials. These frameworks calculate the similarity between patients and clinical trial eligibility criteria, considering the discrepancy between inclusion and exclusion criteria. Recent studies have shown that these frameworks outperform earlier approaches. However, deep learning models may raise fairness issues in patient-trial matching when certain sensitive groups of individuals are underrepresented in clinical trials, leading to incomplete or inaccurate data and potential harm. To tackle the issue of fairness, this work proposes a fair patient-trial matching framework by generating a patient-criterion level fairness constraint. The proposed framework considers the inconsistency between the embedding of inclusion and exclusion criteria among patients of different sensitive groups. The experimental results on real-world patient-trial and patient-criterion matching tasks demonstrate that the proposed framework can successfully alleviate the predictions that tend to be biased.

Fairly Predicting Graft Failure in Liver Transplant for Organ Assigning.

Liver transplant is an essential therapy performed for severe liver diseases. The fact of scarce liver resources makes the organ assigning crucial. Model for End-stage Liver Disease (MELD) score is a widely adopted criterion when making organ distribution decisions. However, it ignores post-transplant outcomes and organ/donor features. These limitations motivate the emergence of machine learning (ML) models. Unfortunately, ML models could be unfair and trigger bias against certain groups of people. To tackle this problem, this work proposes a fair machine learning framework targeting graft failure prediction in liver transplant. Specifically, knowledge distillation is employed to handle dense and sparse features by combining the advantages of tree models and neural networks. A two-step debiasing method is tailored for this framework to enhance fairness. Experiments are conducted to analyze unfairness issues in existing models and demonstrate the superiority of our method in both prediction and fairness performance.