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Importance: The lack of standards in methods to reduce bias for clinical algorithms presents various challenges in providing reliable predictions and in addressing health disparities.
Objective: To evaluate approaches for reducing bias in machine learning models using a real-world clinical scenario.
Design, Setting, And Participants: Health data for this cohort study were obtained from the IBM MarketScan Medicaid Database. Eligibility criteria were as follows: (1) Female individuals aged 12 to 55 years with a live birth record identified by delivery-related codes from January 1, 2014, through December 31, 2018; (2) greater than 80% enrollment through pregnancy to 60 days post partum; and (3) evidence of coverage for depression screening and mental health services. Statistical analysis was performed in 2020.
Exposures: Binarized race (Black individuals and White individuals).
Main Outcomes And Measures: Machine learning models (logistic regression [LR], random forest, and extreme gradient boosting) were trained for 2 binary outcomes: postpartum depression (PPD) and postpartum mental health service utilization. Risk-adjusted generalized linear models were used for each outcome to assess potential disparity in the cohort associated with binarized race (Black or White). Methods for reducing bias, including reweighing, Prejudice Remover, and removing race from the models, were examined by analyzing changes in fairness metrics compared with the base models. Baseline characteristics of female individuals at the top-predicted risk decile were compared for systematic differences. Fairness metrics of disparate impact (DI, 1 indicates fairness) and equal opportunity difference (EOD, 0 indicates fairness).
Results: Among 573 634 female individuals initially examined for this study, 314 903 were White (54.9%), 217 899 were Black (38.0%), and the mean (SD) age was 26.1 (5.5) years. The risk-adjusted odds ratio comparing White participants with Black participants was 2.06 (95% CI, 2.02-2.10) for clinically recognized PPD and 1.37 (95% CI, 1.33-1.40) for postpartum mental health service utilization. Taking the LR model for PPD prediction as an example, reweighing reduced bias as measured by improved DI and EOD metrics from 0.31 and -0.19 to 0.79 and 0.02, respectively. Removing race from the models had inferior performance for reducing bias compared with the other methods (PPD: DI = 0.61; EOD = -0.05; mental health service utilization: DI = 0.63; EOD = -0.04).
Conclusions And Relevance: Clinical prediction models trained on potentially biased data may produce unfair outcomes on the basis of the chosen metrics. This study's results suggest that the performance varied depending on the model, outcome label, and method for reducing bias. This approach toward evaluating algorithmic bias can be used as an example for the growing number of researchers who wish to examine and address bias in their data and models.
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http://dx.doi.org/10.1001/jamanetworkopen.2021.3909 | DOI Listing |
Med Decis Making
December 2024
Department of Health Policy, Stanford School of Medicine, Stanford, CA, USA.
Purpose: Individual-level state-transition microsimulations (iSTMs) have proliferated for economic evaluations in place of cohort state transition models (cSTMs). Probabilistic economic evaluations quantify decision uncertainty and value of information (VOI). Previous studies show that iSTMs provide unbiased estimates of expected incremental net monetary benefits (EINMB), but statistical properties of iSTM-produced estimates of decision uncertainty and VOI remain uncharacterized.
View Article and Find Full Text PDFEClinicalMedicine
January 2025
Department of Neurosurgery and Chinese Evidence-Based Medicine Centre and Cochrane China Centre and MAGIC China Centre and IDEAL China Centre, West China Hospital, Sichuan University, Chengdu, China.
Background: Surgical interventions for spontaneous supratentorial intracerebral haemorrhage (ICH) include conventional craniotomy (CC), decompressive craniectomy (DC), and minimally invasive surgery (MIS), with the latter encompassing endoscopic surgery (ES) and minimally invasive puncture surgery (MIPS). However, the superiority of surgery over conservative medical treatment (CMT) and the comparative benefits of different surgical procedures remain unclear. We aimed to evaluate the efficacy and safety of various surgical interventions for treating ICH.
View Article and Find Full Text PDFNeuropsychiatr Dis Treat
December 2024
Department of Adult Psychiatry, The Affiliated Brain Hospital, Guangzhou Medical University, Guangzhou, Guangdong Province, People's Republic of China.
Purpose: Constructing a multiple mediation model based on two mediating variables, social support and self-efficacy, to explore the mechanism of health literacy's effect on decisional conflict in patients with stable schizophrenia.
Patients And Methods: A total of 205 patients with stable schizophrenia who were hospitalized in a psychiatric hospital in Guangdong Province, China, were selected for the study. The All Aspects of Health Literacy Scale (AAHLS), Decisional Conflict Scale (DCS), Social Support Rating Scale (SSRS) and General Self-Efficacy Scale (GSES) were used to evaluate health literacy, decisional conflict, social support and self-efficacy.
Pediatr Investig
December 2024
Hematology Department, Hemophilia Comprehensive Care Center, Hematology Center, Beijing Key Laboratory of Pediatric Hematology-Oncology, Key Laboratory of Major Diseases in Children, National Center for Children's Health National Key Discipline of Pediatrics (Capital Medical University), Ministry of Education, Beijing Children's Hospital, Capital Medical University Beijing China.
Importance: Emicizumab (EMI) is efficacious and safe for hemophilia A (HA) prophylaxis. However, its high cost poses a challenge in China.
Objective: To explore the possibility of using reduced-dosage EMI in Chinese HA children.
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