Multi-Labeled Classification of Demographic Attributes of Patients: a case study of diabetics patients
Automated learning of patients demographics can be seen as multi-label problem where a patient model is based on different race and gender groups. The resulting model can be further integrated into Privacy-Preserving Data Mining, where it can be used to assess risk of identification of different patient groups. Our project considers relations between diabetes and demographics of patients as a multi-labelled problem. Most research in this area has been done as binary classification, where the target class is finding if a person has diabetes or not. But very few, and maybe no work has been done in multi-labeled analysis of the demographics of patients who are likely to be diagnosed with diabetes. To identify such groups, we applied ensembles of several multi-label learning algorithms.
Code (0)
등록된 구현이 없습니다.
Tasks
Binary ClassificationGeneral ClassificationMulti-Label LearningPrivacy PreservingSimilar Papers 제목 키워드 기반
RadFusion: Benchmarking Performance and Fairness for Multimodal Pulmonary Embolism Detection from CT and EHR
Despite the routine use of electronic health record (EHR) data by radiologists to contextualize clinical history and inform image interpretation, the majority of deep learning architectures for medical imaging are unimod…
BenchmarkingComputed Tomography (CT)FairnessPulmonary Embolism DetectionNeuron Incidence Redistribution for Fairness in Medical Image Classification
Deep learning models for medical image classification are susceptible to subgroup performance disparities across demographic attributes such as age, gender, and race. We identify a latent representational mechanism under…
Medical Image ClassificationRegression under demographic parity constraints via unlabeled post-processing
We address the problem of performing regression while ensuring demographic parity, even without access to sensitive attributes during inference. We present a general-purpose post-processing algorithm that, using accurate…
AttributeMulti-class ClassificationregressionHiCoTraj:Zero-Shot Demographic Reasoning via Hierarchical Chain-of-Thought Prompting from Trajectory
Inferring demographic attributes such as age, sex, or income level from human mobility patterns enables critical applications such as targeted public health interventions, equitable urban planning, and personalized trans…
Zero-Shot LearningOn Fairness of Medical Image Classification with Multiple Sensitive Attributes via Learning Orthogonal Representations
Mitigating the discrimination of machine learning models has gained increasing attention in medical image analysis. However, rare works focus on fair treatments for patients with multiple sensitive demographic ones, whic…
Fairnessimage-classificationImage ClassificationMedical Image Analysis+2