PMID- 37995843
OWN - NLM
STAT- MEDLINE
DCOM- 20231216
LR  - 20240426
IS  - 1532-0480 (Electronic)
IS  - 1532-0464 (Print)
IS  - 1532-0464 (Linking)
VI  - 148
DP  - 2023 Dec
TI  - Fair patient model: Mitigating bias in the patient representation learned from 
      the electronic health records.
PG  - 104544
LID - S1532-0464(23)00265-4 [pii]
LID - 10.1016/j.jbi.2023.104544 [doi]
AB  - OBJECTIVE: To pre-train fair and unbiased patient representations from Electronic 
      Health Records (EHRs) using a novel weighted loss function that reduces bias and 
      improves fairness in deep representation learning models. METHODS: We defined a 
      new loss function, called weighted loss function, in the deep representation 
      learning model to balance the importance of different groups of patients and 
      features. We applied the proposed model, called Fair Patient Model (FPM), to a 
      sample of 34,739 patients from the MIMIC-III dataset and learned patient 
      representations for four clinical outcome prediction tasks. RESULTS: FPM 
      outperformed the baseline models in terms of three fairness metrics: demographic 
      parity, equality of opportunity difference, and equalized odds ratio. FPM also 
      achieved comparable predictive performance with the baselines, with an average 
      accuracy of 0.7912. Feature analysis revealed that FPM captured more information 
      from clinical features than the baselines. CONCLUSION: FPM is a novel method to 
      pre-train fair and unbiased patient representations from the EHR data using a 
      weighted loss function. The learned representations can be used for various 
      downstream tasks in healthcare and can be extended to other domains where 
      fairness is important.
CI  - Copyright (c) 2023 Elsevier Inc. All rights reserved.
FAU - Sivarajkumar, Sonish
AU  - Sivarajkumar S
AD  - Intelligent Systems Program, School of Computing and Information, University of 
      Pittsburgh, Pittsburgh, PA, United States.
FAU - Huang, Yufei
AU  - Huang Y
AD  - Intelligent Systems Program, School of Computing and Information, University of 
      Pittsburgh, Pittsburgh, PA, United States; Department of Medicine, University of 
      Pittsburgh, Pittsburgh, PA, United States; Department of Electrical and Computer 
      Engineering, University of Pittsburgh, Pittsburgh, PA, United States; University 
      of Pittsburgh Medical Center Hillman Cancer Center, Pittsburgh, PA, United 
      States.
FAU - Wang, Yanshan
AU  - Wang Y
AD  - Intelligent Systems Program, School of Computing and Information, University of 
      Pittsburgh, Pittsburgh, PA, United States; Department of Health Information 
      Management, University of Pittsburgh, Pittsburgh, PA, United States; Department 
      of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, United 
      States; Clinical and Translational Science Institute, University of Pittsburgh, 
      Pittsburgh, PA, United States; University of Pittsburgh Medical Center Hillman 
      Cancer Center, Pittsburgh, PA, United States. Electronic address: 
      yanshan.wang@pitt.edu.
LA  - eng
GR  - R01 LM014306/LM/NLM NIH HHS/United States
GR  - U24 TR004111/TR/NCATS NIH HHS/United States
PT  - Journal Article
PT  - Research Support, N.I.H., Extramural
PT  - Research Support, Non-U.S. Gov't
DEP - 20231122
PL  - United States
TA  - J Biomed Inform
JT  - Journal of biomedical informatics
JID - 100970413
SB  - IM
MH  - Humans
MH  - *Electronic Health Records
MH  - Prognosis
MH  - *Benchmarking
PMC - PMC10850918
MID - NIHMS1949244
COIS- Declaration of Competing Interest The authors declare that they have no known 
      competing financial interests or personal relationships that could have appeared 
      to influence the work reported in this paper.
EDAT- 2023/11/24 00:42
MHDA- 2023/12/17 13:19
PMCR- 2024/12/01
CRDT- 2023/11/23 19:28
PHST- 2023/06/02 00:00 [received]
PHST- 2023/10/02 00:00 [revised]
PHST- 2023/11/10 00:00 [accepted]
PHST- 2024/12/01 00:00 [pmc-release]
PHST- 2023/12/17 13:19 [medline]
PHST- 2023/11/24 00:42 [pubmed]
PHST- 2023/11/23 19:28 [entrez]
AID - S1532-0464(23)00265-4 [pii]
AID - 10.1016/j.jbi.2023.104544 [doi]
PST - ppublish
SO  - J Biomed Inform. 2023 Dec;148:104544. doi: 10.1016/j.jbi.2023.104544. Epub 2023 
      Nov 22.