Safatli D, Günther A, Schlattmann P et al (2016) Predictors of 30-day mortality in patients with spontaneous primary intracerebral hemorrhage. Surg Neurol Int 7:510. https://doi.org/10.4103/2152-7806.187493
Macellari F, Paciaroni M, Agnelli G, Caso V (2014) Neuroimaging in Intracerebral Hemorrhage. Stroke 45:903–908. https://doi.org/10.1161/STROKEAHA.113.003701
Powers WJ, Rabinstein AA, Ackerson T et al (2019) Guidelines for the early management of patients with Acute ischemic stroke: 2019 update to the 2018 guidelines for the early management of Acute ischemic stroke: a Guideline for Healthcare professionals from the American Heart Association/American Stroke Association. Stroke 50:e344–e418. https://doi.org/10.1161/STR.0000000000000211
Benjamens S, Dhunnoo P, Meskó B (2020) The state of artificial intelligence-based FDA-approved medical devices and algorithms: an online database. NPJ Digit Med 3:118. https://doi.org/10.1038/s41746-020-00324-0
Article PubMed PubMed Central Google Scholar
Ginat DT (2020) Analysis of head CT scans flagged by deep learning software for acute intracranial hemorrhage. Neuroradiology 62:335–340. https://doi.org/10.1007/s00234-019-02330-w
Zia A, Fletcher C, Bigwood S et al (2022) Retrospective analysis and prospective validation of an AI-based software for intracranial haemorrhage detection at a high-volume trauma centre. Sci Rep 12:19885. https://doi.org/10.1038/s41598-022-24504-y
Article PubMed PubMed Central CAS Google Scholar
Beheshtian E, Putman K, Santomartino SM et al (2023) Generalizability and Bias in a Deep Learning Pediatric Bone Age Prediction Model using hand radiographs. Radiology 306:e220505. https://doi.org/10.1148/radiol.220505
Juhn YJ, Ryu E, Wi C-I et al (2022) Assessing socioeconomic bias in machine learning algorithms in health care: a case study of the HOUSES index. J Am Med Inf Assoc 29:1142–1151. https://doi.org/10.1093/jamia/ocac052
Lahti A-M, Nätynki M, Huhtakangas J et al (2021) Long-term survival after primary intracerebral hemorrhage: a population-based case–control study spanning a quarter of a century. Eur J Neurol 28:3663–3669. https://doi.org/10.1111/ene.14988
Craen A, Mangal R, Stead TG, Ganti L Gender differences in outcomes after non-traumatic intracerebral hemorrhage. Cureus 11:e5818. https://doi.org/10.7759/cureus.5818
Leasure AC, King ZA, Torres-Lopez V et al (2020) Racial/ethnic disparities in the risk of intracerebral hemorrhage recurrence. Neurology 94:e314–e322. https://doi.org/10.1212/WNL.0000000000008737
Article PubMed PubMed Central Google Scholar
Kim J, Kitlen E, Torres-Lopez V et al (2023) Neighborhood disadvantage and outcomes following intracerebral hemorrhage (S29.002). https://doi.org/10.1212/WNL.0000000000203392. Neurology 100:
Voter AF, Meram E, Garrett JW, Yu J-PJ (2021) Diagnostic accuracy and failure Mode Analysis of a deep learning algorithm for the detection of intracranial hemorrhage. J Am Coll Radiol 18:1143–1152. https://doi.org/10.1016/j.jacr.2021.03.005
Article PubMed PubMed Central Google Scholar
Yg C, Mm M, Bd P (2019) Prescreening for Intracranial Hemorrhage on CT Head scans with an AI-Based Radiology Workflow Triage Tool: an Accuracy Study. J Med Diagn Methods 8:1–5
U.S (2020) Centers for Medicare & Medicaid Services Age and Sex Estimates in the National Health Expenditure accounts. Definitions, Sources, and Methods
Jensen E, Jones N, Orozco K et al (2021) Measuring Racial and Ethnic Diversity for the 2020 Census. In: Census.gov. https://www.census.gov/newsroom/blogs/random-samplings/2021/08/measuring-racial-ethnic-diversity-2020-census.html. Accessed 3 Nov 2023
Barber LE, Zirpoli GR, Cozier YC et al (2021) Neighborhood disadvantage and individual-level life stressors in relation to breast cancer incidence in US black women. Breast Cancer Res 23:108. https://doi.org/10.1186/s13058-021-01483-y
Article PubMed PubMed Central Google Scholar
Ginat D (2021) Implementation of Machine Learning Software on the Radiology Worklist decreases scan View Delay for the detection of intracranial hemorrhage on CT. Brain Sci 11:832. https://doi.org/10.3390/brainsci11070832
Article PubMed PubMed Central Google Scholar
Seyyed-Kalantari L, Zhang H, McDermott MBA et al (2021) Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in under-served patient populations. Nat Med 27:2176–2182. https://doi.org/10.1038/s41591-021-01595-0
Article PubMed PubMed Central CAS Google Scholar
Bako AT, Pan A, Potter T et al (2022) Contemporary trends in the Nationwide incidence of primary intracerebral hemorrhage. Stroke 53:e70–e74. https://doi.org/10.1161/STROKEAHA.121.037332
Lioutas V-A, Beiser AS, Aparicio HJ et al (2020) Assessment of incidence and risk factors of Intracerebral Hemorrhage among participants in the Framingham Heart Study between 1948 and 2016. JAMA Neurol 77:1252–1260. https://doi.org/10.1001/jamaneurol.2020.1512
Article PubMed PubMed Central Google Scholar
Gokhale S, Caplan LR, James ML (2015) Sex differences in incidence, pathophysiology, and outcome of primary intracerebral hemorrhage. Stroke 46:886–892. https://doi.org/10.1161/STROKEAHA.114.007682
Zhang S, Shu Y, Li W et al (2022) High haemoglobin levels and mortality in males with intracerebral haemorrhage: a retrospective cohort study. BMJ Open 12:e048108. https://doi.org/10.1136/bmjopen-2020-048108
Article PubMed PubMed Central Google Scholar
Bruni SG, Patafio FM, Dufton JA et al (2013) The assessment of anemia from attenuation values of cranial venous drainage on unenhanced computed tomography of the head. Can Assoc Radiol J 64:46–50. https://doi.org/10.1016/j.carj.2011.08.005
Li P, Cheng Z, yan, Liu G (2020) lin Availability Bias Causes Misdiagnoses by Physicians: Direct Evidence from a Randomized Controlled Trial. Intern Med 59:3141–3146. https://doi.org/10.2169/internalmedicine.4664-20
Mattocks K, Casares J, Brown A et al (2020) Women veterans’ experiences with perceived gender Bias in U.S. Department of Veterans Affairs Specialty Care. Womens Health Issues 30:113–119. https://doi.org/10.1016/j.whi.2019.10.003
Yang J, Soltan AAS, Eyre DW, Clifton DA (2023) Algorithmic fairness and bias mitigation for clinical machine learning with deep reinforcement learning. Nat Mach Intell 5:884–894. https://doi.org/10.1038/s42256-023-00697-3
Article PubMed PubMed Central Google Scholar
Seyyed-Kalantari L, Liu G, McDermott M et al (2021) CheXclusion: Fairness gaps in deep chest X-ray classifiers. Pac Symp Biocomput 26:232–243
Gichoya JW, Banerjee I, Bhimireddy AR et al (2022) AI recognition of patient race in medical imaging: a modelling study. Lancet Digit Health 4:e406–e414. https://doi.org/10.1016/S2589-7500(22)00063-2
Article PubMed PubMed Central CAS Google Scholar
Hobson C, Dortch J, Ozrazgat Baslanti T et al (2014) Insurance status is Associated with Treatment Allocation and outcomes after Subarachnoid Hemorrhage. PLoS ONE 9:e105124. https://doi.org/10.1371/journal.pone.0105124
Article PubMed PubMed Central CAS Google Scholar
Uscher-Pines L, Pines J, Kellermann A et al (2013) Deciding to visit the Emergency Department for non-urgent conditions: a systematic review of the literature. Am J Manag Care 19:47–59
PubMed PubMed Central Google Scholar
Bhayana R, Vermeulen MJ, Li Q et al (2014) Socioeconomic status and the use of computed tomography in the emergency department. CJEM 16:288–295. https://doi.org/10.2310/8000.2013.131102
Jencks SF, Schuster A, Dougherty GB et al (2019) Safety-Net hospitals, Neighborhood Disadvantage, and readmissions under Maryland’s all-payer program: an observational study. Ann Intern Med 171:91–98. https://doi.org/10.7326/M16-2671
Article PubMed PubMed Central Google Scholar
Neighborhood Atlas - Changelog https://www.neighborhoodatlas.medicine.wisc.edu/changelog. Accessed 3 Nov 2023
Sabottke CF, Spieler BM (2020) The Effect of Image Resolution on Deep Learning in Radiography. Radiol Artif Intell 2:e190015. https://doi.org/10.1148/ryai.2019190015
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