Zhihong Gao, Lihua Lou, Meihao Wang, Zhen Sun, Xiaodong Chen, Xiang Zhang, Zhifang Pan, Haibin Hao, Yu Zhang, Shichao Quan, Shaobo Yin, Cai Lin, Xian Shen
Application of Machine Learning in Intelligent Medical Image Diagnosis and Construction of Intelligent Service Process
Computational intelligence and neuroscience
characterize sociodemographics comorbid medical psychiatric diagnoses patients general hospital diagnosed malingering conducted retrospective observational cohort study using data 2019 national inpatient sample allpayors database acute care general hospital discharges united states querying patients aged 18 older discharged diagnosis malingerer conscious simulation icd10 code z765 45645 hospitalizations 95 ci 43503 47787 study year included discharge diagnosis malingering 561 male patients median age 43 years iqr 33 54 black patients represented 268 patients discharge diagnosis malingering compared 149 patients sampled zip codes lowest household income quartile comprised 399 malingering diagnoses top categories primary discharge diagnoses hospitalizations included medical diabetes mellitus without complications psychiatric depressive disorders substance use alcoholrelated disorders disorders sepsis unspecified organism common primary diagnosis striking overrepresentation black patients hospitalizations diagnosis malingering raises concern roles implicit systemic biases assigning label disproportionate number patients low socioeconomic status suggestive bias disparity another potential contribution lower health literacy populations results limited knowledge traditional ways meet ones needs thus greater reliance malingered behavior alternative means accurate description patients sociodemographics comorbid medical psychiatric diagnoses reliable data large samples lead improved understanding malingering label applied ultimately better patient care