Confidence Is All You Need for MI Attacks

In this evolving era of machine learning security, membership inference attacks have emerged as a potent threat to the confidentiality of sensitive data. In this attack, adversaries aim to determine whether a particular point was used during the training of a target model. This paper proposes a new method to gauge a data point's membership in a model's training set. Instead of correlating loss with membership, as is traditionally done, we have leveraged the fact that training examples generally exhibit higher confidence values when classified into their actual class. During training, the model is essentially being 'fit' to the training data and might face particular difficulties in generalization to unseen data. This asymmetry leads to the model achieving higher confidence on the training data as it exploits the specific patterns and noise present in the training data. Our proposed approach leverages the confidence values generated by the machine learning model. These confidence values provide a probabilistic measure of the model's certainty in its predictions and can further be used to infer the membership of a given data point. Additionally, we also introduce another variant of our method that allows us to carry out this attack without knowing the ground truth(true class) of a given data point, thus offering an edge over existing label-dependent attack methods.Comment: 2 pages, 1 figur

Association of clinical features, comorbidities and laboratory profile with outcomes among dengue patients admitted in a tertiary care hospital, Delhi NCR

Background: Dengue fever is an endemic disease across multiple countries. Dengue infection results in a wide spectrum of non-specific clinical manifestations with unpredictable clinical course and outcome. Objective of the study was to understand the association of different clinical features, comorbidities and laboratory profile with outcomes (ICU use, ventilation use and blood transfusion) among dengue patients admitted in a tertiary care hospital in Delhi, National Capital Region.Methods This cross-sectional study included 75 dengue patients with fever <1 week confirmed based on NS-1 antigen and/or IgM antibody positivity. Descriptive analysis was used.Results: Gender was not significantly associated with the outcomes. The duration of fever was significantly higher among those with ICU use (median: 6 versus 4 days; p=0.005), ventilator use (median: 5.5 versus 4.0 days; p=0.049] and blood transfusion (median: 6 versus 4 days; p=0.013). Dengue patients with co-morbidities (diabetes, hypertension, or chronic obstructive pulmonary disease) or co-infection had a significantly higher odds of the outcomes. The platelet level was significantly lower while liver enzymes were significantly higher among those with the outcomes.Conclusions: The clinical features, comorbidities and laboratory profile can help in identifying critical patients for ICU admission and timely intervention to improve outcome

Acute Kidney Injury Associated With Urinary Stone Disease in Children and Young Adults Presenting to a Pediatric Emergency Department

Background: Acute kidney injury (AKI) due to urinary stone disease (USD) is rare in adults; AKI rates in children with USD may be higher, and emerging data links stones to chronic kidney disease (CKD) development in adults. Methods: This study is a retrospective analysis of USD patients at a single pediatric hospital system's emergency department (ED). Patients were initially identified by USD ICD codes; USD was then confirmed by imaging or physician documentation; patients had to have baseline creatinine (Cr) and Cr in the ED for comparison to be included. AKI was defined by Kidney Disease: Improving Global Outcomes (KDIGO), Acute Kidney Injury Network (AKIN), and Pediatric Risk, Injury, Failure, Loss, End Stage (pRIFLE). Results: Of the 589 total visits, 264/589 (45%) had data to evaluate for AKI, 23% were AKI(+) and 77% were AKI(-). pRIFLE was most common (82%) and 18% were only positive by AKIN/KDIGO. AKI(+) were more likely to be younger (16.7 vs. 17.4 years, p = 0.046) and more likely to present with vomiting {odds ratio [OR] [95% confidence interval (CI)]: 2.4 [1.4-4.3], p = 0.002}; also, the proportion of AKI(+) was significantly higher in <18 vs. ≥18 years [26.9 vs. 15.5%, p = 0.032, OR (95% CI): 2.0 (1.1-3.9)]. Urinary tract infection (UTI) and obstruction rates were similar between groups. AKI(+) patients had a significant OR <1 suggesting less risk of receiving non-steroidal anti-inflammatory drugs (NSAIDs); however, 51% of them did receive NSAIDs during their ED encounter. AKI(+) patients were more likely to require admission to the hospital (53 vs. 32%, p = 0.001). Conclusion: We have demonstrated a novel association between USD-induced renal colic and AKI in a group of young adults and children. AKI(+) patients were younger and were more likely to present with vomiting. AKI(+) patients did not have higher rates of obstruction or UTI, and 51% of AKI(+) received NSAIDs

Confidence Is All You Need for MI Attacks (Student Abstract)

In this evolving era of machine learning security, membership inference attacks have emerged as a potent threat to the confidentiality of sensitive data. In this attack, adversaries aim to determine whether a particular point was used during the training of a target model. This paper proposes a new method to gauge a data point’s membership in a model’s training set. Instead of correlating loss with membership, as is traditionally done, we have leveraged the fact that training examples generally exhibit higher confidence values when classified into their actual class. During training, the model is essentially being ’fit’ to the training data and might face particular difficulties in generalization to unseen data. This asymmetry leads to the model achieving higher confidence on the training data as it exploits the specific patterns and noise present in the training data. Our proposed approach leverages the confidence values generated by the machine-learning model. These confidence values provide a probabilistic measure of the model’s certainty in its predictions and can further be used to infer the membership of a given data point. Additionally, we also introduce another variant of our method that allows us to carry out this attack without knowing the ground truth(true class) of a given data point, thus offering an edge over existing label-dependent attack methods

Increased delay from initial concern to diagnosis of autism spectrum disorder and associated health care resource utilization and cost among children aged younger than 6 years in the United States.

BACKGROUND: Prolonged delays between first caregiver concern and autism spectrum disorder (ASD) diagnosis have been reported, but associations between length of time to diagnosis (TTD) and health care resource utilization (HCRU) and costs have not been studied in a large sample of children with ASD. OBJECTIVE: To address these informational gaps in the ASD diagnostic pathway. METHODS: This retrospective, observational, single cohort analysis of Optums administrative claims data from January 1, 2011, to December 31, 2020, included commercially insured children who had 2 or more claims for an ASD diagnosis (earliest diagnosis designated as the index date), were between the ages of older than 1.5 years and 6 years or younger at index date, and were continuously enrolled for up to 48 months before and for 12 months after the index date. Two cohorts (between the ages of older than 1.5 years and 3 years or younger and between the ages of older than 3 years and 6 years or younger at ASD diagnosis) were divided into shorter (less than median) and longer (greater than or equal to median) TTD around each cohort median TTD calculated from the first documented ASD-related concern to the earliest ASD diagnosis, because TTD may vary by age at diagnosis. This exploratory analysis compared all-cause and ASD-related HCRU and costs during a 12-month period preceding ASD diagnosis among children with shorter vs longer TTD. RESULTS: 8,954 children met selection criteria: 4,205 aged 3 years or younger and 4,749 aged older than 3 years at diagnosis, with median TTD of 9.5 and 22.1 months, respectively. In the year preceding ASD diagnosis, children with longer TTD in both age cohorts experienced a greater number of all-cause and ASD-related health care visits compared with those with shorter TTD (mean and median number of office or home visits were approximately 1.5- and 2-fold greater in longer vs shorter TTD groups; P &lt; 0.0001). The mean all-cause medical cost per child in the year preceding ASD diagnosis was approximately 2-fold higher for those with longer vs shorter TTD ($5,268 vs$2,525 in the younger and $5,570 vs$2,265 in the older cohort; P &lt; 0.0001 for both). Mean ASD-related costs were also higher across age cohorts for those with longer vs shorter TTD ($2,355 vs$859 in the younger and $2,351 vs$1,144 in the older cohort; P &lt; 0.0001 for both). CONCLUSIONS: In the year prior to diagnosis, children with longer TTD experienced more frequent health care visits and greater cost burden in their diagnostic journey compared with children with shorter TTD. Novel diagnostic approaches that could accelerate TTD may reduce costs and HCRU for commercially insured children. DISCLOSURES: This study was funded by Cognoa, Inc. Optum received funding from Cognoa to conduct this study. Dr Salomon is an employee and holds stock options of Cognoa, Inc. Dr Campbell was an employee of Cognoa, Inc., at the time this study was conducted. Dr Duhig was an employee of Cognoa, Inc., at the time the study was conducted and holds stock options. Dr Vu, Ms Kruse, Mr Gaur, and Ms Gupta are employees and/or stockholders of Optum. Dr Tibrewal was an employee of Optum at the time the research for this study was conducted. Dr Taraman is an employee and holds stock options of Cognoa, Inc., receives consulting fees from Cognito Therapeutics, volunteers as a board member of the American Academy of Pediatrics California and Orange County Chapter, is a paid advisor for MI10 LLC, and owns stock options of NTX, Inc., and HandzIn