Creating Fair Models of Atherosclerotic Cardiovascular Disease Risk

Guidelines for the management of atherosclerotic cardiovascular disease (ASCVD) recommend the use of risk stratification models to identify patients most likely to benefit from cholesterol-lowering and other therapies. These models have differential performance across race and gender groups with inconsistent behavior across studies, potentially resulting in an inequitable distribution of beneficial therapy. In this work, we leverage adversarial learning and a large observational cohort extracted from electronic health records (EHRs) to develop a "fair" ASCVD risk prediction model with reduced variability in error rates across groups. We empirically demonstrate that our approach is capable of aligning the distribution of risk predictions conditioned on the outcome across several groups simultaneously for models built from high-dimensional EHR data. We also discuss the relevance of these results in the context of the empirical trade-off between fairness and model performance

Enabling Privacy-Preserving GWAS in Heterogeneous Human Populations

The projected increase of genotyping in the clinic and the rise of large genomic databases has led to the possibility of using patient medical data to perform genomewide association studies (GWAS) on a larger scale and at a lower cost than ever before. Due to privacy concerns, however, access to this data is limited to a few trusted individuals, greatly reducing its impact on biomedical research. Privacy preserving methods have been suggested as a way of allowing more people access to this precious data while protecting patients. In particular, there has been growing interest in applying the concept of differential privacy to GWAS results. Unfortunately, previous approaches for performing differentially private GWAS are based on rather simple statistics that have some major limitations. In particular, they do not correct for population stratification, a major issue when dealing with the genetically diverse populations present in modern GWAS. To address this concern we introduce a novel computational framework for performing GWAS that tailors ideas from differential privacy to protect private phenotype information, while at the same time correcting for population stratification. This framework allows us to produce privacy preserving GWAS results based on two of the most commonly used GWAS statistics: EIGENSTRAT and linear mixed model (LMM) based statistics. We test our differentially private statistics, PrivSTRAT and PrivLMM, on both simulated and real GWAS datasets and find that they are able to protect privacy while returning meaningful GWAS results.Comment: To be presented at RECOMB 201

J Perinatol

OBJECTIVETo quantify the importance of successful endotracheal intubation on the first attempt among extremely low birth weight (ELBW) infants who require resuscitation after delivery.STUDY DESIGNA retrospective chart review was conducted for all ELBW infants \ue2\u2030\ua41000 g born between January 2007 and May 2014 at a level IV neonatal intensive care unit. Infants were included if intubation was attempted during the first five minutes of life, or if intubation was attempted during the first 10 minutes of life with heart rate < 100. The primary outcome was death or neurodevelopmental impairment. The association between successful intubation on the first attempt and the primary outcome was assessed using multivariable logistic regression with adjustment for birth weight, gestational age, gender, and antenatal steroids.RESULTSThe study sample included 88 ELBW infants. Forty-percent were intubated on the first attempt and 60% required multiple intubation attempts. Death or neurodevelopmental impairment occurred in 29% of infants intubated on the first attempt, compared to 53% of infants that required multiple attempts, adjusted odds ratio 0.4 (95% confidence interval 0.1 - 1.0), p < 0.05.CONCLUSIONSuccessful intubation on the first attempt is associated with improved neurodevelopmental outcomes among ELBW infants. This study confirms the importance of rapid establishment of a stable airway in ELBW infants requiring resuscitation after birth and has implications for personnel selection and role assignment in the delivery room.U01 DD001033/DD/NCBDD CDC HHS/United States2016-08-01T00:00:00Z26540244PMC473126

Extraction d'association d'EIM à partir de dossiers patients : expérimentation avec les structures de patrons et les ontologies

National audienceLes Dossiers Médicaux Electroniques (DME) constituent une ressource de grand intérêt pour étudier les Evènements Indésirables Médicamenteux (EIM). Nous proposons ici de fouiller les DME pour identifier des EIM fréquemment associés dans des sous-groupes de patients. Les EIM ayant des manifestations complexes, nous utilisons l'analyse formelle de concepts et ses structures de patrons, un cadre mathématique permettant la généralisation, en exploitant les connaissances du domaine médical formalisées dans des ontologies. Les résultats obtenus dans trois expériences montrent que cette approche est flexible et permet d'extraire des règles d'association à divers niveaux de généralisation