Causal-structure Driven Augmentations for Text OOD Generalization

The reliance of text classifiers on spurious correlations can lead to poor generalization at deployment, raising concerns about their use in safety-critical domains such as healthcare. In this work, we propose to use counterfactual data augmentation, guided by knowledge of the causal structure of the data, to simulate interventions on spurious features and to learn more robust text classifiers. We show that this strategy is appropriate in prediction problems where the label is spuriously correlated with an attribute. Under the assumptions of such problems, we discuss the favorable sample complexity of counterfactual data augmentation, compared to importance re-weighting. Pragmatically, we match examples using auxiliary data, based on diff-in-diff methodology, and use a large language model (LLM) to represent a conditional probability of text. Through extensive experimentation on learning caregiver-invariant predictors of clinical diagnoses from medical narratives and on semi-synthetic data, we demonstrate that our method for simulating interventions improves out-of-distribution (OOD) accuracy compared to baseline invariant learning algorithms.Comment: Forthcoming in NeurIPS 202

Artificial intelligence approaches to predicting and detecting cognitive decline in older adults: A conceptual review.

Preserving cognition and mental capacity is critical to aging with autonomy. Early detection of pathological cognitive decline facilitates the greatest impact of restorative or preventative treatments. Artificial Intelligence (AI) in healthcare is the use of computational algorithms that mimic human cognitive functions to analyze complex medical data. AI technologies like machine learning (ML) support the integration of biological, psychological, and social factors when approaching diagnosis, prognosis, and treatment of disease. This paper serves to acquaint clinicians and other stakeholders with the use, benefits, and limitations of AI for predicting, diagnosing, and classifying mild and major neurocognitive impairments, by providing a conceptual overview of this topic with emphasis on the features explored and AI techniques employed. We present studies that fell into six categories of features used for these purposes: (1) sociodemographics; (2) clinical and psychometric assessments; (3) neuroimaging and neurophysiology; (4) electronic health records and claims; (5) novel assessments (e.g., sensors for digital data); and (6) genomics/other omics. For each category we provide examples of AI approaches, including supervised and unsupervised ML, deep learning, and natural language processing. AI technology, still nascent in healthcare, has great potential to transform the way we diagnose and treat patients with neurocognitive disorders

Grit Associated with New Graduate Registered Nurse Initial Competency

The delivery of highly reliable health care is in jeopardy as many new graduate registered nurses (NGRNs) enter clinical roles under-prepared for demands of professional practice. Identifying and addressing challenges to safe practice early in the onboarding process were paramount for patient safety at a large Midwestern healthcare system (HCS). Post-hire and pre-practice Performance Based Development System (PBDS) assessments were administered to more than 7,600 NGRNs between January 2011 and December 2018. Only 19% of NGRNs demonstrated entry-level competencies and practice readiness; 26% were unsafe for novice independent practice. Data analysis revealed no differences in competence ratings by nursing degree or program type. Factors that drive or support NGRN competency (other than intelligence), such as Grit, defined as passion and perseverance for long-term goals, were unknown. Self-determination theory was used as the theoretical framework to underpin the study. This quantitative, non-experimental, correlational study sought to explore if there is a relationship between Grit, as measured by the original 12 item Grit scale (Grit-O), and initial competency of NGRNs based on PBDS assessment groupings of low, medium, and high competency to practice. The study used de-identified retrospective data collected as part of the onboarding process for NGRNs hired between July and December of 2018. The independent predictor variable was level of Grit as measured by the self-reported Grit-O scale. The dependent variable was initial competency/practice readiness as measured by PBDS. In data analysis, Grit was not a predictor of NGRN initial competence or practice readiness as measured by PBDS (p-value 0.77)