Abstract Objective To determine whether graph neural network based models of electronic health records can predict specialty consultation care needs for endocrinology and hematology more accurately than the standard of care checklists and other conventional medical recommendation algorithms in the literature. Methods Demand for medical expertise far outstrips supply, with tens of millions in the US alone with deficient access to specialty care. Rather than potentially months long delays to initiate diagnostic workup and medical treatment with a specialist, referring primary care supported by an automated recommender algorithm could anticipate and directly initiate patient evaluation that would otherwise be needed at subsequent a specialist appointment. We propose a novel graph representation learning approach with a heterogeneous graph neural network to model structured electronic health records and formulate recommendation/prediction of subsequent specialist orders as a link prediction problem. Results Models are trained and assessed in two specialty care sites: endocrinology and hematology. Our experimental results show that our model achieves an 8% improvement in ROC-AUC for endocrinology (ROC-AUC=0.88) and 5% improvement for hematology (ROC-AUC=0.84) personalized procedure recommendations over prior medical recommender systems. These recommender algorithm approaches provide medical procedure recommendations for endocrinology referrals more effectively than manual clinical checklists (recommender: precision=0.60, recall=0.27, F1-score=0.37) vs. (checklist: precision=0.16, recall=0.28, F1-score=0.20), and similarly for hematology referrals (recommender: precision=0.44, recall=0.38, F1-score=0.41) vs. (checklist: precision=0.27, recall=0.71, F1-score=0.39). Conclusion Embedding graph neural network models into clinical care can improve digital specialty consultation systems and expand the access to medical experience of prior similar cases.
We investigated how artificial intelligence (AI) reveals factors shaping COVID-19 vaccine hesitancy among healthcare providers by examining their open-text comments. We conducted a longitudinal survey starting in Spring of 2020 with 38,788 current and former female nurses in three national cohorts to assess how the pandemic has affected their livelihood. In January and March–April 2021 surveys, participants were invited to contribute open-text comments and answer specific questions about COVID-19 vaccine uptake. A closed-ended question in the survey identified vaccine-hesitant (VH) participants who either had no intention or were unsure of receiving a COVID-19 vaccine. We collected 1970 comments from VH participants and trained two machine learning (ML) algorithms to identify behavioral factors related to VH. The first predictive model classified each comment into one of three health belief model (HBM) constructs (barriers, severity, and susceptibility) related to adopting disease prevention activities. The second predictive model used the words in January comments to predict the vaccine status of VH in March–April 2021; vaccine status was correctly predicted 89% of the time. Our results showed that 35% of VH participants cited barriers, 17% severity, and 7% susceptibility to receiving a COVID-19 vaccine. Out of the HBM constructs, the VH participants citing a barrier, such as allergic reactions and side effects, had the most associated change in vaccine status from VH to later receiving a vaccine.
In this paper, an efficient, novel neuro-evolutionary algorithm to navigate a mobile robot in partially visible environments is introduced. The main disadvantage of Neuro-Evolutionary algorithm is the slow perception and low efficiency in complex environments which is required to be developed. This research is aimed to speed up the iteration and improve the performance in complicated ambient. In the typical neuro-evolutionary algorithm, random values are employed either in weights initialization of neural networks or during the training phase. To do so, this research employed a novel method in which robot navigation will be done by using selected values by 3 neural networks rather than one which improve the performance of learning procedure. Another novel method used in this article is replacing the neural networks which are responsible for obstacle avoidance by fuzzy algorithm. It will be shown that fuzzy logic is an easy way to put some initial knowledge in the neuro-evolution algorithm to avoid learning from zero. The results clearly demonstrate that the training algorithm approaches the optimum values with the least iterations which not only reduce the required time for reaching the target but also materialize the obstacle avoidance aim.
Myeloid cells dominate metabolic disease-associated inflammation (metaflammation) in mouse obesity, but the contributions of myeloid cells to the peripheral inflammation that fuels sequelae of human obesity are untested. This study used unbiased approaches to rank contributions of myeloid and T cells to peripheral inflammation in people with obesity across the spectrum of metabolic health.
Target trial emulation is the process of mimicking target randomized trials using real-world data, where effective confounding control for unbiased treatment effect estimation remains a main challenge. Although various approaches have been proposed for this challenge, a systematic evaluation is still lacking. Here we emulated trials for thousands of medications from two large-scale real-world data warehouses, covering over 10 years of clinical records for over 170 million patients, aiming to identify new indications of approved drugs for Alzheimer's disease. We assessed different propensity score models under the inverse probability of treatment weighting framework and suggested a model selection strategy for improved baseline covariate balancing. We also found that the deep learning-based propensity score model did not necessarily outperform logistic regression-based methods in covariate balancing. Finally, we highlighted five top-ranked drugs (pantoprazole, gabapentin, atorvastatin, fluticasone, and omeprazole) originally intended for other indications with potential benefits for Alzheimer's patients.
The Negative Selection Algorithm is an anomaly detection technique, inspired by the self-nonself discrimination behavior observed in the Biological Immune Systems. The most controversial problem of the negative selection algorithm is its inherent limitation in detecting foreign patterns as anomalies. This limitation causes high false positive rate in anomaly detection systems which are based on the negative selection algorithm. To tackle this limitation, this paper introduces an efficient negative selection algorithm by focusing on generating more efficient detectors using a more flexible boundary for self-patterns. In other words, instead of applying conventional affinity measures, a Gaussian Mixture Model is fitted on normal space so that detectors are generated employing this Gaussian Mixture Model of self-space. The efficiency of the proposed algorithm is evaluated using different data sets including 2D synthesis data sets and NSL-KDD data set. The results indicate that generating detectors based on the Gaussian mixture model leads to more efficient negative selection algorithm even in real application with high dimensions where the traditional negative selection algorithms have limitations.
About 20% of individuals with attention deficit hyperactivity disorder are first diagnosed during adolescence. While preclinical experiments suggest that adolescent-onset exposure to attention deficit hyperactivity disorder medication is an important factor in the development of substance use disorder phenotypes in adulthood, the long-term impact of attention deficit hyperactivity disorder medication initiated during adolescence has been largely unexplored in humans. Our analysis of 11,624 adolescent enrollees with attention deficit hyperactivity disorder in the Truven database indicates that temporal medication features, rather than stationary features, are the most important factors on the health consequences related to substance use disorder and attention deficit hyperactivity disorder medication initiation during adolescence.
Abstract The availability of large healthcare datasets offers the opportunity for researchers to navigate the traditional clinical and translational science research stages in a nonlinear manner. In particular, data scientists can harness the power of large healthcare datasets to bridge from preclinical discoveries (T0) directly to assessing population-level health impact (T4). A successful bridge from T0 to T4 does not bypass the other stages entirely; rather, effective team science makes a direct progression from T0 to T4 impactful by incorporating the perspectives of researchers from every stage of the clinical and translational science research spectrum. In this exemplar, we demonstrate how effective team science overcame challenges and, ultimately, ensured success when a diverse team of researchers worked together, using healthcare big data to test population-level substance use disorder (SUD) hypotheses generated from preclinical rodent studies. This project, called Advancing Substance use disorder Knowledge using Big Data (ASK Big Data), highlights the critical roles that data science expertise and effective team science play in quickly translating preclinical research into public health impact.
