Comprehensive overview of the anesthesiology research landscape: A machine Learning Analysis of 737 NIH-funded anesthesiology primary Investigator's publication trends.

Background: Anesthesiology plays a crucial role in perioperative care, critical care, and pain management, impacting patient experiences and clinical outcomes. However, our understanding of the anesthesiology research landscape is limited. Accordingly, we initiated a data-driven analysis through topic modeling to uncover research trends, enabling informed decision-making and fostering progress within the field. Methods: The easyPubMed R package was used to collect 32,300 PubMed abstracts spanning from 2000 to 2022. These abstracts were authored by 737 Anesthesiology Principal Investigators (PIs) who were recipients of National Institute of Health (NIH) funding from 2010 to 2022. Abstracts were preprocessed, vectorized, and analyzed with the state-of-the-art BERTopic algorithm to identify pillar topics and trending subtopics within anesthesiology research. Temporal trends were assessed using the Mann-Kendall test. Results: The publishing journals with most abstracts in this dataset were Anesthesia & Analgesia 1133, Anesthesiology 992, and Pain 671. Eight pillar topics were identified and categorized as basic or clinical sciences based on a hierarchical clustering analysis. Amongst the pillar topics, "Cells & Proteomics" had both the highest annual and total number of abstracts. Interestingly, there was an overall upward trend for all topics spanning the years 2000-2022. However, when focusing on the period from 2015 to 2022, topics "Cells & Proteomics" and "Pulmonology" exhibit a downward trajectory. Additionally, various subtopics were identified, with notable increasing trends in "Aneurysms", "Covid 19 Pandemic", and "Artificial intelligence & Machine Learning". Conclusion: Our work offers a comprehensive analysis of the anesthesiology research landscape by providing insights into pillar topics, and trending subtopics. These findings contribute to a better understanding of anesthesiology research and can guide future directions.

RETRACTED: 60 Predicting chorioamnionitis using AI-based methods: a retrospective cohort study.

This article has been retracted: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/policies/article-withdrawal). This meeting abstract has been retracted at the request of the authors. The team determined further analysis is warranted before the formal presentation of the results.

Generalist versus Specialist Self-Replicators.

Darwinian evolution, including the selection of the fittest species under given environmental conditions, is a major milestone in the development of synthetic living systems. In this regard, generalist or specialist behavior (the ability to replicate in a broader or narrower, more specific food environment) are of importance. Here we demonstrate generalist and specialist behavior in dynamic combinatorial libraries composed of a peptide-based and an oligo(ethylene glycol) based building block. Three different sets of macrocyclic replicators could be distinguished based on their supramolecular organization: two prepared from a single building block as well as one prepared from an equimolar mixture of them. Peptide-containing hexamer replicators were found to be generalists, i. e. they could replicate in a broad range of food niches, whereas the octamer peptide-based replicator and hexameric ethyleneoxide-based replicator were proven to be specialists, i. e. they only replicate in very specific food niches that correspond to their composition. However, sequence specificity cannot be demonstrated for either of the generalist replicators. The generalist versus specialist nature of these replicators was linked to their supramolecular organization. Assembly modes that accommodate structurally different building blocks lead to generalist replicators, while assembly modes that are more restrictive yield specialist replicators.

Hydroxychloroquine in Lupus Pregnancy and Risk of Preeclampsia.

OBJECTIVE: Systemic lupus erythematosus (SLE) disproportionately affects women during childbearing years, and hydroxychloroquine (HCQ) is the standard first-line treatment. Preeclampsia complicates up to one-third of pregnancies in lupus patients, although reports vary by parity and multifetal gestation. We investigated whether taking HCQ early in pregnancy may reduce the risk of preeclampsia. METHODS: We studied 1,068 live birth singleton pregnancies among 1,020 privately insured patients with SLE (2007-2016). HCQ treatment was defined as three months preconception through the first trimester, and prescription fills were a proxy for taking HCQ. Modified Poisson regression estimated risk ratios (RRs) and 95% confidence intervals (CIs), stratified by parity. Propensity scores accounted for confounders, and stratified analyses examined effect modification. RESULTS: Approximately 15% of pregnant patients were diagnosed with preeclampsia. In 52% of pregnancies, patients had one or more HCQ fills. Pregnant patients exposed to HCQ had more comorbidities, SLE activity, and azathioprine treatment. We found no evidence of a statistical association between HCQ and preeclampsia among nulliparous (RR 1.26 [95% CI 0.82-1.93]) and multiparous pregnancies (RR 1.20 [95% CI 0.80-1.70]). Additional controls for confounding decreased the RRs toward the null (nulliparous pregnancy, propensity score-adjusted [PS-adj] RR 1.09 [95% CI 0.68-1.76]; multiparous pregnancy, PS-adj RR 1.01 [95% CI 0.66-1.53]). CONCLUSION: Using a large insurance-based database, we did not observe a decreased risk of preeclampsia associated with HCQ treatment in pregnancy, although we cannot rule out residual and unmeasured confounding and misclassification. Further studies leveraging large population-based data and prospective collection could characterize how HCQ influences preeclampsia risk in pregnant patients with SLE and among persons at greater risk of hypertensive disorders of pregnancy.

Discovery of sparse, reliable omic biomarkers with Stabl.

Adoption of high-content omic technologies in clinical studies, coupled with computational methods, has yielded an abundance of candidate biomarkers. However, translating such findings into bona fide clinical biomarkers remains challenging. To facilitate this process, we introduce Stabl, a general machine learning method that identifies a sparse, reliable set of biomarkers by integrating noise injection and a data-driven signal-to-noise threshold into multivariable predictive modeling. Evaluation of Stabl on synthetic datasets and five independent clinical studies demonstrates improved biomarker sparsity and reliability compared to commonly used sparsity-promoting regularization methods while maintaining predictive performance; it distills datasets containing 1,400-35,000 features down to 4-34 candidate biomarkers. Stabl extends to multi-omic integration tasks, enabling biological interpretation of complex predictive models, as it hones in on a shortlist of proteomic, metabolomic and cytometric events predicting labor onset, microbial biomarkers of pre-term birth and a pre-operative immune signature of post-surgical infections. Stabl is available at https://github.com/gregbellan/Stabl .

Large-scale correlation network construction for unraveling the coordination of complex biological systems.

Advanced measurement and data storage technologies have enabled high-dimensional profiling of complex biological systems. For this, modern multiomics studies regularly produce datasets with hundreds of thousands of measurements per sample, enabling a new era of precision medicine. Correlation analysis is an important first step to gain deeper insights into the coordination and underlying processes of such complex systems. However, the construction of large correlation networks in modern high-dimensional datasets remains a major computational challenge owing to rapidly growing runtime and memory requirements. Here we address this challenge by introducing CorALS (Correlation Analysis of Large-scale (biological) Systems), an open-source framework for the construction and analysis of large-scale parametric as well as non-parametric correlation networks for high-dimensional biological data. It features off-the-shelf algorithms suitable for both personal and high-performance computers, enabling workflows and downstream analysis approaches. We illustrate the broad scope and potential of CorALS by exploring perspectives on complex biological processes in large-scale multiomics and single-cell studies.

Hantavirus Pulmonary Syndrome Caused by Puumala Orthohantavirus-A Case Report and Literature Review.

In this article, we report on a rare case of acute respiratory distress syndrome (ARDS) caused by the Puumala orthohantavirus (PUUV), which is typically associated with hemorrhagic fever with renal syndrome (HFRS). This is the first documented case of PUUV-associated ARDS in Southeast Europe. The diagnosis was confirmed by serum RT-PCR and serology and corroborated by phylogenetic analysis and chemokine profiling. The patient was a 23-year-old male from Zagreb, Croatia, who had recently traveled throughout Europe. He presented with fever, headache, abdominal pain, and sudden onset of ARDS. Treatment involved high-flow nasal cannula oxygen therapy and glucocorticoids, which resulted in a full recovery. A systematic literature review identified 10 cases of hantavirus pulmonary syndrome (HPS) caused by PUUV in various European countries and Turkey between 2002 and 2023. The median age of patients was 53 years (range 24-73), and six of the patients were male. Most patients were treated in intensive care units, but none received antiviral therapy targeting PUUV. Eight patients survived hospitalization. The presented case highlights the importance of considering HPS in the differential diagnosis of ARDS, even in areas where HFRS is the dominant form of hantavirus infection.

From the stomach to locus coeruleus: new neural substrate for ghrelin's effects on ingestive, motivated and anxiety-like behaviors.

Ghrelin, a stomach-derived orexigenic hormone, has a well-established role in energy homeostasis, food reward, and emotionality. Noradrenergic neurons of the locus coeruleus (LC) are known to play an important role in arousal, emotion, cognition, but recently have also been implicated in control of feeding behavior. Ghrelin receptors (the growth hormone secretagogue receptor, GHSR) may be found in the LC, but the behavioral effects of ghrelin signaling in this area are still unexplored. Here, we first determined whether GHSR are present in the rat LC, and demonstrate that GHSR are expressed on noradrenergic neurons in both sexes. We next investigated whether ghrelin controls ingestive and motivated behaviors as well as anxiety-like behavior by acting in the LC. To pursue this idea, we examined the effects of LC GHSR stimulation and blockade on food intake, operant responding for a palatable food reward and, anxiety-like behavior in the open field (OF) and acoustic startle response (ASR) tests in male and female rats. Our results demonstrate that intra-LC ghrelin administration increases chow intake and motivated behavior for sucrose in both sexes. Additionally, females, but not males, exhibited a potent anxiolytic response in the ASR. In order to determine whether activation of GHSR in the LC was necessary for feeding and anxiety behavior control, we utilized liver-expressed antimicrobial peptide 2 (LEAP2), a newly identified endogenous GHSR antagonist. LEAP2 delivered specifically into the LC was sufficient to reduce fasting-induced chow hyperphagia in both sexes, but food reward only in females. Moreover, blockade of GHSR in the LC increased anxiety-like behavior measured in the ASR test in both sexes. Taken together, these results indicate that ghrelin acts in the LC to alter ingestive, motivated and anxiety-like behaviors, with a degree of sex divergence.

Deep representation learning identifies associations between physical activity and sleep patterns during pregnancy and prematurity.

Preterm birth (PTB) is the leading cause of infant mortality globally. Research has focused on developing predictive models for PTB without prioritizing cost-effective interventions. Physical activity and sleep present unique opportunities for interventions in low- and middle-income populations (LMICs). However, objective measurement of physical activity and sleep remains challenging and self-reported metrics suffer from low-resolution and accuracy. In this study, we use physical activity data collected using a wearable device comprising over 181,944 h of data across N = 1083 patients. Using a new state-of-the art deep learning time-series classification architecture, we develop a 'clock' of healthy dynamics during pregnancy by using gestational age (GA) as a surrogate for progression of pregnancy. We also develop novel interpretability algorithms that integrate unsupervised clustering, model error analysis, feature attribution, and automated actigraphy analysis, allowing for model interpretation with respect to sleep, activity, and clinical variables. Our model performs significantly better than 7 other machine learning and AI methods for modeling the progression of pregnancy. We found that deviations from a normal 'clock' of physical activity and sleep changes during pregnancy are strongly associated with pregnancy outcomes. When our model underestimates GA, there are 0.52 fewer preterm births than expected (P = 1.01e - 67, permutation test) and when our model overestimates GA, there are 1.44 times (P = 2.82e - 39, permutation test) more preterm births than expected. Model error is negatively correlated with interdaily stability (P = 0.043, Spearman's), indicating that our model assigns a more advanced GA when an individual's daily rhythms are less precise. Supporting this, our model attributes higher importance to sleep periods in predicting higher-than-actual GA, relative to lower-than-actual GA (P = 1.01e - 21, Mann-Whitney U). Combining prediction and interpretability allows us to signal when activity behaviors alter the likelihood of preterm birth and advocates for the development of clinical decision support through passive monitoring and exercise habit and sleep recommendations, which can be easily implemented in LMICs.

Therapeutic Potential of BMP7 in the Treatment of Osteoporosis Caused by the Interaction between Inflammation and Corticosteroids in Inflammatory Bowel Disease.

Individuals with inflammatory bowel disease (IBD) have an increased risk of bone impairment, which is a process controlled by the RANKL/RANK/OPG system, mostly due to chronic inflammation and corticosteroid treatment. Bone morphogenic protein 7 (BMP7) has a complex role in maintaining inflammation and bone remodeling but little is known about its anti-inflammatory potential in chronic colitis. We investigated the effect of systemically administered BMP7 and corticosteroids on the severity of inflammation, macrophage differentiation, and bone regeneration in a chronic IBD model. METHODS: Chronic colitis was induced in male Sprague Dawley rats via weekly administration of 2,4,6-trinitrobenzenesulfonic acid over 21 days following BMP7 or corticosteroid treatment for five days. The levels of serum and colon tissue inflammatory cytokines, RANKL/OPG system, as well as markers of macrophage polarization, were detected using RT-PCR, ELISA, or immunohistochemistry. Long bone and spine analyses were performed using microcomputed tomography (micro-CT). RESULTS: The administration of BMP7 reduced the adverse effects of colitis and led to elevated OPG and RANK in the colon with a simultaneous decrease in TNF-α and an increase in IL-10 and TGF-ß. Decreased expression of the M2 macrophage marker CD163 was found in the BMP7-treated rats compared with the colitis group, whereas the number of M1 marker iNOS-positive cells did not differ between the groups. As a result of the BMP7 treatment, morphometric parameters of trabecular bone increased, and increased trabecular separation noted in the colitis group did not appear. CONCLUSIONS: We showed that BMP7 suppressed the inflammatory response in chronic colitis, mainly by shifting the cytokine balance and by triggering alterations in the RANKL/OPG system rather than through a macrophage polarization imbalance. In addition, considering the demonstrated effect of BMP7 on bone morphology and structure, it can be suggested that BMP7 plays a role in the managing of osteoporosis in chronic colitis, and thus, its therapeutic potential in the treatment of IBD should be further evaluated.

Development of a Urine Metabolomics Biomarker-Based Prediction Model for Preeclampsia during Early Pregnancy.

Preeclampsia (PE) is a condition that poses a significant risk of maternal mortality and multiple organ failure during pregnancy. Early prediction of PE can enable timely surveillance and interventions, such as low-dose aspirin administration. In this study, conducted at Stanford Health Care, we examined a cohort of 60 pregnant women and collected 478 urine samples between gestational weeks 8 and 20 for comprehensive metabolomic profiling. By employing liquid chromatography mass spectrometry (LCMS/MS), we identified the structures of seven out of 26 metabolomics biomarkers detected. Utilizing the XGBoost algorithm, we developed a predictive model based on these seven metabolomics biomarkers to identify individuals at risk of developing PE. The performance of the model was evaluated using 10-fold cross-validation, yielding an area under the receiver operating characteristic curve of 0.856. Our findings suggest that measuring urinary metabolomics biomarkers offers a noninvasive approach to assess the risk of PE prior to its onset.

Persistent Bacterial Vaginosis and Risk for Spontaneous Preterm Birth.

OBJECTIVE: The aim of this study was to determine the association between persistent bacterial vaginosis (BV) in pregnancy and risk for spontaneous preterm birth (sPTB). STUDY DESIGN: Retrospective data from IBM MarketScan Commercial Database were analyzed. Women aged between 12 and 55 years with singleton gestations were included and linked to an outpatient medications database and medications prescribed during the pregnancy were analyzed. BV in pregnancy was determined based on both a diagnosis of BV and treatment with metronidazole and/or clindamycin, and persistent treatment of BV was defined as BV in more than one trimester or BV requiring more than one antibiotic prescription. Odds ratios were calculated comparing sPTB frequencies in those with BV, or persistent BV, to women without BV in pregnancy. Survival analysis using Kaplan-Meier curves for the gestational age at delivery was also performed. RESULTS: Among a cohort of 2,538,606 women, 216,611 had an associated International Classification of Diseases, 9th Revision or 10th Revision code for diagnosis of BV alone, and 63,817 had both a diagnosis of BV and were treated with metronidazole and/or clindamycin. Overall, the frequency of sPTB among women treated with BV was 7.5% compared with 5.7% for women without BV who did not receive antibiotics. Relative to those without BV in pregnancy, odds ratios for sPTB were highest in those treated for BV in both the first and second trimester (1.66 [95% confidence interval [CI]: 1.52, 1.81]) or those with three or more prescriptions in pregnancy (1.48 [95% CI: 1.35, 1.63]. CONCLUSION: Persistent BV may have a higher risk for sPTB than a single episode of BV in pregnancy. KEY POINTS: · Persistent BV beyond one trimester may increase the risk for sPTB.. · Persistent BV requiring more than one prescription may increase the risk for sPTB.. · Almost half of antibiotic prescriptions treating BV in pregnancy are filled after 20 weeks gestation..

Structure elucidation and characterization of patulin synthase, insights into the formation of a fungal mycotoxin.

Patulin synthase (PatE) from Penicillium expansum is a flavin-dependent enzyme that catalyses the last step in the biosynthesis of the mycotoxin patulin. This secondary metabolite is often present in fruit and fruit-derived products, causing postharvest losses. The patE gene was expressed in Aspergillus niger allowing purification and characterization of PatE. This confirmed that PatE is active not only on the proposed patulin precursor ascladiol but also on several aromatic alcohols including 5-hydroxymethylfurfural. By elucidating its crystal structure, details on its catalytic mechanism were revealed. Several aspects of the active site architecture are reminiscent of that of fungal aryl-alcohol oxidases. Yet, PatE is most efficient with ascladiol as substrate confirming its dedicated role in biosynthesis of patulin.

Target-agnostic drug prediction integrated with medical record analysis uncovers differential associations of statins with increased survival in COVID-19 patients.

Drug repurposing requires distinguishing established drug class targets from novel molecule-specific mechanisms and rapidly derisking their therapeutic potential in a time-critical manner, particularly in a pandemic scenario. In response to the challenge to rapidly identify treatment options for COVID-19, several studies reported that statins, as a drug class, reduce mortality in these patients. However, it is unknown if different statins exhibit consistent function or may have varying therapeutic benefit. A Bayesian network tool was used to predict drugs that shift the host transcriptomic response to SARS-CoV-2 infection towards a healthy state. Drugs were predicted using 14 RNA-sequencing datasets from 72 autopsy tissues and 465 COVID-19 patient samples or from cultured human cells and organoids infected with SARS-CoV-2. Top drug predictions included statins, which were then assessed using electronic medical records containing over 4,000 COVID-19 patients on statins to determine mortality risk in patients prescribed specific statins versus untreated matched controls. The same drugs were tested in Vero E6 cells infected with SARS-CoV-2 and human endothelial cells infected with a related OC43 coronavirus. Simvastatin was among the most highly predicted compounds (14/14 datasets) and five other statins, including atorvastatin, were predicted to be active in > 50% of analyses. Analysis of the clinical database revealed that reduced mortality risk was only observed in COVID-19 patients prescribed a subset of statins, including simvastatin and atorvastatin. In vitro testing of SARS-CoV-2 infected cells revealed simvastatin to be a potent direct inhibitor whereas most other statins were less effective. Simvastatin also inhibited OC43 infection and reduced cytokine production in endothelial cells. Statins may differ in their ability to sustain the lives of COVID-19 patients despite having a shared drug target and lipid-modifying mechanism of action. These findings highlight the value of target-agnostic drug prediction coupled with patient databases to identify and clinically evaluate non-obvious mechanisms and derisk and accelerate drug repurposing opportunities.

Multiomic signals associated with maternal epidemiological factors contributing to preterm birth in low- and middle-income countries.

Preterm birth (PTB) is the leading cause of death in children under five, yet comprehensive studies are hindered by its multiple complex etiologies. Epidemiological associations between PTB and maternal characteristics have been previously described. This work used multiomic profiling and multivariate modeling to investigate the biological signatures of these characteristics. Maternal covariates were collected during pregnancy from 13,841 pregnant women across five sites. Plasma samples from 231 participants were analyzed to generate proteomic, metabolomic, and lipidomic datasets. Machine learning models showed robust performance for the prediction of PTB (AUROC = 0.70), time-to-delivery (r = 0.65), maternal age (r = 0.59), gravidity (r = 0.56), and BMI (r = 0.81). Time-to-delivery biological correlates included fetal-associated proteins (e.g., ALPP, AFP, and PGF) and immune proteins (e.g., PD-L1, CCL28, and LIFR). Maternal age negatively correlated with collagen COL9A1, gravidity with endothelial NOS and inflammatory chemokine CXCL13, and BMI with leptin and structural protein FABP4. These results provide an integrated view of epidemiological factors associated with PTB and identify biological signatures of clinical covariates affecting this disease.

Postpartum long-acting reversible contraception among privately insured: U.S. National analysis 2007-2016, by term and preterm birth.

OBJECTIVES: To investigate postpartum long-acting reversible contraception (LARC) use among privately insured women, with specific consideration of use after preterm delivery. STUDY DESIGN: We used the national IBM MarketScan Commercial Database to identify singleton deliveries from 2007 to 2016, spontaneous preterm birth, and follow-up ≤12 weeks postpartum. We assessed ≤12-week postpartum LARC placement overall and after spontaneous preterm deliveries, across study years. We examined timing of placement, rates of postpartum follow-up, and state-level variation in postpartum LARC. RESULTS: Among 3,132,107 singleton deliveries, 6.6% were spontaneous preterm. Over the time period, total postpartum LARC use increased 4.8% to 11.7% for intrauterine devices (IUDs), 0.2% to 2.4% for implants. In 2016, those who experienced a spontaneous preterm birth were less likely to initiate postpartum IUDs compared to their peers (10.2% vs 11.8%, p < 0.001), minimally more likely to initiate implants (2.7% vs 2.4%, p = 0.04) and more likely to present for postpartum care (61.7% vs 55.9%, p < 0.001). LARC placement prior to hospital discharge was rare (preterm: 8 per 10,000 deliveries vs all others: 6.3 per 10,000 deliveries, p = 0.002). State-level analysis showed wide variation in postpartum LARC (range 6%-32%). CONCLUSIONS: While postpartum LARC use increased among the privately insured 2007-2016, few received LARC prior to hospital discharge. Those experiencing preterm birth were no more likely to receive inpatient LARC. Postpartum follow-up remained low and regional variation of LARC was high, highlighting the need for efforts to remove barriers to inpatient postpartum LARC for all who desire it-public and privately insured alike. IMPLICATIONS: Among the half of U.S. births that are privately insured, postpartum LARC is increasing after both term and preterm births, yet exceedingly few (<0.1%) received LARC prior to hospital discharge.

Stabl: sparse and reliable biomarker discovery in predictive modeling of high-dimensional omic data.

High-content omic technologies coupled with sparsity-promoting regularization methods (SRM) have transformed the biomarker discovery process. However, the translation of computational results into a clinical use-case scenario remains challenging. A rate-limiting step is the rigorous selection of reliable biomarker candidates among a host of biological features included in multivariate models. We propose Stabl, a machine learning framework that unifies the biomarker discovery process with multivariate predictive modeling of clinical outcomes by selecting a sparse and reliable set of biomarkers. Evaluation of Stabl on synthetic datasets and four independent clinical studies demonstrates improved biomarker sparsity and reliability compared to commonly used SRMs at similar predictive performance. Stabl readily extends to double- and triple-omics integration tasks and identifies a sparser and more reliable set of biomarkers than those selected by state-of-the-art early- and late-fusion SRMs, thereby facilitating the biological interpretation and clinical translation of complex multi-omic predictive models. The complete package for Stabl is available online at https://github.com/gregbellan/Stabl.

Tailorable and Biocompatible Supramolecular-Based Hydrogels Featuring two Dynamic Covalent Chemistries.

Dynamic covalent chemistry (DCC) has proven to be a valuable tool in creating fascinating molecules, structures, and emergent properties in fully synthetic systems. Here we report a system that uses two dynamic covalent bonds in tandem, namely disulfides and hydrazones, for the formation of hydrogels containing biologically relevant ligands. The reversibility of disulfide bonds allows fiber formation upon oxidation of dithiol-peptide building block, while the reaction between NH-NH2 functionalized C-terminus and aldehyde cross-linkers results in a gel. The same bond-forming reaction was exploited for the "decoration" of the supramolecular assemblies by cell-adhesion-promoting sequences (RGD and LDV). Fast triggered gelation, cytocompatibility and ability to "on-demand" chemically customize fibrillar scaffold offer potential for applying these systems as a bioactive platform for cell culture and tissue engineering.

Data-driven longitudinal characterization of neonatal health and morbidity.

Although prematurity is the single largest cause of death in children under 5 years of age, the current definition of prematurity, based on gestational age, lacks the precision needed for guiding care decisions. Here, we propose a longitudinal risk assessment for adverse neonatal outcomes in newborns based on a deep learning model that uses electronic health records (EHRs) to predict a wide range of outcomes over a period starting shortly before conception and ending months after birth. By linking the EHRs of the Lucile Packard Children's Hospital and the Stanford Healthcare Adult Hospital, we developed a cohort of 22,104 mother-newborn dyads delivered between 2014 and 2018. Maternal and newborn EHRs were extracted and used to train a multi-input multitask deep learning model, featuring a long short-term memory neural network, to predict 24 different neonatal outcomes. An additional cohort of 10,250 mother-newborn dyads delivered at the same Stanford Hospitals from 2019 to September 2020 was used to validate the model. Areas under the receiver operating characteristic curve at delivery exceeded 0.9 for 10 of the 24 neonatal outcomes considered and were between 0.8 and 0.9 for 7 additional outcomes. Moreover, comprehensive association analysis identified multiple known associations between various maternal and neonatal features and specific neonatal outcomes. This study used linked EHRs from more than 30,000 mother-newborn dyads and would serve as a resource for the investigation and prediction of neonatal outcomes. An interactive website is available for independent investigators to leverage this unique dataset: https://maternal-child-health-associations.shinyapps.io/shiny_app/.