Heterogeneous data integration methods for patient similarity networks.

Patient similarity networks (PSNs), where patients are represented as nodes and their similarities as weighted edges, are being increasingly used in clinical research. These networks provide an insightful summary of the relationships among patients and can be exploited by inductive or transductive learning algorithms for the prediction of patient outcome, phenotype and disease risk. PSNs can also be easily visualized, thus offering a natural way to inspect complex heterogeneous patient data and providing some level of explainability of the predictions obtained by machine learning algorithms. The advent of high-throughput technologies, enabling us to acquire high-dimensional views of the same patients (e.g. omics data, laboratory data, imaging data), calls for the development of data fusion techniques for PSNs in order to leverage this rich heterogeneous information. In this article, we review existing methods for integrating multiple biomedical data views to construct PSNs, together with the different patient similarity measures that have been proposed. We also review methods that have appeared in the machine learning literature but have not yet been applied to PSNs, thus providing a resource to navigate the vast machine learning literature existing on this topic. In particular, we focus on methods that could be used to integrate very heterogeneous datasets, including multi-omics data as well as data derived from clinical information and medical imaging.

An expectation-maximization framework for comprehensive prediction of isoform-specific functions.

MOTIVATION: Advances in RNA sequencing technologies have achieved an unprecedented accuracy in the quantification of mRNA isoforms, but our knowledge of isoform-specific functions has lagged behind. There is a need to understand the functional consequences of differential splicing, which could be supported by the generation of accurate and comprehensive isoform-specific gene ontology annotations. RESULTS: We present isoform interpretation, a method that uses expectation-maximization to infer isoform-specific functions based on the relationship between sequence and functional isoform similarity. We predicted isoform-specific functional annotations for 85 617 isoforms of 17 900 protein-coding human genes spanning a range of 17 430 distinct gene ontology terms. Comparison with a gold-standard corpus of manually annotated human isoform functions showed that isoform interpretation significantly outperforms state-of-the-art competing methods. We provide experimental evidence that functionally related isoforms predicted by isoform interpretation show a higher degree of domain sharing and expression correlation than functionally related genes. We also show that isoform sequence similarity correlates better with inferred isoform function than with gene-level function. AVAILABILITY AND IMPLEMENTATION: Source code, documentation, and resource files are freely available under a GNU3 license at https://github.com/TheJacksonLaboratory/isopretEM and https://zenodo.org/record/7594321.

A method for comparing multiple imputation techniques: A case study on the U.S. national COVID cohort collaborative.

Healthcare datasets obtained from Electronic Health Records have proven to be extremely useful for assessing associations between patients' predictors and outcomes of interest. However, these datasets often suffer from missing values in a high proportion of cases, whose removal may introduce severe bias. Several multiple imputation algorithms have been proposed to attempt to recover the missing information under an assumed missingness mechanism. Each algorithm presents strengths and weaknesses, and there is currently no consensus on which multiple imputation algorithm works best in a given scenario. Furthermore, the selection of each algorithm's parameters and data-related modeling choices are also both crucial and challenging. In this paper we propose a novel framework to numerically evaluate strategies for handling missing data in the context of statistical analysis, with a particular focus on multiple imputation techniques. We demonstrate the feasibility of our approach on a large cohort of type-2 diabetes patients provided by the National COVID Cohort Collaborative (N3C) Enclave, where we explored the influence of various patient characteristics on outcomes related to COVID-19. Our analysis included classic multiple imputation techniques as well as simple complete-case Inverse Probability Weighted models. Extensive experiments show that our approach can effectively highlight the most promising and performant missing-data handling strategy for our case study. Moreover, our methodology allowed a better understanding of the behavior of the different models and of how it changed as we modified their parameters. Our method is general and can be applied to different research fields and on datasets containing heterogeneous types.

Boosting tissue-specific prediction of active cis-regulatory regions through deep learning and Bayesian optimization techniques.

BACKGROUND: Cis-regulatory regions (CRRs) are non-coding regions of the DNA that fine control the spatio-temporal pattern of transcription; they are involved in a wide range of pivotal processes such as the development of specific cell-lines/tissues and the dynamic cell response to physiological stimuli. Recent studies showed that genetic variants occurring in CRRs are strongly correlated with pathogenicity or deleteriousness. Considering the central role of CRRs in the regulation of physiological and pathological conditions, the correct identification of CRRs and of their tissue-specific activity status through Machine Learning methods plays a major role in dissecting the impact of genetic variants on human diseases. Unfortunately, the problem is still open, though some promising results have been already reported by (deep) machine-learning based methods that predict active promoters and enhancers in specific tissues or cell lines by encoding epigenetic or spectral features directly extracted from DNA sequences. RESULTS: We present the experiments we performed to compare two Deep Neural Networks, a Feed-Forward Neural Network model working on epigenomic features, and a Convolutional Neural Network model working only on genomic sequence, targeted to the identification of enhancer- and promoter-activity in specific cell lines. While performing experiments to understand how the experimental setup influences the prediction performance of the methods, we particularly focused on (1) automatic model selection performed by Bayesian optimization and (2) exploring different data rebalancing setups for reducing negative unbalancing effects. CONCLUSIONS: Results show that (1) automatic model selection by Bayesian optimization improves the quality of the learner; (2) data rebalancing considerably impacts the prediction performance of the models; test set rebalancing may provide over-optimistic results, and should therefore be cautiously applied; (3) despite working on sequence data, convolutional models obtain performance close to those of feed forward models working on epigenomic information, which suggests that also sequence data carries informative content for CRR-activity prediction. We therefore suggest combining both models/data types in future works.

HEMDAG: a family of modular and scalable hierarchical ensemble methods to improve Gene Ontology term prediction.

MOTIVATION: Automated protein function prediction is a complex multi-class, multi-label, structured classification problem in which protein functions are organized in a controlled vocabulary, according to the Gene Ontology (GO). 'Hierarchy-unaware' classifiers, also known as 'flat' methods, predict GO terms without exploiting the inherent structure of the ontology, potentially violating the True-Path-Rule (TPR) that governs the GO, while 'hierarchy-aware' approaches, even if they obey the TPR, do not always show clear improvements with respect to flat methods, or do not scale well when applied to the full GO. RESULTS: To overcome these limitations, we propose Hierarchical Ensemble Methods for Directed Acyclic Graphs (HEMDAG), a family of highly modular hierarchical ensembles of classifiers, able to build upon any flat method and to provide 'TPR-safe' predictions, by leveraging a combination of isotonic regression and TPR learning strategies. Extensive experiments on synthetic and real data across several organisms firstly show that HEMDAG can be used as a general tool to improve the predictions of flat classifiers, and secondly that HEMDAG is competitive versus state-of-the-art hierarchy-aware learning methods proposed in the last CAFA international challenges. AVAILABILITY AND IMPLEMENTATION: Fully tested R code freely available at https://anaconda.org/bioconda/r-hemdag. Tutorial and documentation at https://hemdag.readthedocs.io. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

NSAID use and clinical outcomes in COVID-19 patients: a 38-center retrospective cohort study.

BACKGROUND: Non-steroidal anti-inflammatory drugs (NSAIDs) are commonly used to reduce pain, fever, and inflammation but have been associated with complications in community-acquired pneumonia. Observations shortly after the start of the COVID-19 pandemic in 2020 suggested that ibuprofen was associated with an increased risk of adverse events in COVID-19 patients, but subsequent observational studies failed to demonstrate increased risk and in one case showed reduced risk associated with NSAID use. METHODS: A 38-center retrospective cohort study was performed that leveraged the harmonized, high-granularity electronic health record data of the National COVID Cohort Collaborative. A propensity-matched cohort of 19,746 COVID-19 inpatients was constructed by matching cases (treated with NSAIDs at the time of admission) and 19,746 controls (not treated) from 857,061 patients with COVID-19 available for analysis. The primary outcome of interest was COVID-19 severity in hospitalized patients, which was classified as: moderate, severe, or mortality/hospice. Secondary outcomes were acute kidney injury (AKI), extracorporeal membrane oxygenation (ECMO), invasive ventilation, and all-cause mortality at any time following COVID-19 diagnosis. RESULTS: Logistic regression showed that NSAID use was not associated with increased COVID-19 severity (OR: 0.57 95% CI: 0.53-0.61). Analysis of secondary outcomes using logistic regression showed that NSAID use was not associated with increased risk of all-cause mortality (OR 0.51 95% CI: 0.47-0.56), invasive ventilation (OR: 0.59 95% CI: 0.55-0.64), AKI (OR: 0.67 95% CI: 0.63-0.72), or ECMO (OR: 0.51 95% CI: 0.36-0.7). In contrast, the odds ratios indicate reduced risk of these outcomes, but our quantitative bias analysis showed E-values of between 1.9 and 3.3 for these associations, indicating that comparatively weak or moderate confounder associations could explain away the observed associations. CONCLUSIONS: Study interpretation is limited by the observational design. Recording of NSAID use may have been incomplete. Our study demonstrates that NSAID use is not associated with increased COVID-19 severity, all-cause mortality, invasive ventilation, AKI, or ECMO in COVID-19 inpatients. A conservative interpretation in light of the quantitative bias analysis is that there is no evidence that NSAID use is associated with risk of increased severity or the other measured outcomes. Our results confirm and extend analogous findings in previous observational studies using a large cohort of patients drawn from 38 centers in a nationally representative multicenter database.

An approach to evaluate the quality of radiological reports in Head and Neck cancer loco-regional staging: experience of two Academic Hospitals.

OBJECTIVES: To evaluate the quality of the reports of loco-regional staging computed tomography (CT) or magnetic resonance imaging (MRI) in head and neck (H&N) cancer. METHODS: Consecutive reports of staging CT and MRI of all H&N cancer cases from 2018 to 2020 were collected. We created lists of quality indicators for tumor (T) for each district and for node (N). We marked these as 0 or 1 in the report calculating a report score (RS) and a maximum sum (MS) of each list. Two radiologists and two otolaryngologists in consensus classified reports as low quality (LQ) if the RS fell in the percentage range 0-59% of MS and as high quality (HQ) if it fell in the range 60-100%, annotating technique and district. We evaluated the distribution of reports in these categories. RESULTS: Two hundred thirty-seven reports (97 CT and 140 MRI) of 95 oral cavity, 52 laryngeal, 47 oropharyngeal, 19 hypo-pharyngeal, 14 parotid, and 10 nasopharyngeal cancers were included. Sixty-six percent of all the reports were LQ for T, 66% out of all the MRI reports, and 65% out of all CT reports were LQ. Eight-five percent of reports were HQ for N, 85% out of all the MRI reports, and 82% out of all CT reports were HQ. Reports of oral cavity, oro-nasopharynx, and parotid were LQ, respectively, in 76%, 73%, 100% and 92 out of cases. CONCLUSION: Reports of staging CT/MRI in H&N cancer were LQ for T description and HQ for N description.

Pseudo-pneumatosis of the gastrointestinal tract: its incidence and the accuracy of a checklist supported by artificial intelligence (AI) techniques to reduce the misinterpretation of pneumatosis.

PURPOSE: To assess the incidence of erroneous diagnosis of pneumatosis (pseudo-pneumatosis) in patients who underwent an emergency abdominal CT and to verify the performance of imaging features, supported by artificial intelligence (AI) techniques, to reduce this misinterpretation. METHODS: We selected 71 radiological reports where the presence of pneumatosis was considered definitive or suspected. Surgical findings, clinical outcomes, and reevaluation of the CT scans were used to assess the correct diagnosis of pneumatosis. We identified four imaging signs from literature, to differentiate pneumatosis from pseudo-pneumatosis: gas location, dissecting gas in the bowel wall, a circumferential gas pattern, and intramural gas beyond a gas-fluid/faecal level. Two radiologists reevaluated in consensus all the CT scans, assessing the four above-mentioned variables. Variable discriminative importance was assessed using the Fisher exact test. Accurate and statistically significant variables (p-value < 0.05, accuracy > 75%) were pooled using boosted Random Forests (RFs) executed using a Leave-One-Out cross-validation (LOO cv) strategy to obtain unbiased estimates of individual variable importance by permutation analysis. After the LOO cv, the comparison of the variable importance distribution was validated by one-sided Wilcoxon test. RESULTS: Twenty-seven patients proved to have pseudo-pneumatosis (error: 38%). The most significant features to diagnose pneumatosis were presence of dissecting gas in the bowel wall (accuracy: 94%), presence of intramural gas beyond a gas-fluid/faecal level (accuracy: 86%), and a circumferential gas pattern (accuracy: 78%). CONCLUSION: The incidence of pseudo-pneumatosis can be high. The use of a checklist which includes three imaging signs can be useful to reduce this overestimation.

Variations in volume of emergency surgeries and emergency department access at a third level hospital in Milan, Lombardy, during the COVID-19 outbreak.

BACKGROUND: During the recent outbreak of COVID-19 (coronavirus disease 2019), Lombardy was the most affected region in Italy, with 87,000 patients and 15,876 deaths up to May 26, 2020. Since February 22, 2020, well before the Government declared a state of emergency, there was a huge reduction in the number of emergency surgeries performed at hospitals in Lombardy. A general decrease in attendance at emergency departments (EDs) was also observed. The aim of our study is to report the experience of the ED of a third-level hospital in downtown Milan, Lombardy, and provide possible explanations for the observed phenomena. METHODS: This retrospective, observational study assessed the volume of emergency surgeries and attendance at an ED during the course of the pandemic, i.e. immediately before, during and after a progressive community lockdown in response to the COVID-19 pandemic. These data were compared with data from the same time periods in 2019. The results are presented as means, standard error (SE), and 95% studentized confidence intervals (CI). The Wilcoxon rank signed test at a 0.05 significance level was used to assess differences in per-day ED access distributions. RESULTS: Compared to 2019, a significant overall drop in emergency surgeries (60%, p < 0.002) and in ED admittance (66%, p ≅ 0) was observed in 2020. In particular, there were significant decreases in medical (40%), surgical (74%), specialist (ophthalmology, otolaryngology, traumatology, and urology) (92%), and psychiatric (60%) cases. ED admittance due to domestic violence (59%) and individuals who left the ED without being seen (76%) also decreased. Conversely, the number of deaths increased by 196%. CONCLUSIONS: During the COVID-19 outbreak the volume of urgent surgeries and patients accessing our ED dropped. Currently, it is not known if mortality of people who did not seek care increased during the pandemic. Further studies are needed to understand if such reductions during the COVID-19 pandemic will result in a rebound of patients left untreated or in unwanted consequences for population health.

Characterization of liver nodules in patients with chronic liver disease by MRI: performance of the Liver Imaging Reporting and Data System (LI-RADS v.2018) scale and its comparison with the Likert scale.

OBJECTIVES: To evaluate the performance of the LI-RADS v.2018 scale by comparing it with the Likert scale, in the characterization of liver lesions. METHODS: A total of 39 patients with chronic liver disease underwent MR examination for characterization of 44 liver lesions. Images were independently analyzed by two radiologists using the LI-RADS scale and by another two radiologists using the Likert scale. The reference standard used was either histopathological evaluation or a 4-year MRI follow-up. Receiver operating characteristic analysis was performed. RESULTS: The LI-RADS scale obtained an accuracy of 80%, a sensitivity of 72%, a specificity of 93%, a positive predictive value (PPV) of 93% and a negative predictive value (NPV) of 70%, while the Likert scale achieved an accuracy of 79%, a sensitivity of 73%, a specificity of 87%, a PPV of 89% and a NPV of 70%. The area under the curve (AUC) was 85% for the LI-RADS scale and 83% for the Likert scale. The inter-observer agreement was strong (k = 0.89) between the LI-RADS evaluators and moderate (k = 0.69) between the Likert evaluators. CONCLUSIONS: There was no statistically significant difference between the performances of the two scales; nevertheless, we suggest that the LI-RADS scale be used, as it appeared more objective and consistent.

Chest CT in patients with a moderate or high pretest probability of COVID-19 and negative swab.

OBJECTIVES: We aimed to assess the diagnostic performance of CT in patients with a negative first RT-PCR testing and to identify typical features of COVID-19 pneumonia that can guide diagnosis in this case. METHODS: Patients suspected of COVID-19 with a negative first RT-PCR testing were retrospectively revalued after undergoing CT. CT was reviewed by two radiologists and classified as suspected COVID-19 pneumonia, non-COVID-19 pneumonia or negative. The performance of both first RT-PCR result and CT was evaluated by using sensitivity (SE), specificity (SP), positive predictive value (PPV), negative predictive value (NPV) and area under the curve (AUC) and by using the second RT-PCR test as the reference standard. CT findings for confirmed COVID-19 positive or negative were compared by using the Pearson chi-squared test (P values < 0.05) RESULTS: Totally, 337 patients suspected of COVID-19 underwent CT and nasopharyngeal swabs in March 2020. Eighty-seven out of 337 patients had a negative first RT-PCR result; of these, 68 repeated RT-PCR testing and were included in the study. The first RT-PCR test showed SE 0, SP = 100%, PPV = NaN, NPV = 70%, AUC = 50%, and CT showed SE = 70% SP = 79%, PPV = 86%, NPV = 76%, AUC = 75%. The most relevant CT variables were ground glass opacity more than 50% and peripheral and/or perihilar distribution. DISCUSSION: Negative RT-PCR test but positive CT features should be highly suggestive of COVID-19 in a cluster or community transmission scenarios, and the second RT-PCR test should be promptly requested to confirm the final diagnosis.

ki67 nuclei detection and ki67-index estimation: a novel automatic approach based on human vision modeling.

BACKGROUND: The protein ki67 (pki67) is a marker of tumor aggressiveness, and its expression has been proven to be useful in the prognostic and predictive evaluation of several types of tumors. To numerically quantify the pki67 presence in cancerous tissue areas, pathologists generally analyze histochemical images to count the number of tumor nuclei marked for pki67. This allows estimating the ki67-index, that is the percentage of tumor nuclei positive for pki67 over all the tumor nuclei. Given the high image resolution and dimensions, its estimation by expert clinicians is particularly laborious and time consuming. Though automatic cell counting techniques have been presented so far, the problem is still open. RESULTS: In this paper we present a novel automatic approach for the estimations of the ki67-index. The method starts by exploiting the STRESS algorithm to produce a color enhanced image where all pixels belonging to nuclei are easily identified by thresholding, and then separated into positive (i.e. pixels belonging to nuclei marked for pki67) and negative by a binary classification tree. Next, positive and negative nuclei pixels are processed separately by two multiscale procedures identifying isolated nuclei and separating adjoining nuclei. The multiscale procedures exploit two Bayesian classification trees to recognize positive and negative nuclei-shaped regions. CONCLUSIONS: The evaluation of the computed results, both through experts' visual assessments and through the comparison of the computed indexes with those of experts, proved that the prototype is promising, so that experts believe in its potential as a tool to be exploited in the clinical practice as a valid aid for clinicians estimating the ki67-index. The MATLAB source code is open source for research purposes.

UNIPred-Web: a web tool for the integration and visualization of biomolecular networks for protein function prediction.

BACKGROUND: One of the main issues in the automated protein function prediction (AFP) problem is the integration of multiple networked data sources. The UNIPred algorithm was thereby proposed to efficiently integrate -in a function-specific fashion- the protein networks by taking into account the imbalance that characterizes protein annotations, and to subsequently predict novel hypotheses about unannotated proteins. UNIPred is publicly available as R code, which might result of limited usage for non-expert users. Moreover, its application requires efforts in the acquisition and preparation of the networks to be integrated. Finally, the UNIPred source code does not handle the visualization of the resulting consensus network, whereas suitable views of the network topology are necessary to explore and interpret existing protein relationships. RESULTS: We address the aforementioned issues by proposing UNIPred-Web, a user-friendly Web tool for the application of the UNIPred algorithm to a variety of biomolecular networks, already supplied by the system, and for the visualization and exploration of protein networks. We support different organisms and different types of networks -e.g., co-expression, shared domains and physical interaction networks. Users are supported in the different phases of the process, ranging from the selection of the networks and the protein function to be predicted, to the navigation of the integrated network. The system also supports the upload of user-defined protein networks. The vertex-centric and the highly interactive approach of UNIPred-Web allow a narrow exploration of specific proteins, and an interactive analysis of large sub-networks with only a few mouse clicks. CONCLUSIONS: UNIPred-Web offers a practical and intuitive (visual) guidance to biologists interested in gaining insights into protein biomolecular functions. UNIPred-Web provides facilities for the integration of networks, and supplies a framework for the imbalance-aware protein network integration of nine organisms, the prediction of thousands of GO protein functions, and a easy-to-use graphical interface for the visual analysis, navigation and interpretation of the integrated networks and of the functional predictions.

A novel computational method for automatic segmentation, quantification and comparative analysis of immunohistochemically labeled tissue sections.

BACKGROUND: In the clinical practice, the objective quantification of histological results is essential not only to define objective and well-established protocols for diagnosis, treatment, and assessment, but also to ameliorate disease comprehension. SOFTWARE: The software MIAQuant_Learn presented in this work segments, quantifies and analyzes markers in histochemical and immunohistochemical images obtained by different biological procedures and imaging tools. MIAQuant_Learn employs supervised learning techniques to customize the marker segmentation process with respect to any marker color appearance. Our software expresses the location of the segmented markers with respect to regions of interest by mean-distance histograms, which are numerically compared by measuring their intersection. When contiguous tissue sections stained by different markers are available, MIAQuant_Learn aligns them and overlaps the segmented markers in a unique image enabling a visual comparative analysis of the spatial distribution of each marker (markers' relative location). Additionally, it computes novel measures of markers' co-existence in tissue volumes depending on their density. CONCLUSIONS: Applications of MIAQuant_Learn in clinical research studies have proven its effectiveness as a fast and efficient tool for the automatic extraction, quantification and analysis of histological sections. It is robust with respect to several deficits caused by image acquisition systems and produces objective and reproducible results. Thanks to its flexibility, MIAQuant_Learn represents an important tool to be exploited in basic research where needs are constantly changing.

Epidemiological profile of non-traumatic emergencies of the neck in CT imaging: our experience.

PURPOSE: This study was undertaken to collect information on the incidence and distribution of acute, non-traumatic conditions of the neck at our emergency radiology department and to review the literature about this topic. MATERIALS AND METHODS: We retrospectively reviewed 143 consecutive patients who underwent neck computed tomography (CT) for non-traumatic emergencies between 1 December 2008 and 31 December 2012. For each of the conditions identified, we defined the overall incidence, the incidence based on the site, gender, average age and age range. RESULTS: Computed tomography examination was positive in 125 out of 143 patients (87.4%), 74 men and 51 women, with an average age of 51.1 years, aged between 10 and 90 years. We found 79 inflammatory/infectious conditions (63.2% of positive cases, 55.2% of total cases), 46 men and 33 women, with an average age of 47 years. Computed tomography revealed 26 newly found tumours (20.8/18.2%), 19 men and 7 women, with an average age of 68.5 years, aged between 49 and 97 years. In 20 cases, 9 men and 11 women, with an average age of 57.3 years, aged between 21 and 90 years, we diagnosed other acute conditions: six cases of foreign body ingestion (4.8/4.2%), five benign swellings (4/3.5%), five cases of vascular disorders (4/3.5%), and four cases of oedema of the larynx (3.2/2.8 %). CONCLUSIONS: Our study of emergency CT of non-traumatic conditions of the neck fundamentally revealed infectious/inflammatory diseases and newly found neoplasms.

On the limitations of large language models in clinical diagnosis.

Objective: Large Language Models such as GPT-4 previously have been applied to differential diagnostic challenges based on published case reports. Published case reports have a sophisticated narrative style that is not readily available from typical electronic health records (EHR). Furthermore, even if such a narrative were available in EHRs, privacy requirements would preclude sending it outside the hospital firewall. We therefore tested a method for parsing clinical texts to extract ontology terms and programmatically generating prompts that by design are free of protected health information. Materials and Methods: We investigated different methods to prepare prompts from 75 recently published case reports. We transformed the original narratives by extracting structured terms representing phenotypic abnormalities, comorbidities, treatments, and laboratory tests and creating prompts programmatically. Results: Performance of all of these approaches was modest, with the correct diagnosis ranked first in only 5.3-17.6% of cases. The performance of the prompts created from structured data was substantially worse than that of the original narrative texts, even if additional information was added following manual review of term extraction. Moreover, different versions of GPT-4 demonstrated substantially different performance on this task. Discussion: The sensitivity of the performance to the form of the prompt and the instability of results over two GPT-4 versions represent important current limitations to the use of GPT-4 to support diagnosis in real-life clinical settings. Conclusion: Research is needed to identify the best methods for creating prompts from typically available clinical data to support differential diagnostics.

Predicting nutrition and environmental factors associated with female reproductive disorders using a knowledge graph and random forests.

OBJECTIVE: Female reproductive disorders (FRDs) are common health conditions that may present with significant symptoms. Diet and environment are potential areas for FRD interventions. We utilized a knowledge graph (KG) method to predict factors associated with common FRDs (for example, endometriosis, ovarian cyst, and uterine fibroids). MATERIALS AND METHODS: We harmonized survey data from the Personalized Environment and Genes Study (PEGS) on internal and external environmental exposures and health conditions with biomedical ontology content. We merged the harmonized data and ontologies with supplemental nutrient and agricultural chemical data to create a KG. We analyzed the KG by embedding edges and applying a random forest for edge prediction to identify variables potentially associated with FRDs. We also conducted logistic regression analysis for comparison. RESULTS: Across 9765 PEGS respondents, the KG analysis resulted in 8535 significant or suggestive predicted links between FRDs and chemicals, phenotypes, and diseases. Amongst these links, 32 were exact matches when compared with the logistic regression results, including comorbidities, medications, foods, and occupational exposures. DISCUSSION: Mechanistic underpinnings of predicted links documented in the literature may support some of our findings. Our KG methods are useful for predicting possible associations in large, survey-based datasets with added information on directionality and magnitude of effect from logistic regression. These results should not be construed as causal but can support hypothesis generation. CONCLUSION: This investigation enabled the generation of hypotheses on a variety of potential links between FRDs and exposures. Future investigations should prospectively evaluate the variables hypothesized to impact FRDs.

Node-degree aware edge sampling mitigates inflated classification performance in biomedical random walk-based graph representation learning.

Motivation: Graph representation learning is a family of related approaches that learn low-dimensional vector representations of nodes and other graph elements called embeddings. Embeddings approximate characteristics of the graph and can be used for a variety of machine-learning tasks such as novel edge prediction. For many biomedical applications, partial knowledge exists about positive edges that represent relationships between pairs of entities, but little to no knowledge is available about negative edges that represent the explicit lack of a relationship between two nodes. For this reason, classification procedures are forced to assume that the vast majority of unlabeled edges are negative. Existing approaches to sampling negative edges for training and evaluating classifiers do so by uniformly sampling pairs of nodes. Results: We show here that this sampling strategy typically leads to sets of positive and negative examples with imbalanced node degree distributions. Using representative heterogeneous biomedical knowledge graph and random walk-based graph machine learning, we show that this strategy substantially impacts classification performance. If users of graph machine-learning models apply the models to prioritize examples that are drawn from approximately the same distribution as the positive examples are, then performance of models as estimated in the validation phase may be artificially inflated. We present a degree-aware node sampling approach that mitigates this effect and is simple to implement. Availability and implementation: Our code and data are publicly available at https://github.com/monarch-initiative/negativeExampleSelection.

Association of post-COVID phenotypic manifestations with new-onset psychiatric disease.

Acute COVID-19 infection can be followed by diverse clinical manifestations referred to as Post Acute Sequelae of SARS-CoV2 Infection (PASC). Studies have shown an increased risk of being diagnosed with new-onset psychiatric disease following a diagnosis of acute COVID-19. However, it was unclear whether non-psychiatric PASC-associated manifestations (PASC-AMs) are associated with an increased risk of new-onset psychiatric disease following COVID-19. A retrospective electronic health record (EHR) cohort study of 2,391,006 individuals with acute COVID-19 was performed to evaluate whether non-psychiatric PASC-AMs are associated with new-onset psychiatric disease. Data were obtained from the National COVID Cohort Collaborative (N3C), which has EHR data from 76 clinical organizations. EHR codes were mapped to 151 non-psychiatric PASC-AMs recorded 28-120 days following SARS-CoV-2 diagnosis and before diagnosis of new-onset psychiatric disease. Association of newly diagnosed psychiatric disease with age, sex, race, pre-existing comorbidities, and PASC-AMs in seven categories was assessed by logistic regression. There were significant associations between a diagnosis of any psychiatric disease and five categories of PASC-AMs with odds ratios highest for neurological, cardiovascular, and constitutional PASC-AMs with odds ratios of 1.31, 1.29, and 1.23 respectively. Secondary analysis revealed that the proportions of 50 individual clinical features significantly differed between patients diagnosed with different psychiatric diseases. Our study provides evidence for association between non-psychiatric PASC-AMs and the incidence of newly diagnosed psychiatric disease. Significant associations were found for features related to multiple organ systems. This information could prove useful in understanding risk stratification for new-onset psychiatric disease following COVID-19. Prospective studies are needed to corroborate these findings.

The Use of Artificial Intelligence in Head and Neck Cancers: A Multidisciplinary Survey.

Artificial intelligence (AI) approaches have been introduced in various disciplines but remain rather unused in head and neck (H&N) cancers. This survey aimed to infer the current applications of and attitudes toward AI in the multidisciplinary care of H&N cancers. From November 2020 to June 2022, a web-based questionnaire examining the relationship between AI usage and professionals' demographics and attitudes was delivered to different professionals involved in H&N cancers through social media and mailing lists. A total of 139 professionals completed the questionnaire. Only 49.7% of the respondents reported having experience with AI. The most frequent AI users were radiologists (66.2%). Significant predictors of AI use were primary specialty (V = 0.455; p < 0.001), academic qualification and age. AI's potential was seen in the improvement of diagnostic accuracy (72%), surgical planning (64.7%), treatment selection (57.6%), risk assessment (50.4%) and the prediction of complications (45.3%). Among participants, 42.7% had significant concerns over AI use, with the most frequent being the 'loss of control' (27.6%) and 'diagnostic errors' (57.0%). This survey reveals limited engagement with AI in multidisciplinary H&N cancer care, highlighting the need for broader implementation and further studies to explore its acceptance and benefits.