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BackgroundPrecision oncology has the potential to leverage clinical and genomic data in advancing disease prevention, diagnosis, and treatment. A key research area focuses on the early detection of primary cancers and potential prediction of cancers of unknown primary in order to facilitate optimal treatment decisions. ObjectiveThis study presents a methodology to harmonize phenotypic and genetic data features to classify primary cancer types and predict cancers of unknown primaries. MethodsWe extracted genetic data elements from oncology genetic reports of 1011 patients with cancer and their corresponding phenotypical data from Mayo Clinic’s electronic health records. We modeled both genetic and electronic health record data with HL7 Fast Healthcare Interoperability Resources. The semantic web Resource Description Framework was employed to generate the network-based data representation (ie, patient-phenotypic-genetic network). Based on the Resource Description Framework data graph, Node2vec graph-embedding algorithm was applied to generate features. Multiple machine learning and deep learning backbone models were compared for cancer prediction performance. ResultsWith 6 machine learning tasks designed in the experiment, we demonstrated the proposed method achieved favorable results in classifying primary cancer types (area under the receiver operating characteristic curve [AUROC] 96.56% for all 9 cancer predictions on average based on the cross-validation) and predicting unknown primaries (AUROC 80.77% for all 8 cancer predictions on average for real-patient validation). To demonstrate the interpretability, 17 phenotypic and genetic features that contributed the most to the prediction of each cancer were identified and validated based on a literature review. ConclusionsAccurate prediction of cancer types can be achieved with existing electronic health record data with satisfactory precision. The integration of genetic reports improves prediction, illustrating the translational values of incorporating genetic tests early at the diagnosis stage for patients with cancer.

BackgroundImmune checkpoint inhibitors are associated with unique immune-related adverse events (irAEs). As most of the immune checkpoint inhibitors are new to the market, it is important to conduct studies using real-world data sources to investigate their safety profiles. ObjectiveThe aim of the study was to develop a framework for signal detection and filtration of novel irAEs for 6 Food and Drug Administration–approved immune checkpoint inhibitors. MethodsIn our framework, we first used the Food and Drug Administration’s Adverse Event Reporting System (FAERS) standardized in an Observational Health Data Sciences and Informatics (OHDSI) common data model (CDM) to collect immune checkpoint inhibitor-related event data and conducted irAE signal detection. OHDSI CDM is a standard-driven data model that focuses on transforming different databases into a common format and standardizing medical terms to a common representation. We then filtered those already known irAEs from drug labels and literature by using a customized text-mining pipeline based on clinical text analysis and knowledge extraction system with Medical Dictionary for Regulatory Activities (MedDRA) as a dictionary. Finally, we classified the irAE detection results into three different categories to discover potentially new irAE signals. ResultsBy our text-mining pipeline, 490 irAE terms were identified from drug labels, and 918 terms were identified from the literature. In addition, of the 94 positive signals detected using CDM-based FAERS, 53 signals (56%) were labeled signals, 10 (11%) were unlabeled published signals, and 31 (33%) were potentially new signals. ConclusionsWe demonstrated that our approach is effective for irAE signal detection and filtration. Moreover, our CDM-based framework could facilitate adverse drug events detection and filtration toward the goal of next-generation pharmacovigilance that seamlessly integrates electronic health record data for improved signal detection.

Internal validation is the most popular evaluation strategy used for drug-target predictive models. The simple random shuffling in the cross-validation, however, is not always ideal to handle large, diverse and copious datasets as it could potentially introduce bias. Hence, these predictive models cannot be comprehensively evaluated to provide insight into their general performance on a variety of use-cases (e.g. permutations of different levels of connectiveness and categories in drug and target space, as well as validations based on different data sources). In this work, we introduce a benchmark, BETA, that aims to address this gap by (i) providing an extensive multipartite network consisting of 0.97 million biomedical concepts and 8.5 million associations, in addition to 62 million drug-drug and protein-protein similarities and (ii) presenting evaluation strategies that reflect seven cases (i.e. general, screening with different connectivity, target and drug screening based on categories, searching for specific drugs and targets and drug repurposing for specific diseases), a total of seven Tests (consisting of 344 Tasks in total) across multiple sampling and validation strategies. Six state-of-the-art methods covering two broad input data types (chemical structure- and gene sequence-based and network-based) were tested across all the developed Tasks. The best-worst performing cases have been analyzed to demonstrate the ability of the proposed benchmark to identify limitations of the tested methods for running over the benchmark tasks. The results highlight BETA as a benchmark in the selection of computational strategies for drug repurposing and target discovery.

ObjectiveFinding relevant datasets is important for promoting data reuse in the biomedical domain, but it is challenging given the volume and complexity of biomedical data. Here we describe the development of an open source biomedical data discovery system called DataMed, with the goal of promoting the building of additional data indexes in the biomedical domain.Materials and methodsDataMed, which can efficiently index and search diverse types of biomedical datasets across repositories, is developed through the National Institutes of Health-funded biomedical and healthCAre Data Discovery Index Ecosystem (bioCADDIE) consortium. It consists of 2 main components: (1) a data ingestion pipeline that collects and transforms original metadata information to a unified metadata model, called DatA Tag Suite (DATS), and (2) a search engine that finds relevant datasets based on user-entered queries. In addition to describing its architecture and techniques, we evaluated individual components within DataMed, including the accuracy of the ingestion pipeline, the prevalence of the DATS model across repositories, and the overall performance of the dataset retrieval engine.Results and conclusionOur manual review shows that the ingestion pipeline could achieve an accuracy of 90% and core elements of DATS had varied frequency across repositories. On a manually curated benchmark dataset, the DataMed search engine achieved an inferred average precision of 0.2033 and a precision at 10 (P@10, the number of relevant results in the top 10 search results) of 0.6022, by implementing advanced natural language processing and terminology services. Currently, we have made the DataMed system publically available as an open source package for the biomedical community.

MotivationA heterogeneous network topology possessing abundant interactions between biomedical entities has yet to be utilized in similarity-based methods for predicting drug-target associations based on the array of varying features of drugs and their targets. Deep learning reveals features of vertices of a large network that can be adapted in accommodating the similarity-based solutions to provide a flexible method of drug-target prediction.ResultsWe propose a similarity-based drug-target prediction method that enhances existing association discovery methods by using a topology-based similarity measure. DeepWalk, a deep learning method, is adopted in this study to calculate the similarities within Linked Tripartite Network (LTN), a heterogeneous network generated from biomedical linked datasets. This proposed method shows promising results for drug-target association prediction: 98.96% AUC ROC score with a 10-fold cross-validation and 99.25% AUC ROC score with a Monte Carlo cross-validation with LTN. By utilizing DeepWalk, we demonstrate that: (i) this method outperforms other existing topology-based similarity computation methods, (ii) the performance is better for tripartite than with bipartite networks and (iii) the measure of similarity using network topology outperforms the ones derived from chemical structure (drugs) or genomic sequence (targets). Our proposed methodology proves to be capable of providing a promising solution for drug-target prediction based on topological similarity with a heterogeneous network, and may be readily re-purposed and adapted in the existing of similarity-based methodologies.Availability and implementationThe proposed method has been developed in JAVA and it is available, along with the data at the following URL: https://github.com/zongnansu1982/drug-target-prediction .Contactnazong@ucsd.edu.Supplementary informationSupplementary data are available at Bioinformatics online.

Today's science increasingly requires effective ways to find and access existing datasets that are distributed across a range of repositories. For researchers in the life sciences, discoverability of datasets may soon become as essential as identifying the latest publications via PubMed. Through an international collaborative effort funded by the National Institutes of Health (NIH)'s Big Data to Knowledge (BD2K) initiative, we have designed and implemented the DAta Tag Suite (DATS) model to support the DataMed data discovery index. DataMed's goal is to be for data what PubMed has been for the scientific literature. Akin to the Journal Article Tag Suite (JATS) used in PubMed, the DATS model enables submission of metadata on datasets to DataMed. DATS has a core set of elements, which are generic and applicable to any type of dataset, and an extended set that can accommodate more specialized data types. DATS is a platform-independent model also available as an annotated serialization in schema.org, which in turn is widely used by major search engines like Google, Microsoft, Yahoo and Yandex.

The value of broadening searches for data across multiple repositories has been identified by the biomedical research community. As part of the US National Institutes of Health (NIH) Big Data to Knowledge initiative, we work with an international community of researchers, service providers and knowledge experts to develop and test a data index and search engine, which are based on metadata extracted from various data sets in a range of repositories. DataMed is designed to be, for data, what PubMed has been for the scientific literature. DataMed supports the findability and accessibility of data sets. These characteristics—along with interoperability and reusability—compose the four FAIR principles to facilitate knowledge discovery in today’s big data–intensive science landscape.

Objectives Heart failure (HF) impacts millions of patients worldwide, yet the variability in treatment responses remains a major challenge for healthcare professionals. The current treatment strategies, largely derived from population based evidence, often fail to consider the unique characteristics of individual patients, resulting in suboptimal outcomes. This study aims to develop computational models that are patient-specific in predicting treatment outcomes, by utilizing a large Electronic Health Records (EHR) database. The goal is to improve drug response predictions by identifying specific HF patient subgroups that are likely to benefit from existing HF medications. Materials and Methods A novel, graph-based model capable of predicting treatment responses, combining Graph Neural Network and Transformer was developed. This method differs from conventional approaches by transforming a patient's EHR data into a graph structure. By defining patient subgroups based on this representation via K-Means Clustering, we were able to enhance the performance of drug response predictions. Results Leveraging EHR data from 11 627 Mayo Clinic HF patients, our model significantly outperformed traditional models in predicting drug response using NT-proBNP as a HF biomarker across five medication categories (best RMSE of 0.0043). Four distinct patient subgroups were identified with differential characteristics and outcomes, demonstrating superior predictive capabilities over existing HF subtypes (best mean RMSE of 0.0032). Discussion These results highlight the power of graph-based modeling of EHR in improving HF treatment strategies. The stratification of patients sheds light on particular patient segments that could benefit more significantly from tailored response predictions. Conclusions Longitudinal EHR data have the potential to enhance personalized prognostic predictions through the application of graph-based AI techniques. [ABSTRACT FROM AUTHOR]

Background Error analysis plays a crucial role in clinical concept extraction, a fundamental subtask within clinical natural language processing (NLP). The process typically involves a manual review of error types, such as contextual and linguistic factors contributing to their occurrence, and the identification of underlying causes to refine the NLP model and improve its performance. Conducting error analysis can be complex, requiring a combination of NLP expertise and domain-specific knowledge. Due to the high heterogeneity of electronic health record (EHR) settings across different institutions, challenges may arise when attempting to standardize and reproduce the error analysis process. Objectives This study aims to facilitate a collaborative effort to establish common definitions and taxonomies for capturing diverse error types, fostering community consensus on error analysis for clinical concept extraction tasks. Materials and Methods We iteratively developed and evaluated an error taxonomy based on existing literature, standards, real-world data, multisite case evaluations, and community feedback. The finalized taxonomy was released in both.dtd and.owl formats at the Open Health Natural Language Processing Consortium. The taxonomy is compatible with several different open-source annotation tools, including MAE, Brat, and MedTator. Results The resulting error taxonomy comprises 43 distinct error classes, organized into 6 error dimensions and 4 properties, including model type (symbolic and statistical machine learning), evaluation subject (model and human), evaluation level (patient, document, sentence, and concept), and annotation examples. Internal and external evaluations revealed strong variations in error types across methodological approaches, tasks, and EHR settings. Key points emerged from community feedback, including the need to enhancing clarity, generalizability, and usability of the taxonomy, along with dissemination strategies. Conclusion The proposed taxonomy can facilitate the acceleration and standardization of the error analysis process in multi-site settings, thus improving the provenance, interpretability, and portability of NLP models. Future researchers could explore the potential direction of developing automated or semi-automated methods to assist in the classification and standardization of error analysis. [ABSTRACT FROM AUTHOR]

Background Alzheimer's disease (AD) is a progressive neurological disorder with no specific curative medications. Sophisticated clinical skills are crucial to optimize treatment regimens given the multiple coexisting comorbidities in the patient population. Objective Here, we propose a study to leverage reinforcement learning (RL) to learn the clinicians' decisions for AD patients based on the longitude data from electronic health records. Methods In this study, we selected 1736 patients from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. We focused on the two most frequent concomitant diseases—depression, and hypertension, thus creating 5 data cohorts (ie, Whole Data, AD, AD-Hypertension, AD-Depression, and AD-Depression-Hypertension). We modeled the treatment learning into an RL problem by defining states, actions, and rewards. We built a regression model and decision tree to generate multiple states, used six combinations of medications (ie, cholinesterase inhibitors, memantine, memantine-cholinesterase inhibitors, hypertension drugs, supplements, or no drugs) as actions, and Mini-Mental State Exam (MMSE) scores as rewards. Results Given the proper dataset, the RL model can generate an optimal policy (regimen plan) that outperforms the clinician's treatment regimen. Optimal policies (ie, policy iteration and Q-learning) had lower rewards than the clinician's policy (mean −3.03 and −2.93 vs. −2.93, respectively) for smaller datasets but had higher rewards for larger datasets (mean −4.68 and −2.82 vs. −4.57, respectively). Conclusions Our results highlight the potential of using RL to generate the optimal treatment based on the patients' longitude records. Our work can lead the path towards developing RL-based decision support systems that could help manage AD with comorbidities. [ABSTRACT FROM AUTHOR]

An increasing number of studies have reported using natural language processing (NLP) to assist observational research by extracting clinical information from electronic health records (EHRs). Currently, no standardized reporting guidelines for NLP‐assisted observational studies exist. The absence of detailed reporting guidelines may create ambiguity in the use of NLP‐derived content, knowledge gaps in the current research reporting practices, and reproducibility challenges. To address these issues, we conducted a scoping review of NLP‐assisted observational clinical studies and examined their reporting practices, focusing on NLP methodology and evaluation. Through our investigation, we discovered a high variation regarding the reporting practices, such as inconsistent use of references for measurement studies, variation in the reporting location (reference, appendix, and manuscript), and different granularity of NLP methodology and evaluation details. To promote the wide adoption and utilization of NLP solutions in clinical research, we outline several perspectives that align with the six principles released by the World Health Organization (WHO) that guide the ethical use of artificial intelligence for health. [ABSTRACT FROM AUTHOR]