Curation of the CANDID-PTX Dataset with Free-Text Reports.
Ontology highlight
ABSTRACT:
SUBMITTER: Feng S
PROVIDER: S-EPMC8637219 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Background:Free-text sections of pathology reports contain the most important information from a diagnostic standpoint. However, this information is largely underutilized for computer-based analytics. The vast majority of NLP-based methods lack a capacity to accurately extract complex diagnostic entities and relationships among them as well as to provide an adequate knowledge representation for downstream data-mining applications. Methods:In this paper, we introduce a novel informatics pipeline that extends open information extraction (openIE) techniques with artificial intelligence (AI) based modeling to extract and transform complex diagnostic entities and relationships among them into Knowledge Graphs (KGs) of relational triples (RTs). Results:Evaluation studies have demonstrated that the pipeline's output significantly differs from a random process. The semantic similarity with original reports is high (Mean Weighted Overlap of 0.83). The precision and recall of extracted RTs based on experts' assessment were 0.925 and 0.841 respectively (P <0.0001). Inter-rater agreement was significant at 93.6% and inter-rated reliability was 81.8%. Conclusion:The results demonstrated important properties of the pipeline such as high accuracy, minimality and adequate knowledge representation. Therefore, we conclude that the pipeline can be used in various downstream data-mining applications to assist diagnostic medicine.
| S-EPMC7045509 | biostudies-literature
Project description:BACKGROUND:Some medications carry increased risk of patient harm when they are given in error. In incident reports, names of the medications that are involved in errors could be found written both in a specific medication field and/or within the free text description of the incident. Analysing only the names of the medications implicated in a specific unstructured medication field does not give information of the associated factors and risk areas, but when analysing unstructured free text descriptions, the information about the medication involved and associated risk factors may be buried within other non-relevant text. Thus, the aim of this study was to extract medication names most commonly used in free text descriptions of medication administration incident reports to identify terms most frequently associated with risk for each of these medications using text mining. METHOD:Free text descriptions of medication administration incidents (n = 72,390) reported in 2016 to the National Reporting and Learning System for England and Wales were analysed using SAS® Text miner. Analysis included text parsing and filtering free text to identify most commonly mentioned medications, followed by concept linking, and clustering to identify terms associated with commonly mentioned medications and the associated risk areas. RESULTS:The following risk areas related to medications were identified: 1. Allergic reactions to antibacterial drugs, 2. Intravenous administration of antibacterial drugs, 3. Fentanyl patches, 4. Checking and documenting of analgesic doses, 5. Checking doses of anticoagulants, 6. Insulin doses and blood glucose, 7. Administration of intravenous infusions. CONCLUSIONS:Interventions to increase medication administration safety should focus on checking patient allergies and medication doses, especially for intravenous and transdermal medications. High-risk medications include insulin, analgesics, antibacterial drugs, anticoagulants, and potassium chloride. Text mining may be useful for analysing large free text datasets and should be developed further.
| S-EPMC6829803 | biostudies-other
Project description:Precision medicine tailors treatment to individuals personal data including differences in their genome. The Pharmacogenomics Knowledgebase (PharmGKB) provides highly curated information on the effect of genetic variation on drug response and side effects for a wide range of drugs. PharmGKB's scientific curators triage, review and annotate a large number of papers each year but the task is challenging. We present the PGxMine resource, a text-mined resource of pharmacogenomic associations from all accessible published literature to assist in the curation of PharmGKB. We developed a supervised machine learning pipeline to extract associations between a variant (DNA and protein changes, star alleles and dbSNP identifiers) and a chemical. PGxMine covers 452 chemicals and 2,426 variants and contains 19,930 mentions of pharmacogenomic associations across 7,170 papers. An evaluation by PharmGKB curators found that 57 of the top 100 associations not found in PharmGKB led to 83 curatable papers and a further 24 associations would likely lead to curatable papers through citations. The results can be viewed at https://pgxmine.pharmgkb.org/ and code can be downloaded at https://github.com/jakelever/pgxmine.
| S-EPMC6917032 | biostudies-literature
Project description:ObjectiveTo classify automatically lung tumor-node-metastases (TNM) cancer stages from free-text pathology reports using symbolic rule-based classification.DesignBy exploiting report substructure and the symbolic manipulation of systematized nomenclature of medicine-clinical terms (SNOMED CT) concepts in reports, statements in free text can be evaluated for relevance against factors relating to the staging guidelines. Post-coordinated SNOMED CT expressions based on templates were defined and populated by concepts in reports, and tested for subsumption by staging factors. The subsumption results were used to build logic according to the staging guidelines to calculate the TNM stage.MeasurementsThe accuracy measure and confusion matrices were used to evaluate the TNM stages classified by the symbolic rule-based system. The system was evaluated against a database of multidisciplinary team staging decisions and a machine learning-based text classification system using support vector machines.ResultsOverall accuracy on a corpus of pathology reports for 718 lung cancer patients against a database of pathological TNM staging decisions were 72%, 78%, and 94% for T, N, and M staging, respectively. The system's performance was also comparable to support vector machine classification approaches.ConclusionA system to classify lung TNM stages from free-text pathology reports was developed, and it was verified that the symbolic rule-based approach using SNOMED CT can be used for the extraction of key lung cancer characteristics from free-text reports. Future work will investigate the applicability of using the proposed methodology for extracting other cancer characteristics and types.
| S-EPMC2995652 | biostudies-literature
Project description:Chest radiography is an extremely powerful imaging modality, allowing for a detailed inspection of a patient's chest, but requires specialized training for proper interpretation. With the advent of high performance general purpose computer vision algorithms, the accurate automated analysis of chest radiographs is becoming increasingly of interest to researchers. Here we describe MIMIC-CXR, a large dataset of 227,835 imaging studies for 65,379 patients presenting to the Beth Israel Deaconess Medical Center Emergency Department between 2011-2016. Each imaging study can contain one or more images, usually a frontal view and a lateral view. A total of 377,110 images are available in the dataset. Studies are made available with a semi-structured free-text radiology report that describes the radiological findings of the images, written by a practicing radiologist contemporaneously during routine clinical care. All images and reports have been de-identified to protect patient privacy. The dataset is made freely available to facilitate and encourage a wide range of research in computer vision, natural language processing, and clinical data mining.
| S-EPMC6908718 | biostudies-literature
Project description:As part of its core business of gathering population-based information on new cancer diagnoses, the Belgian Cancer Registry receives free-text pathology reports, describing results of (pre-)malignant specimens. These reports are provided by 82 laboratories and written in 2 national languages, Dutch or French. For breast cancer, the reports characterize the status of estrogen receptor, progesterone receptor, and Erb-b2 receptor tyrosine kinase 2. These biomarkers are related with tumor growth and prognosis and are essential to define therapeutic management. The availability of population-scale information about their status in breast cancer patients can therefore be considered crucial to enrich real-world scientific studies and to guide public health policies regarding personalized medicine. The main objective of this study is to expand the data available at the Belgian Cancer Registry by automatically extracting the status of these biomarkers from the pathology reports. Various types of numeric features are computed from over 1,300 manually annotated reports linked to breast tumors diagnosed in 2014. A range of popular machine learning classifiers, such as support vector machines, random forests and logistic regressions, are trained on this data and compared using their F 1 scores on a separate validation set. On a held-out test set, the best performing classifiers achieve F 1 scores ranging from 0.89 to 0.92 for the four classification tasks. The extraction is thus reliable and allows to significantly increase the availability of this valuable information on breast cancer receptor status at a population level.
| S-EPMC8522027 | biostudies-literature
Project description:IntroductionRadiological imaging is one of the most frequently performed diagnostic tests worldwide. The free-text contained in radiology reports is currently only rarely used for secondary use purposes, including research and predictive analysis. However, this data might be made available by means of information extraction (IE), based on natural language processing (NLP). Recently, a new approach to NLP, large language models (LLMs), has gained momentum and continues to improve performance of IE-related tasks. The objective of this scoping review is to show the state of research regarding IE from free-text radiology reports based on LLMs, to investigate applied methods and to guide future research by showing open challenges and limitations of current approaches. To our knowledge, no systematic or scoping review of IE from radiology reports based on LLMs has been published. Existing publications are outdated and do not comprise LLM-based methods.Methods and analysisThis protocol is designed based on the JBI Manual for Evidence Synthesis, chapter 11.2: 'Development of a scoping review protocol'. Inclusion criteria and a search strategy comprising four databases (PubMed, IEEE Xplore, Web of Science Core Collection and ACM Digital Library) are defined. Furthermore, we describe the screening process, data charting, analysis and presentation of extracted data.Ethics and disseminationThis protocol describes the methodology of a scoping literature review and does not comprise research on or with humans, animals or their data. Therefore, no ethical approval is required. After the publication of this protocol and the conduct of the review, its results are going to be published in an open access journal dedicated to biomedical informatics/digital health.
| S-EPMC10729196 | biostudies-literature
Project description:Text-mining algorithms make mistakes in extracting facts from natural-language texts. In biomedical applications, which rely on use of text-mined data, it is critical to assess the quality (the probability that the message is correctly extracted) of individual facts--to resolve data conflicts and inconsistencies. Using a large set of almost 100,000 manually produced evaluations (most facts were independently reviewed more than once, producing independent evaluations), we implemented and tested a collection of algorithms that mimic human evaluation of facts provided by an automated information-extraction system. The performance of our best automated classifiers closely approached that of our human evaluators (ROC score close to 0.95). Our hypothesis is that, were we to use a larger number of human experts to evaluate any given sentence, we could implement an artificial-intelligence curator that would perform the classification job at least as accurately as an average individual human evaluator. We illustrated our analysis by visualizing the predicted accuracy of the text-mined relations involving the term cocaine.
| S-EPMC1560402 | biostudies-literature
Project description:BackgroundDiagnostic neurovascular imaging data are important in stroke research, but obtaining these data typically requires laborious manual chart reviews.ObjectiveWe aimed to determine the accuracy of a natural language processing (NLP) approach to extract information on the presence and location of vascular occlusions as well as other stroke-related attributes based on free-text reports.MethodsFrom the full reports of 1320 consecutive computed tomography (CT), CT angiography, and CT perfusion scans of the head and neck performed at a tertiary stroke center between October 2017 and January 2019, we manually extracted data on the presence of proximal large vessel occlusion (primary outcome), as well as distal vessel occlusion, ischemia, hemorrhage, Alberta stroke program early CT score (ASPECTS), and collateral status (secondary outcomes). Reports were randomly split into training (n=921) and validation (n=399) sets, and attributes were extracted using rule-based NLP. We reported the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and the overall accuracy of the NLP approach relative to the manually extracted data.ResultsThe overall prevalence of large vessel occlusion was 12.2%. In the training sample, the NLP approach identified this attribute with an overall accuracy of 97.3% (95.5% sensitivity, 98.1% specificity, 84.1% PPV, and 99.4% NPV). In the validation set, the overall accuracy was 95.2% (90.0% sensitivity, 97.4% specificity, 76.3% PPV, and 98.5% NPV). The accuracy of identifying distal or basilar occlusion as well as hemorrhage was also high, but there were limitations in identifying cerebral ischemia, ASPECTS, and collateral status.ConclusionsNLP may improve the efficiency of large-scale imaging data collection for stroke surveillance and research.
| S-EPMC8132979 | biostudies-literature
Project description:Deep learning has yielded promising results for medical image diagnosis but relies heavily on manual image annotations, which are expensive to acquire. We present Cross-DL, a cross-modality learning framework for intracranial abnormality detection and localization in head computed tomography (CT) scans by learning from free-text imaging reports. Cross-DL has a discretizer that automatically extracts discrete labels of abnormality types and locations from reports, which are utilized to train an image analyzer by a dynamic multi-instance learning approach. Benefiting from the low annotation cost and a consequent large-scale training set of 28,472 CT scans, Cross-DL achieves accurate performance, with an average area under the receiver operating characteristic curve (AUROC) of 0.956 (95% confidence interval: 0.952-0.959) in detecting 4 abnormality types in 17 regions while accurately localizing abnormalities at the voxel level. An intracranial hemorrhage classification experiment on the external dataset CQ500 achieves an AUROC of 0.928 (0.905-0.951). The model can also help review prioritization.
| S-EPMC10518589 | biostudies-literature