Annotation and extraction of age and temporally-related events from clinical histories

Abstract Background Age and time information stored within the histories of clinical notes can provide valuable insights for assessing a patient’s disease risk, understanding disease progression, and studying therapeutic outcomes. However, details of age and temporally-specified clinical events are not well captured, consistently codified, and readily available to research databases for study. Methods We expanded upon existing annotation schemes to capture additional age and temporal information, conducted an annotation study to validate our expanded schema, and developed a prototypical, rule-based Named Entity Recognizer to extract our novel clinical named entities (NE). The annotation study was conducted on 138 discharge summaries from the pre-annotated 2014 ShARe/CLEF eHealth Challenge corpus. In addition to existing NE classes (TIMEX3, SUBJECT_CLASS, DISEASE_DISORDER), our schema proposes 3 additional NEs (AGE, PROCEDURE, OTHER_EVENTS). We also propose new attributes, e.g., “degree_relation” which captures the degree of biological relation for subjects annotated under SUBJECT_CLASS. As a proof of concept, we applied the schema to 49 H&P notes to encode pertinent history information for a lung cancer cohort study. Results An abundance of information was captured under the new OTHER_EVENTS, PROCEDURE and AGE classes, with 23%, 10% and 8% of all annotated NEs belonging to the above classes, respectively. We observed high inter-annotator agreement of >80% for AGE and TIMEX3; the automated NLP system achieved F1 scores of 86% (AGE) and 86% (TIMEX3). Age and temporally-specified mentions within past medical, family, surgical, and social histories were common in our lung cancer data set; annotation is ongoing to support this translational research study. Conclusions Our annotation schema and NLP system can encode historical events from clinical notes to support clinical and translational research studies.

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Investigation of the international comparability of population-based routine hospital data set derived comorbidity scores for patients with lung cancer

Thorax ◽

10.1136/thoraxjnl-2017-210362 ◽

2017 ◽

Vol 73 (4) ◽

pp. 339-349 ◽

Cited By ~ 4

Author(s):

Margreet Lüchtenborg ◽

Eva J A Morris ◽

Daniela Tataru ◽

Victoria H Coupland ◽

Andrew Smith ◽

...

Keyword(s):

Lung Cancer ◽

Cancer Survival ◽

Population Based ◽

Data Sets ◽

Hospital Data ◽

Data Set ◽

Discharge Data ◽

International Differences ◽

Cancer Data ◽

Comorbidity Scores

IntroductionThe International Cancer Benchmarking Partnership (ICBP) identified significant international differences in lung cancer survival. Differing levels of comorbid disease across ICBP countries has been suggested as a potential explanation of this variation but, to date, no studies have quantified its impact. This study investigated whether comparable, robust comorbidity scores can be derived from the different routine population-based cancer data sets available in the ICBP jurisdictions and, if so, use them to quantify international variation in comorbidity and determine its influence on outcome.MethodsLinked population-based lung cancer registry and hospital discharge data sets were acquired from nine ICBP jurisdictions in Australia, Canada, Norway and the UK providing a study population of 233 981 individuals. For each person in this cohort Charlson, Elixhauser and inpatient bed day Comorbidity Scores were derived relating to the 4–36 months prior to their lung cancer diagnosis. The scores were then compared to assess their validity and feasibility of use in international survival comparisons.ResultsIt was feasible to generate the three comorbidity scores for each jurisdiction, which were found to have good content, face and concurrent validity. Predictive validity was limited and there was evidence that the reliability was questionable.ConclusionThe results presented here indicate that interjurisdictional comparability of recorded comorbidity was limited due to probable differences in coding and hospital admission practices in each area. Before the contribution of comorbidity on international differences in cancer survival can be investigated an internationally harmonised comorbidity index is required.

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Comparison of haplotype-based tests for detecting gene–environment interactions with rare variants

Briefings in Bioinformatics ◽

10.1093/bib/bbz031 ◽

2019 ◽

Vol 21 (3) ◽

pp. 851-862 ◽

Cited By ~ 1

Author(s):

Charalampos Papachristou ◽

Swati Biswas

Keyword(s):

Lung Cancer ◽

Rare Variants ◽

Practical Interest ◽

Serum Triglyceride ◽

Error Rates ◽

Type I ◽

Rare Haplotype ◽

Data Set ◽

Cancer Data ◽

Gene Environment

Abstract Dissecting the genetic mechanism underlying a complex disease hinges on discovering gene–environment interactions (GXE). However, detecting GXE is a challenging problem especially when the genetic variants under study are rare. Haplotype-based tests have several advantages over the so-called collapsing tests for detecting rare variants as highlighted in recent literature. Thus, it is of practical interest to compare haplotype-based tests for detecting GXE including the recent ones developed specifically for rare haplotypes. We compare the following methods: haplo.glm, hapassoc, HapReg, Bayesian hierarchical generalized linear model (BhGLM) and logistic Bayesian LASSO (LBL). We simulate data under different types of association scenarios and levels of gene–environment dependence. We find that when the type I error rates are controlled to be the same for all methods, LBL is the most powerful method for detecting GXE. We applied the methods to a lung cancer data set, in particular, in region 15q25.1 as it has been suggested in the literature that it interacts with smoking to affect the lung cancer susceptibility and that it is associated with smoking behavior. LBL and BhGLM were able to detect a rare haplotype–smoking interaction in this region. We also analyzed the sequence data from the Dallas Heart Study, a population-based multi-ethnic study. Specifically, we considered haplotype blocks in the gene ANGPTL4 for association with trait serum triglyceride and used ethnicity as a covariate. Only LBL found interactions of haplotypes with race (Hispanic). Thus, in general, LBL seems to be the best method for detecting GXE among the ones we studied here. Nonetheless, it requires the most computation time.

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A Generic Data Harmonization Process for Cross-linked Research and Network Interaction

Methods of Information in Medicine ◽

10.3414/me14-02-0030 ◽

2015 ◽

Vol 54 (05) ◽

pp. 455-460 ◽

Cited By ~ 4

Author(s):

M. Ganzinger ◽

T. Muley ◽

M. Thomas ◽

P. Knaup ◽

D. Firnkorn

Keyword(s):

Lung Cancer ◽

Research Networks ◽

Data Set ◽

Common Data Elements ◽

Cancer Data ◽

Data Harmonization ◽

Wide Range ◽

Harmonization Process ◽

Cancer Phenotype ◽

Data Elements

Summary Objective: Joint data analysis is a key requirement in medical research networks. Data are available in heterogeneous formats at each network partner and their harmonization is often rather complex. The objective of our paper is to provide a generic approach for the harmonization process in research networks. We applied the process when harmonizing data from three sites for the Lung Cancer Phenotype Database within the German Center for Lung Research. Methods: We developed a spreadsheet-based solution as tool to support the harmonization process for lung cancer data and a data integration procedure based on Talend Open Studio. Results: The harmonization process consists of eight steps describing a systematic approach for defining and reviewing source data elements and standardizing common data elements. The steps for defining common data elements and harmonizing them with local data definitions are repeated until consensus is reached. Application of this process for building the phenotype database led to a common basic data set on lung cancer with 285 structured parameters. The Lung Cancer Phenotype Database was realized as an i2b2 research data warehouse. Conclusion: Data harmonization is a challenging task requiring informatics skills as well as domain knowledge. Our approach facilitates data harmonization by providing guidance through a uniform process that can be applied in a wide range of projects.

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A Critical Assessment of Geographic Clusters of Breast and Lung Cancer Incidences among Residents Living near the Tittabawassee and Saginaw Rivers, Michigan, USA

Journal of Environmental and Public Health ◽

10.1155/2009/316249 ◽

2009 ◽

Vol 2009 ◽

pp. 1-16 ◽

Cited By ~ 3

Author(s):

Olga A. Guajardo ◽

Tonny J. Oyana

Keyword(s):

Lung Cancer ◽

Point Source ◽

Environmental Contamination ◽

Disease Risk ◽

Spatial Association ◽

Point Source Pollution ◽

Cancer Data ◽

Increased Risk ◽

Close Proximity ◽

Local Risk

Objectives. To assess previously determined geographic clusters of breast and lung cancer incidences among residents living near the Tittabawassee and Saginaw Rivers, Michigan, using a new set of environmental factors.Materials and Methods. Breast and lung cancer data were acquired from the Michigan Department of Community Health, along with point source pollution data from the U.S. Environmental Protection Agency. The datasets were used to determine whether there is a spatial association between disease risk and environmental contamination. GIS and spatial techniques were combined with statistical analysis to investigate local risk of breast and lung cancer.Results and Conclusion. The study suggests that neighborhoods in close proximity to the river were associated with a high risk of breast cancer, while increased risk of lung cancer was detected among neighborhoods in close proximity to point source pollution and major highways. Statistically significant(P≤.001)clusters of cancer incidences were observed among residents living near the rivers. These findings are useful to researchers and governmental agencies for risk assessment, regulation, and control of environmental contamination in the floodplains.

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High-Dimensional Mediation Analysis With Confounders in Survival Models

Frontiers in Genetics ◽

10.3389/fgene.2021.688871 ◽

2021 ◽

Vol 12 ◽

Author(s):

Zhangsheng Yu ◽

Yidan Cui ◽

Ting Wei ◽

Yanran Ma ◽

Chengwen Luo

Keyword(s):

Lung Cancer ◽

Mediation Analysis ◽

Mediation Effect ◽

Smoking History ◽

Survival Models ◽

High Dimensional ◽

Data Set ◽

Cancer Data ◽

Risk Of Death ◽

Effect Estimation

Mediation analysis is a common statistical method for investigating the mechanism of environmental exposures on health outcomes. Previous studies have extended mediation models with a single mediator to high-dimensional mediators selection. It is often assumed that there are no confounders that influence the relations among the exposure, mediator, and outcome. This is not realistic for the observational studies. To accommodate the potential confounders, we propose a concise and efficient high-dimensional mediation analysis procedure using the propensity score for adjustment. Results from simulation studies demonstrate the proposed procedure has good performance in mediator selection and effect estimation compared with methods that ignore all confounders. Of note, as the sample size increases, the performance of variable selection and mediation effect estimation is as well as the results shown in the method which include all confounders as covariates in the mediation model. By applying this procedure to a TCGA lung cancer data set, we find that lung cancer patients who had serious smoking history have increased the risk of death via the methylation markers cg21926276 and cg20707991 with significant hazard ratios of 1.2093 (95% CI: 1.2019–1.2167) and 1.1388 (95% CI: 1.1339–1.1438), respectively.

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Integrating Speculation Detection and Deep Learning to Extract Lung Cancer Diagnosis from Clinical Notes

Applied Sciences ◽

10.3390/app11020865 ◽

2021 ◽

Vol 11 (2) ◽

pp. 865

Author(s):

Oswaldo Solarte Pabón ◽

Maria Torrente ◽

Mariano Provencio ◽

Alejandro Rodríguez-Gonzalez ◽

Ernestina Menasalvas

Keyword(s):

Lung Cancer ◽

Deep Learning ◽

Cancer Diagnosis ◽

Hybrid Approach ◽

Named Entity Recognition ◽

Entity Recognition ◽

Clinical Notes ◽

Named Entity ◽

Lung Cancer Diagnosis ◽

Diagnosis Date

Despite efforts to develop models for extracting medical concepts from clinical notes, there are still some challenges in particular to be able to relate concepts to dates. The high number of clinical notes written for each single patient, the use of negation, speculation, and different date formats cause ambiguity that has to be solved to reconstruct the patient’s natural history. In this paper, we concentrate on extracting from clinical narratives the cancer diagnosis and relating it to the diagnosis date. To address this challenge, a hybrid approach that combines deep learning-based and rule-based methods is proposed. The approach integrates three steps: (i) lung cancer named entity recognition, (ii) negation and speculation detection, and (iii) relating the cancer diagnosis to a valid date. In particular, we apply the proposed approach to extract the lung cancer diagnosis and its diagnosis date from clinical narratives written in Spanish. Results obtained show an F-score of 90% in the named entity recognition task, and a 89% F-score in the task of relating the cancer diagnosis to the diagnosis date. Our findings suggest that speculation detection is together with negation detection a key component to properly extract cancer diagnosis from clinical notes.

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Perbandingan CART dan Random Forest untuk Deteksi Kanker berbasis Klasifikasi Data Microarray

Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) ◽

10.29207/resti.v4i5.2083 ◽

2020 ◽

Vol 4 (5) ◽

pp. 805-812

Author(s):

Riska Chairunisa ◽

Adiwijaya ◽

Widi Astuti

Keyword(s):

Breast Cancer ◽

Lung Cancer ◽

Ovarian Cancer ◽

Random Forest ◽

Classification And Regression Tree ◽

Classification Method ◽

Discrete Wavelet ◽

Prostate Tumors ◽

Cancer Data ◽

Colon Tumors

Cancer is one of the deadliest diseases in the world with a mortality rate of 57,3% in 2018 in Asia. Therefore, early diagnosis is needed to avoid an increase in mortality caused by cancer. As machine learning develops, cancer gene data can be processed using microarrays for early detection of cancer outbreaks. But the problem that microarray has is the number of attributes that are so numerous that it is necessary to do dimensional reduction. To overcome these problems, this study used dimensions reduction Discrete Wavelet Transform (DWT) with Classification and Regression Tree (CART) and Random Forest (RF) as classification method. The purpose of using these two classification methods is to find out which classification method produces the best performance when combined with the DWT dimension reduction. This research use five microarray data, namely Colon Tumors, Breast Cancer, Lung Cancer, Prostate Tumors and Ovarian Cancer from Kent-Ridge Biomedical Dataset. The best accuracy obtained in this study for breast cancer data were 76,92% with CART-DWT, Colon Tumors 90,1% with RF-DWT, lung cancer 100% with RF-DWT, prostate tumors 95,49% with RF-DWT, and ovarian cancer 100% with RF-DWT. From these results it can be concluded that RF-DWT is better than CART-DWT.

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Patients with Metastatic Lung Cancer and Oncologists’ Views on Achievement of Treatment Goals and Making the Right Treatment Decision: A Prospective Multicenter Study

Medical Decision Making ◽

10.1177/0272989x21998951 ◽

2021 ◽

pp. 0272989X2199895

Author(s):

Adinda Mieras ◽

Annemarie Becker-Commissaris ◽

Hanna T. Klop ◽

H. Roeline W. Pasman ◽

Denise de Jong ◽

...

Keyword(s):

Lung Cancer ◽

Multicenter Study ◽

Systemic Treatment ◽

Treatment Decision ◽

Treatment Goals ◽

Metastatic Lung Cancer ◽

Prospective Multicenter Study ◽

Cancer Data ◽

Metastatic Lung ◽

The Right

Background Previous studies have investigated patients’ treatment goals before starting a treatment for metastatic lung cancer. Data on the evaluation of treatment goals are lacking. Aim To determine if patients with metastatic lung cancer and their oncologists perceive the treatment goals they defined at the start of systemic treatment as achieved after treatment and if in hindsight they believe it was the right decision to start systemic therapy. Design and Participants A prospective multicenter study in 6 hospitals across the Netherlands between 2016 and 2018. Following systemic treatment, 146 patients with metastatic lung cancer and 23 oncologists completed a questionnaire on the achievement of their treatment goals and whether they made the right treatment decision. Additional interviews with 15 patients and 5 oncologists were conducted. Results According to patients and oncologists, treatment goals were achieved in 30% and 37% for ‘quality of life,’ 49% and 41% for ‘life prolongation,’ 26% and 44% for ‘decrease in tumor size,’ and 44% for ‘cure’, respectively. Most patients and oncologists, in hindsight, felt they had made the right decision to start treatment and also if they had not achieved their goals (72% and 93%). This was related to the feeling that they had to do ‘something.’ Conclusions Before deciding on treatment, the treatment options, including their benefits and side effects, and the goals patients have should be discussed. It is key that these discussions include not only systemic treatment but also palliative care as effective options for doing ‘something.’

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Text Mining of Hazard and Operability Analysis Reports Based on Active Learning

Processes ◽

10.3390/pr9071178 ◽

2021 ◽

Vol 9 (7) ◽

pp. 1178

Author(s):

Zhenhua Wang ◽

Beike Zhang ◽

Dong Gao

Keyword(s):

Active Learning ◽

Named Entity Recognition ◽

Entity Recognition ◽

Chemical System ◽

Chemical Safety ◽

High Quality ◽

Data Set ◽

Final Model ◽

Named Entity ◽

Sampling Algorithms

In the field of chemical safety, a named entity recognition (NER) model based on deep learning can mine valuable information from hazard and operability analysis (HAZOP) text, which can guide experts to carry out a new round of HAZOP analysis, help practitioners optimize the hidden dangers in the system, and be of great significance to improve the safety of the whole chemical system. However, due to the standardization and professionalism of chemical safety analysis text, it is difficult to improve the performance of traditional models. To solve this problem, in this study, an improved method based on active learning is proposed, and three novel sampling algorithms are designed, Variation of Token Entropy (VTE), HAZOP Confusion Entropy (HCE) and Amplification of Least Confidence (ALC), which improve the ability of the model to understand HAZOP text. In this method, a part of data is used to establish the initial model. The sampling algorithm is then used to select high-quality samples from the data set. Finally, these high-quality samples are used to retrain the whole model to obtain the final model. The experimental results show that the performance of the VTE, HCE, and ALC algorithms are better than that of random sampling algorithms. In addition, compared with other methods, the performance of the traditional model is improved effectively by the method proposed in this paper, which proves that the method is reliable and advanced.

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Design of novel multi filter union feature selection framework for breast cancer dataset

Concurrent Engineering ◽

10.1177/1063293x211016046 ◽

2021 ◽

pp. 1063293X2110160

Author(s):

Dinesh Morkonda Gunasekaran ◽

Prabha Dhandayudam

Keyword(s):

Breast Cancer ◽

Feature Selection ◽

Care Center ◽

Feature Selection Method ◽

Selection Method ◽

Cancer Center ◽

Breast Cancer Dataset ◽

Data Set ◽

Health Care Center ◽

Cancer Data

Nowadays women are commonly diagnosed with breast cancer. Feature based Selection method plays an important step while constructing a classification based framework. We have proposed Multi filter union (MFU) feature selection method for breast cancer data set. The feature selection process based on random forest algorithm and Logistic regression (LG) algorithm based union model is used for selecting important features in the dataset. The performance of the data analysis is evaluated using optimal features subset from selected dataset. The experiments are computed with data set of Wisconsin diagnostic breast cancer center and next the real data set from women health care center. The result of the proposed approach shows high performance and efficient when comparing with existing feature selection algorithms.

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