The opioid crisis has reached epidemic proportions, yet risk of persistent opioid use following curative intent surgery for cancer and factors influencing this risk are not well understood.
We used ...electronic health record data from 3,901 adult patients who received a prescription for an opioid analgesic related to hysterectomy or large bowel surgery from January 1, 2013, through June 30, 2018. Patients with and without a cancer diagnosis were matched on the basis of demographic, clinical, and procedural variables and compared for persistent opioid use.
Cancer diagnosis was associated with greater risk for persistent opioid use after hysterectomy 18.9% vs. 9.6%; adjusted OR (aOR), 2.26; 95% confidence interval (CI), 1.38-3.69;
= 0.001, but not after large bowel surgery (28.3% vs. 24.1%; aOR 1.25; 95% CI, 0.97-1.59;
= 0.09). In the cancer hysterectomy cohort, persistent opioid use was associated with cancer stage (increased rates among those with stage III cancer compared with stage I) and use of neoadjuvant or adjuvant chemotherapy; however, these factors were not associated with persistent opioid use in the large bowel cohort.
Patients with cancer may have an increased risk of persistent opioid use following hysterectomy.
Risks and benefits of opioid analgesia for surgical pain among patients with cancer undergoing hysterectomy should be carefully considered.
Extending the NegEx lexicon for multiple languages Chapman, Wendy W; Hillert, Dieter; Velupillai, Sumithra ...
Proceedings of the 14th World Congress on Medical and Health Informatics,
2013, Letnik:
192
Journal Article, Conference Proceeding
Recenzirano
We translated an existing English negation lexicon (NegEx) to Swedish, French, and German and compared the lexicon on corpora from each language. We observed Zipf's law for all languages, i.e., a few ...phrases occur a large number of times, and a large number of phrases occur fewer times. Negation triggers "no" and "not" were common for all languages; however, other triggers varied considerably. The lexicon is available in OWL and RDF format and can be extended to other languages. We discuss the challenges in translating negation triggers to other languages and issues in representing multilingual lexical knowledge.
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In electronic health records, patterns of missing laboratory test results could capture patients’ course of disease as well as reflect clinician’s concerns or worries for possible ...conditions. These patterns are often understudied and overlooked. This study aims to identify informative patterns of missingness among laboratory data collected across 15 healthcare system sites in three countries for COVID-19 inpatients.
We collected and analyzed demographic, diagnosis, and laboratory data for 69,939 patients with positive COVID-19 PCR tests across three countries from 1 January 2020 through 30 September 2021. We analyzed missing laboratory measurements across sites, missingness stratification by demographic variables, temporal trends of missingness, correlations between labs based on missingness indicators over time, and clustering of groups of labs based on their missingness/ordering pattern.
With these analyses, we identified mapping issues faced in seven out of 15 sites. We also identified nuances in data collection and variable definition for the various sites. Temporal trend analyses may support the use of laboratory test result missingness patterns in identifying severe COVID-19 patients. Lastly, using missingness patterns, we determined relationships between various labs that reflect clinical behaviors.
In this work, we use computational approaches to relate missingness patterns to hospital treatment capacity and highlight the heterogeneity of looking at COVID-19 over time and at multiple sites, where there might be different phases, policies, etc. Changes in missingness could suggest a change in a patient’s condition, and patterns of missingness among laboratory measurements could potentially identify clinical outcomes. This allows sites to consider missing data as informative to analyses and help researchers identify which sites are better poised to study particular questions.
Oral case presentation is a crucial skill of physicians and a key component of team-based care. However, consistent and objective assessment and feedback on presentations during training are ...infrequent.
To determine the potential value of applying natural language processing, computer software that extracts meaning from text, to transcripts of oral case presentations as a strategy to assess their quality automatically and objectively.
We transcribed a collection of simulated oral case presentations. The presentations were from eight critical care fellows and one critical care attending. They were instructed to review the medical charts of 11 real intensive care unit patient cases and to audio record themselves, presenting each case as if they were doing so on morning rounds. We then used natural language processing to convert the transcripts from human-readable text into machine-readable numbers. These numbers represent details of the presentation style and content. The distance between the numeric representation of two different transcripts negatively correlates with the similarity of those two transcripts. We ranked fellows on the basis of how similar their presentations were to the attending's presentations.
The 99 presentations included 260 minutes of audio (mean length: 2.6 ± 1.24 min per case). On average, 23.88 ± 2.65 sentences were spoken, and each sentence had 14.10 ± 0.67 words, 3.62 ± 0.15 medical concepts, and 0.75 ± 0.09 medical adjectives. When ranking fellows on the basis of how similar their presentations were to the attending's presentation, we found a gap between the five fellows with the most similar presentations and the three fellows with the least similar presentations (average group similarity scores of 0.62 ± 0.01 and 0.53 ± 0.01, respectively). Rankings were sensitive to whether presentation style or content information were weighted more heavily when calculating transcript similarity.
Natural language processing enabled the ranking of case presentations on the basis of how similar they were to a reference presentation. Although additional work is needed to convert these rankings, and underlying similarity scores, into actionable feedback for trainees, these methods may support new tools for improving medical education.
Predicting 30-day readmission risk is paramount to improving the quality of patient care. In this study, we compare sets of patient-, provider-, and community-level variables that are available at ...two different points of a patient's inpatient encounter (first 48 hours and the full encounter) to train readmission prediction models and identify possible targets for appropriate interventions that can potentially reduce avoidable readmissions.
Using electronic health record data from a retrospective cohort of 2,460 oncology patients and a comprehensive machine learning analysis pipeline, we trained and tested models predicting 30-day readmission on the basis of data available within the first 48 hours of admission and from the entire hospital encounter.
Leveraging all features, the light gradient boosting model produced higher, but comparable performance (area under receiver operating characteristic curve AUROC: 0.711) with the Epic model (AUROC: 0.697). Given features in the first 48 hours, the random forest model produces higher AUROC (0.684) than the Epic model (AUROC: 0.676). Both models flagged patients with a similar distribution of race and sex; however, our light gradient boosting and random forest models were more inclusive, flagging more patients among younger age groups. The Epic models were more sensitive to identifying patients with an average lower zip income. Our 48-hour models were powered by novel features at various levels: patient (weight change over 365 days, depression symptoms, laboratory values, and cancer type), hospital (winter discharge and hospital admission type), and community (zip income and marital status of partner).
We developed and validated models comparable with the existing Epic 30-day readmission models with several novel actionable insights that could create service interventions deployed by the case management or discharge planning teams that may decrease readmission rates over time.
Personalized medicine is being realized by our ability to measure biological and environmental information about patients. Much of these data are being stored in electronic health records yielding ...big data that presents challenges for its management and analysis. Here, we review several areas of knowledge that are necessary for next-generation scientists to fully realize the potential of biomedical big data. We begin with an overview of big data and its storage and management. We then review statistics and data science as foundational topics followed by a core curriculum of artificial intelligence, machine learning and natural language processing that are needed to develop predictive models for clinical decision making. We end with some specific training recommendations for preparing next-generation scientists for biomedical big data.
In the United States, 795,000 people suffer strokes each year; 10-15 % of these strokes can be attributed to stenosis caused by plaque in the carotid artery, a major stroke phenotype risk factor. ...Studies comparing treatments for the management of asymptomatic carotid stenosis are challenging for at least two reasons: 1) administrative billing codes (i.e., Current Procedural Terminology (CPT) codes) that identify carotid images do not denote which neurovascular arteries are affected and 2) the majority of the image reports are negative for carotid stenosis. Studies that rely on manual chart abstraction can be labor-intensive, expensive, and time-consuming. Natural Language Processing (NLP) can expedite the process of manual chart abstraction by automatically filtering reports with no/insignificant carotid stenosis findings and flagging reports with significant carotid stenosis findings; thus, potentially reducing effort, costs, and time.
In this pilot study, we conducted an information content analysis of carotid stenosis mentions in terms of their report location (Sections), report formats (structures) and linguistic descriptions (expressions) from Veteran Health Administration free-text reports. We assessed an NLP algorithm, pyConText's, ability to discern reports with significant carotid stenosis findings from reports with no/insignificant carotid stenosis findings given these three document composition factors for two report types: radiology (RAD) and text integration utility (TIU) notes.
We observed that most carotid mentions are recorded in prose using categorical expressions, within the Findings and Impression sections for RAD reports and within neither of these designated sections for TIU notes. For RAD reports, pyConText performed with high sensitivity (88 %), specificity (84 %), and negative predictive value (95 %) and reasonable positive predictive value (70 %). For TIU notes, pyConText performed with high specificity (87 %) and negative predictive value (92 %), reasonable sensitivity (73 %), and moderate positive predictive value (58 %). pyConText performed with the highest sensitivity processing the full report rather than the Findings or Impressions independently.
We conclude that pyConText can reduce chart review efforts by filtering reports with no/insignificant carotid stenosis findings and flagging reports with significant carotid stenosis findings from the Veteran Health Administration electronic health record, and hence has utility for expediting a comparative effectiveness study of treatment strategies for stroke prevention.
As of August 2020, there were ~6 million COVID-19 cases in the United States of America, resulting in ~200,000 deaths. Informatics approaches are needed to better understand the role of individual ...and community risk factors for COVID-19. We developed an informatics method to integrate SARS-CoV-2 data with multiple neighborhood-level factors from the American Community Survey and opendataphilly.org. We assessed the spatial association between neighborhood-level factors and the frequency of SARS-CoV-2 positivity, separately across all patients and across asymptomatic patients. We found that neighborhoods with higher proportions of individuals with a high-school degree and/or who were identified as Hispanic/Latinx were more likely to have higher SARS-CoV-2 positivity rates, after adjusting for other neighborhood covariates. Patients from neighborhoods with higher proportions of individuals receiving public assistance and/or identified as White were less likely to test positive for SARS-CoV-2. Our approach and its findings could inform future public health efforts.
Free-text reports in electronic health records (EHRs) contain medically significant information - signs, symptoms, findings, diagnoses - recorded by clinicians during patient encounters. These ...reports contain rich clinical information which can be leveraged for surveillance of disease and occurrence of adverse events. In order to gain meaningful knowledge from these text reports to support surveillance efforts, information must first be converted into a structured, computable format. Traditional methods rely on manual review of charts, which can be costly and inefficient. Natural language processing (NLP) methods offer an efficient, alternative approach to extracting the information and can achieve a similar level of accuracy. We developed an NLP system to automatically identify mentions of surgical site infections in radiology reports and classify reports containing evidence of surgical site infections leveraging these mentions. We evaluated our system using a reference standard of reports annotated by domain experts, administrative data generated for each patient encounter, and a machine learning-based approach.
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