Automatic Classification of Ultrasound Screening Examinations of the Abdominal Aorta.

Our work facilitates the identification of veterans who may be at risk for abdominal aortic aneurysms (AAA) based on the 2007 mandate to screen all veteran patients that meet the screening criteria. The main research objective is to automatically index three clinical conditions: pertinent negative AAA, pertinent positive AAA, and visually unacceptable image exams. We developed and evaluated a ConText-based algorithm with the GATE (General Architecture for Text Engineering) development system to automatically classify 1402 ultrasound radiology reports for AAA screening. Using the results from JAPE (Java Annotation Pattern Engine) transducer rules, we developed a feature vector to classify the radiology reports with a decision table classifier. We found that ConText performed optimally on precision and recall for pertinent negative (0.99 (0.98-0.99), 0.99 (0.99-1.00)) and pertinent positive AAA detection (0.98 (0.95-1.00), 0.97 (0.92-1.00)), and respectably for determination of non-diagnostic image studies (0.85 (0.77-0.91), 0.96 (0.91-0.99)). In addition, our algorithm can determine the AAA size measurements for further characterization of abnormality. We developed and evaluated a regular expression based algorithm using GATE for determining the three contextual conditions: pertinent negative, pertinent positive, and non-diagnostic from radiology reports obtained for evaluating the presence or absence of abdominal aortic aneurysm. ConText performed very well at identifying the contextual features. Our study also discovered contextual trigger terms to detect sub-standard ultrasound image quality. Limitations of performance included unknown dictionary terms, complex sentences, and vague findings that were difficult to classify and properly code.

Emergency department visits, inpatient hospitalizations, and non-fatal and fatal drug overdoses during COVID-19 among Veterans with opioid use disorder.

BACKGROUND: The COVID-19 pandemic had dramatic adverse impacts on people with opioid use disorder (OUD), as evidenced by significant disruptions to care and unprecedented increases in drug overdoses. In this study, we evaluated the impacts of COVID-19 on utilization of emergency and inpatient care, and fatal and non-fatal overdoses among veterans with OUD. METHODS: We used Veterans Health Administration (VHA) electronic medical record and mortality data to compare emergency department (ED) visits, inpatient hospitalizations, and fatal and non-fatal overdoses between a pandemic-exposed cohort of veterans with OUD observed both pre- and post-pandemic onset (n = 53,803; observed January 2019-March 2021) to a matched pre-pandemic control group (n = 53,803; observed October 2017-December 2019). RESULTS: Compared to pre-pandemic trends, there were significant decreases in the odds of ED and inpatient admissions and total number of ED and inpatient admissions during COVID-19. There was a significant decrease in the odds of having a recorded non-fatal overdose. The odds of overdose death increased during the pandemic compared to pre-pandemic trends. CONCLUSION: We observed significant decreases in the utilization of ED and inpatient care services, and fewer non-fatal overdoses, post-pandemic onset. Healthcare disruptions limiting access to emergency and inpatient care could account for the lower number of recorded non-fatal overdoses, potentially reflecting an underestimate of risk. In contrast, fatal overdoses increased during the pandemic compared to pre-pandemic trends. Lower utilization of emergency and inpatient care, and higher rates of fatal overdoses during the pandemic, suggest an exacerbation of unmet treatment need post-pandemic onset.

Challenges to Using Big Data in Cancer.

Big data in healthcare can enable unprecedented understanding of diseases and their treatment, particularly in oncology. These data may include electronic health records, medical imaging, genomic sequencing, payor records, and data from pharmaceutical research, wearables, and medical devices. The ability to combine datasets and use data across many analyses is critical to the successful use of big data and is a concern for those who generate and use the data. Interoperability and data quality continue to be major challenges when working with different healthcare datasets. Mapping terminology across datasets, missing and incorrect data, and varying data structures make combining data an onerous and largely manual undertaking. Data privacy is another concern addressed by the Health Insurance Portability and Accountability Act, the Common Rule, and the General Data Protection Regulation. The use of big data is now included in the planning and activities of the FDA and the European Medicines Agency. The willingness of organizations to share data in a precompetitive fashion, agreements on data quality standards, and institution of universal and practical tenets on data privacy will be crucial to fully realizing the potential for big data in medicine.

Case Studies for Overcoming Challenges in Using Big Data in Cancer.

The analysis of big healthcare data has enormous potential as a tool for advancing oncology drug development and patient treatment, particularly in the context of precision medicine. However, there are challenges in organizing, sharing, integrating, and making these data readily accessible to the research community. This review presents five case studies illustrating various successful approaches to addressing such challenges. These efforts are CancerLinQ, the American Association for Cancer Research Project GENIE, Project Data Sphere, the National Cancer Institute Genomic Data Commons, and the Veterans Health Administration Clinical Data Initiative. Critical factors in the development of these systems include attention to the use of robust pipelines for data aggregation, common data models, data deidentification to enable multiple uses, integration of data collection into physician workflows, terminology standardization and attention to interoperability, extensive quality assurance and quality control activity, incorporation of multiple data types, and understanding how data resources can be best applied. By describing some of the emerging resources, we hope to inspire consideration of the secondary use of such data at the earliest possible step to ensure the proper sharing of data in order to generate insights that advance the understanding and the treatment of cancer.

The impact of COVID-19 and rapid policy exemptions expanding on access to medication for opioid use disorder (MOUD): A nationwide Veterans Health Administration cohort study.

BACKGROUND: In March 2020, Veterans Health Administration (VHA) enacted policies to expand treatment for Veterans with opioid use disorder (OUD) during COVID-19. In this study, we evaluate whether COVID-19 and subsequent OUD treatment policies impacted receipt of therapy/counseling and medication for OUD (MOUD). METHODS: Using VHA's nationwide electronic health record data, we compared outcomes between a comparison cohort derived using data from prior to COVID-19 (October 2017-December 2019) and a pandemic-exposed cohort (January 2019-March 2021). Primary outcomes included receipt of therapy/counseling or any MOUD (any/none); secondary outcomes included the number of therapy/counseling sessions attended, and the average percentage of days covered (PDC) by, and months prescribed, each MOUD in a year. RESULTS: Veterans were less likely to receive therapy/counseling over time, especially post-pandemic onset, and despite substantial increases in teletherapy. The likelihood of receiving buprenorphine, methadone, and naltrexone was reduced post-pandemic onset. PDC on MOUD generally decreased over time, especially methadone PDC post-pandemic onset, whereas buprenorphine PDC was less impacted during COVID-19. The number of months prescribed methadone and buprenorphine represented relative improvements compared to prior years. We observed important disparities across Veteran demographics. CONCLUSION: Receipt of treatment was negatively impacted during the pandemic. However, there was some evidence that coverage on methadone and buprenorphine may have improved among some veterans who received them. These medication effects are consistent with expected COVID-19 treatment disruptions, while improvements regarding access to therapy/counseling via telehealth, as well as coverage on MOUD during the pandemic, are consistent with the aims of MOUD policy exemptions.

Rapid implementation of a modular clinical trial informatics solution for COVID-19 research.

Veterans Health Administration (VHA) services are most frequently used by patients 65 years and older, an age group that is disproportionally affected by COVID-19. Here we describe a modular Clinical Trial Informatics Solution (CTIS) that was rapidly developed and deployed to support a multi-hospital embedded pragmatic clinical trial in COVID-19 patients within the VHA. Our CTIS includes tools for patient eligibility screening, informed consent tracking, treatment randomization, EHR data transformation for reporting and interfaces for patient outcome and adverse event tracking. We hope our CTIS component descriptions and practical lessons learned will serve as a useful building block for others creating their own clinical trial tools and have made application and database code publicly available.

The Veterans Affairs Precision Oncology Data Repository, a Clinical, Genomic, and Imaging Research Database.

The Veterans Affairs Precision Oncology Data Repository (VA-PODR) is a large, nationwide repository of de-identified data on patients diagnosed with cancer at the Department of Veterans Affairs (VA). Data include longitudinal clinical data from the VA's nationwide electronic health record system and the VA Central Cancer Registry, targeted tumor sequencing data, and medical imaging data including computed tomography (CT) scans and pathology slides. A subset of the repository is available at the Genomic Data Commons (GDC) and The Cancer Imaging Archive (TCIA), and the full repository is available through the Veterans Precision Oncology Data Commons (VPODC). By releasing this de-identified dataset, we aim to advance Veterans' health care through enabling translational research on the Veteran population by a wide variety of researchers.

The Veterans Precision Oncology Data Commons: Transforming VA data into a national resource for research in precision oncology.

The Department of Veterans Affairs (VA) has a strong track record providing high-quality, evidence-based care to cancer patients. In order to accelerate discoveries that will further improve care for Veterans with cancer, the VA has partnered with the Center for Translational Data Science at the University of Chicago and the Open Commons Consortium to establish a data sharing platform, the Veterans Precision Oncology Data Commons (VPODC). The VPODC makes clinical, genomic, and imaging data from the VA available to the research community at large. In this paper, we detail our motivation for data sharing, describe the VPODC, and outline our collaboration model. By transforming VA data into a national resource for research in precision oncology, the VPODC seeks to foster innovation through collaboration and resource sharing that will ultimately lead to improved care for Veterans with cancer.

Machine Learning Methods to Predict Lung Cancer Survival Using the Veterans Affairs Research Precision Oncology Data Commons.

We completed a pilot study to guide the development of the VA Research Precision Oncology Data Commons infrastructure as a collaboration platform with the greater research community. Our results using a small subset of patients from the VA's Precision Oncology Program demonstrate the feasibility of our data sharing platform to build predictive models for lung cancer survival using machine learning, as well as highlight the potential of target genome sequencing data.

Differential scanning calorimetry (DSC) and temperature-modulated DSC study of three mouthguard materials.

OBJECTIVES: Employ differential scanning calorimetry (DSC) and temperature-modulated DSC (TMDSC) to investigate thermal transformations in three mouthguard materials and provide insight into their previously investigated energy absorption. METHODS: Samples (13-21mg) were obtained from (a) conventional ethylene vinyl acetate (EVA), (b) Pro-form, another EVA polymer, and (c) PolyShok, an EVA polymer containing polyurethane. Conventional DSC (n=5) was first performed from -80 to 150 degrees C at a heating rate of 10 degrees C/min to determine the temperature range for structural transformations. Subsequently, TMDSC (n=5) was performed from -20 to 150 degrees C at a heating rate of 1 degrees C/min. Onset and peak temperatures were compared using ANOVA and the Tukey-Kramer HSD test. Other samples were coated with a gold-palladium film and examined with an SEM. RESULTS: DSC and TMDSC curves were similar for both conventional EVA and Pro-form, showing two endothermic peaks suggestive of melting processes, with crystallization after the higher-temperature peak. Evidence for crystallization and the second endothermic peak were much less prominent for PolyShok, which had no peaks associated with the polyurethane constituent. The onset of the lower-temperature endothermic transformation is near body temperature. No glass transitions were observed in the materials. SEM examination revealed different surface morphology and possible cushioning effect for PolyShok, compared to Pro-form and EVA. SIGNIFICANCE: The difference in thermal behavior for PolyShok is tentatively attributed to disruption of EVA crystal formation, which may contribute to its superior impact resistance. The lower-temperature endothermic peak suggests that impact testing of these materials should be performed at 37 degrees C.

A Bayesian model for estimating multi-state disease progression.

A growing number of individuals who are considered at high risk of cancer are now routinely undergoing population screening. However, noted harms such as radiation exposure, overdiagnosis, and overtreatment underscore the need for better temporal models that predict who should be screened and at what frequency. The mean sojourn time (MST), an average duration period when a tumor can be detected by imaging but with no observable clinical symptoms, is a critical variable for formulating screening policy. Estimation of MST has been long studied using continuous Markov model (CMM) with Maximum likelihood estimation (MLE). However, a lot of traditional methods assume no observation error of the imaging data, which is unlikely and can bias the estimation of the MST. In addition, the MLE may not be stably estimated when data is sparse. Addressing these shortcomings, we present a probabilistic modeling approach for periodic cancer screening data. We first model the cancer state transition using a three state CMM model, while simultaneously considering observation error. We then jointly estimate the MST and observation error within a Bayesian framework. We also consider the inclusion of covariates to estimate individualized rates of disease progression. Our approach is demonstrated on participants who underwent chest x-ray screening in the National Lung Screening Trial (NLST) and validated using posterior predictive p-values and Pearson's chi-square test. Our model demonstrates more accurate and sensible estimates of MST in comparison to MLE.

Feasibility of Automating Patient Acuity Measurement Using a Machine Learning Algorithm.

BACKGROUND AND PURPOSE: One method of determining nurse staffing is to match patient demand for nursing care (patient acuity) with available nursing staff. This pilot study explored the feasibility of automating acuity measurement using a machine learning algorithm. METHODS: Natural language processing combined with a machine learning algorithm was used to predict acuity levels based on electronic health record data. RESULTS: The algorithm was able to predict acuity relatively well. A main challenge was discordance among nurse raters of acuity in generating a gold standard of acuity before applying the machine learning algorithm. CONCLUSIONS: This pilot study tested applying machine learning techniques to acuity measurement and yielded a moderate level of performance. Higher agreement among the gold standard may yield higher performance in future studies.

Automatic generation of repeated patient information for tailoring clinical notes.

Generating clear, readable, and accurate reports can be a time-consuming task for physicians. Clinical notes, which document patient encounters, often contain a certain set of patient information including demographics, medical history, surgical history, examination results or the current medical condition that is propagated from one clinical note to all subsequent clinical notes for the same patient. To this end, we present a system, which automatically generates this patient information for the creation of a new clinical note. We use semantic patterns and an approximate sequence matching algorithm for capturing the discourse role of sentences, which we show to be a useful feature for determining whether the sentence should be repeated. Our system is shown to perform better than a simple baseline metric using precision/recall results. We believe such a system would allow clinical notes to be more complete, timely, and accurate.

Automating the generation of lexical patterns for processing free text in clinical documents.

OBJECTIVE: Many tasks in natural language processing utilize lexical pattern-matching techniques, including information extraction (IE), negation identification, and syntactic parsing. However, it is generally difficult to derive patterns that achieve acceptable levels of recall while also remaining highly precise. MATERIALS AND METHODS: We present a multiple sequence alignment (MSA)-based technique that automatically generates patterns, thereby leveraging language usage to determine the context of words that influence a given target. MSAs capture the commonalities among word sequences and are able to reveal areas of linguistic stability and variation. In this way, MSAs provide a systemic approach to generating lexical patterns that are generalizable, which will both increase recall levels and maintain high levels of precision. RESULTS: The MSA-generated patterns exhibited consistent F1-, F.5-, and F2- scores compared to two baseline techniques for IE across four different tasks. Both baseline techniques performed well for some tasks and less well for others, but MSA was found to consistently perform at a high level for all four tasks. DISCUSSION: The performance of MSA on the four extraction tasks indicates the method's versatility. The results show that the MSA-based patterns are able to handle the extraction of individual data elements as well as relations between two concepts without the need for large amounts of manual intervention. CONCLUSION: We presented an MSA-based framework for generating lexical patterns that showed consistently high levels of both performance and recall over four different extraction tasks when compared to baseline methods.

Automatic generation of repeated patient information for tailoring clinical notes.

Dictating clear, readable, and accurate clinical notes can be a time-consuming task for physicians. Clinical notes often contain information concerning the patient's medical history and current medical condition which is propagated from one clinical note to all follow-up clinical notes for the same patient. In this paper, we present a system which, given a clinical note, automatically determines what information should be repeated, and then generates this information for the physician for a new clinical note. We use semantic patterns for capturing the rhetorical category of sentences, which we show to be useful for determining whether the sentence should be repeated. Our system is shown to perform better than a baseline metric based on precision/recall results. Such a system would allow clinical notes to be more complete, timely, and accurate.

Determining word sequence variation patterns in clinical documents using multiple sequence alignment.

Sentences and phrases that represent a certain meaning often exhibit patterns of variation where they differ from a basic structural form by one or two words. We present an algorithm that utilizes multiple sequence alignments (MSAs) to generate a representation of groups of phrases that possess the same semantic meaning but also share in common the same basic word sequence structure. The MSA enables the determination not only of the words that compose the basic word sequence, but also of the locations within the structure that exhibit variation. The algorithm can be utilized to generate patterns of text sequences that can be used as the basis for a pattern-based classifier, as a starting point to bootstrap the pattern building process for a regular expression-based classifiers, or serve to reveal the variation characteristics of sentences and phrases within a particular domain.

A methodology to integrate clinical data for the efficient assessment of brain-tumor patients.

Careful examination of the medical record of brain-tumor patients can be an overwhelming task for the neuroradiologist. The number of clinical documents alone may approach 100 for a patient that has a 3-year-old brain tumor. The neuroradiologist's evaluation of a patient's brain tumor involves examining the current imaging exam and checking for previous imaging exams that may occur pre- or post-treatment. The goal of this research is to develop an effective method to review all of the pertinent patient information from the medical record. We have designed and developed a medical system that incorporates Hospital Information Systems, Radiology Information Systems, and Picture Archiving and Communications Systems information. Our research improves clinical review of patient's data by organizing image display, removing unnecessary documents, and mining for key clinical scenarios that are important in the assessment and care of brain-tumor patients.

Generating models of surgical procedures using UMLS concepts and multiple sequence alignment.

Surgical procedures can be viewed as a process composed of a sequence of steps performed on, by, or with the patient's anatomy. This sequence is typically the pattern followed by surgeons when generating surgical report narratives for documenting surgical procedures. This paper describes a methodology for semi-automatically deriving a model of conducted surgeries, utilizing a sequence of derived Unified Medical Language System (UMLS) concepts for representing surgical procedures. A multiple sequence alignment was computed from a collection of such sequences and was used for generating the model. These models have the potential of being useful in a variety of informatics applications such as information retrieval and automatic document generation.