Publications by authors named "Jeremy L Warner"

Background: Initial insights into oncology clinical trial outcomes are often gleaned manually from conference abstracts. We aimed to develop an automated system to extract safety and efficacy information from study abstracts with high precision and fine granularity, transforming them into computable data for timely clinical decision-making.

Methods: We collected clinical trial abstracts from key conferences and PubMed (2012-2023).

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Article Synopsis
  • People diagnosed with cancer and their caregivers are overwhelmed by a massive amount of complex information due to advancements in cancer diagnostics and treatments.
  • The commentary highlights both the opportunities to improve cancer care and the challenges posed by managing and understanding this large volume of data.
  • It emphasizes the importance of integrating this information effectively into everyday cancer care to benefit patients and their support systems.
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Introduction: In the last decade, melanoma treatment has improved significantly. However, data on population-level treatment utilization and survival trends among older patients is limited. This study aimed to analyze trends in systemic anticancer therapy (Rx), including the uptake of immune checkpoint inhibitors (ICIs), in conjunction with trends in cause-specific survival among older patients (66+) diagnosed with advanced melanoma (2008-2019).

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In clinical practice medications are often interchanged in treatment protocols when a patient negatively reacts to their first line of therapy. Although switching between medications is common, clinicians often lack structured guidance when choosing the initial dose and frequency of a new medication, given the former with respect to risk of adverse events. In this paper we propose to establish this dose toxo-equivalence relationship using published clinical trial results with one or both drugs of interest via a Bayesian meta-analysis model that accounts for both within- and between-study variances.

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Purpose: Immunocompromised individuals, such as those diagnosed with cancer, are at a significantly higher risk for severe illness and mortality when infected with SARS-CoV-2 (COVID-19) than the general population. Two oral antiviral treatments are approved for COVID-19: Paxlovid® (nirmatrelvir/ritonavir) and Lagevrio® (molnupiravir). There is a paucity of data regarding the benefit from these antivirals among immunocompromised patients with cancer, and recent studies have questioned their efficacy among vaccinated patients, even those with risk factors for severe COVID-19.

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Purpose: The RECIST guidelines provide a standardized approach for evaluating the response of cancer to treatment, allowing for consistent comparison of treatment efficacy across different therapies and patients. However, collecting such information from electronic health records manually can be extremely labor-intensive and time-consuming because of the complexity and volume of clinical notes. The aim of this study is to apply natural language processing (NLP) techniques to automate this process, minimizing manual data collection efforts, and improving the consistency and reliability of the results.

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  • Patient portal messages, particularly related to specific health issues like breast cancer, are gaining attention in research and highlight the need for effective natural language processing techniques; however, traditional models like word2vec often require extensive data that isn't available for these messages.
  • A new model called PK-word2vec has been developed to better analyze these smaller-scale patient messages by integrating prior knowledge of medical and non-medical terms to enhance its performance.
  • The evaluation showed that PK-word2vec outperformed standard word2vec in over 90% of tasks, suggesting it can effectively capture the semantic relationships within limited datasets of patient messages.
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Background: Initial insights into oncology clinical trial outcomes are often gleaned manually from conference abstracts. We aimed to develop an automated system to extract safety and efficacy information from study abstracts with high precision and fine granularity, transforming them into computable data for timely clinical decision-making.

Methods: We collected clinical trial abstracts from key conferences and PubMed (2012-2023).

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Importance: Proton beam therapy is an emerging radiotherapy treatment for patients with cancer that may produce similar outcomes as traditional photon-based therapy for many cancers while delivering lower amounts of toxic radiation to surrounding tissue. Geographic proximity to a proton facility is a critical component of ensuring equitable access both for indicated diagnoses and ongoing clinical trials.

Objective: To characterize the distribution of proton facilities in the US, quantify drive-time access for the population, and investigate the likelihood of long commutes for certain population subgroups.

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Introduction:: Patients with thoracic cancers have one of the highest mortality rates among patients with cancer and COVID-19. Data evaluating the impact of recent anti-cancer therapies on COVID-19 outcomes in patients with thoracic cancers are confined to heterogenous studies with limited follow-up data. We leveraged data from the COVID-19 and Cancer Consortium (CCC19) (NCT04354701) to analyze the impact of recent anti-cancer therapies on the clinical outcomes of COVID-19 in patients with thoracic cancers.

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  • - PheWAS (Phenome-wide association studies) analyze the link between genetic factors and various diseases using data from DNA biobanks and electronic medical records, typically applying Phecodes as outcome measures and logistic regression for analysis.
  • - Due to inaccuracies in clinical diagnoses within electronic medical records, creating accurate lists of cases and controls becomes challenging, leading to biased odds ratio estimates and requiring a costly curation process.
  • - The proposed solution is to estimate relative risks (RR) instead, which is shown to be unbiased without needing exclusion criteria lists, allowing for more efficient and larger-scale analyses using structured phenotypic information from ICD codes rather than Phecodes.
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  • COVID-19 severely impacts cancer patients, particularly those with metastatic cancer, and this study explored the relationship between cancer metastasis and COVID-19 outcomes.
  • Using data from the CCC19 registry, researchers analyzed over 10,000 cancer patients with COVID-19 to assess hospitalization rates and mortality based on the presence and site of metastasis.
  • Findings showed that patients with metastases had higher hospitalization rates (59% vs. 49%) and 30-day mortality (18% vs. 9%), with those having lung metastases experiencing the highest risk of death in that timeframe.
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Purpose: Immunocompromised individuals, such as those diagnosed with cancer, are at a significantly higher risk for severe illness and mortality when infected with SARS-CoV-2 (COVID-19) than the general population. Two oral antiviral treatments are approved for COVID-19: Paxlovid (nirmatrelvir/ritonavir) and Lagevrio (molnupiravir). There is a paucity of data regarding the benefit from these antivirals among immunocompromised patients with cancer, and recent studies have questioned their efficacy among vaccinated patients, even those with risk factors for severe COVID-19.

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Unlabelled: Peritoneal metastases (PM) are common in metastatic colorectal cancer (mCRC). We aimed to characterize patients with mCRC and PM from a clinical and molecular perspective using the American Association of Cancer Research Genomics Evidence Neoplasia Information Exchange (GENIE) Biopharma Collaborative (BPC) registry. Patients' tumor samples underwent targeted next-generation sequencing.

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Treatment patterns in systemic anticancer therapy (SACT) are extremely varied and complex. While professional society guidelines exist that suggest recommended treatment strategies, these guidelines are produced through an extremely laborious and sometimes opaque manual process, making it impossible for such guidelines to cover all relevant treatment scenarios. To complement these manually curated guidelines, we leveraged a database of 5818 clinical trials and 7012 supporting references from 1943-present to calculate a quantifiable "relevance score".

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  • Telehealth involves delivering health care in real time through technology, connecting people or information from different locations.
  • The COVID-19 pandemic accelerated the adoption of telehealth, making it essential for managing chronic conditions like cancer, which patients couldn't receive in person.
  • This review aims to discuss how emerging technologies can innovate telehealth for cancer patients, especially those from underserved or historically excluded communities.
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Background And Purpose: Adverse neurological effects after cancer therapy are common, but biomarkers to diagnose, monitor, or risk stratify patients are still not validated or used clinically. An accessible imaging method, such as fluorodeoxyglucose positron emission tomography (FDG PET) of the brain, could meet this gap and serve as a biomarker for functional brain changes. We utilized FDG PET to evaluate which brain regions are most susceptible to altered glucose metabolism after chemoradiation in patients with head and neck cancer (HNCa).

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Objective: To summarize significant research contributions on cancer informatics published in 2022.

Methods: An extensive search using PubMed/MEDLINE was conducted to identify the scientific contributions published in 2022 that address topics in cancer informatics. The selection process comprised three steps: (i) ten candidate best papers were first selected by the two section editors, (ii) external reviewers from internationally renowned research teams reviewed each candidate best paper, and (iii) the final selection of three best papers was conducted by the editorial board of the Yearbook.

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Purpose: Manual extraction of case details from patient records for cancer surveillance is a resource-intensive task. Natural Language Processing (NLP) techniques have been proposed for automating the identification of key details in clinical notes. Our goal was to develop NLP application programming interfaces (APIs) for integration into cancer registry data abstraction tools in a computer-assisted abstraction setting.

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Purpose: AML accounts for 80% of acute leukemia in adults. While progress has been made in treating younger patients in the past 2 decades, there has been limited improvement for older patients until recently. This study examines the global and European Union (EU) 15+ trends in AML between 1990 and 2019.

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Objective: The diversity of nomenclature and naming strategies makes therapeutic terminology difficult to manage and harmonize. As the number and complexity of available therapeutic ontologies continues to increase, the need for harmonized cross-resource mappings is becoming increasingly apparent. This study creates harmonized concept mappings that enable the linking together of like-concepts despite source-dependent differences in data structure or semantic representation.

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Motivation: Phecodes are widely used and easily adapted phenotypes based on International Classification of Diseases codes. The current version of phecodes (v1.2) was designed primarily to study common/complex diseases diagnosed in adults; however, there are numerous limitations in the codes and their structure.

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