bluuebunny/crossref_metadata_2025_split huggingface dataset:
Time savings from integrating discrete, genomic data into the electronic health record using the Epic Aura Network.
Authors: Posteraro Leo , Mergler Patrick , Urbanek Dennis , Schulin Joseff , Anton Howard , Kokkinos Nancy , Chen James Lin 
Published: June 2023

e13568 Background: The capability to deliver, order, and receive discrete genomics data from external pathology laboratories into the electronic health record (EHR) has improved dramatically. As opposed to unstructured PDF integrations, discrete genomic results allow physicians real-time receipt of results which may trigger rapid clinical actions and expedite patient care. However, these genomic integrations, traditionally point-to-point (P2P), can be time consuming. On the other hand, Epic’s order and results network (Aura) connects pathology labs directly to Epic, which has pre-existing connections to Epic EHR sites. This eliminates the need to create a new connection, and is designed to provide a faster and simpler approach. Tempus was the first vendor to use the Epic genomics module for delivery of discrete, somatic mutational data and now is also the first company to leverage Aura for this purpose. Here, we report on our early Aura integration experiences. Methods: Using the Tempus Database, we retrospectively analyzed medical systems that were all Epic customers and determined their EHR genomic integration time using Aura. Integration time was defined as the time between initial start date of creating “orderables” to time of site testing. The number of EHR medical center analysts was also quantified. We used our prior P2P EHR deployments as a historical control. Results: Six bidirectional Tempus genomic integrations using Aura were performed from July 2022 to December 2022. All medical centers were expansive, multisite hospital systems. Mean number of integrated departments per hospital was 249 (range: 19-699). In 17 P2P integrations prior to Aura, mean integration time was 6.7m (range: 4m-18m). In contrast, mean integration time using Aura over the 6 sites was 1.5m (range: 0.75m-2m), which is a 78% improvement. No correlation was noted between the integration time and number of EHR analysts staffed or departments integrated. The two slowest integration sites (2m and 2.5m) had competing EHR deployment priorities. Conclusions: Using Aura, the speed of Tempus EHR integration was improved by 78% for bidirectional, discrete genomic data. Dedicated EHR integration resourcing reduced deployment times to as low as 3 weeks. This reduction in integration time may further the adoption of genomic EHR integrations and improve patient care by expediting time to appropriate, targeted treatments.
Electronic health record (EHR) and genomics-based machine learning (ML) to predict therapeutic effectiveness among patients with hormone-receptor positive (HR)+/HER2- advanced breast cancer (aBC).
Authors: Hathi Deep K. , Pisano Steven , Honarvar Hossein , Parikh Ravi Bharat , Das Rahul K. 
Published: June 2023

e13574 Background: Patients (pts) with HR+/HER2- aBC may eventually become resistant to endocrine therapies and CDK4/6 inhibitors (CDK4/6i). ML algorithms on EHR linked with NGS data may enable more accurate predictions of therapeutic resistance and identify clinicogenomic (CG) risk factors. Methods: HR+/HER2- aBC pts treated between Jan 2015 – Dec 2022 with at least one NGS test report before line end were identified in ConcertAI’s Genome360 oncology database, consisting of molecular alterations abstracted from NGS reports linked with deeply curated US-based EHR. XGBoost (XGB) and Cox proportional hazards (CoxPH) models were trained to predict real-world progression-free survival (rwPFS) derived from curated tumor progression (TP), as assessed by the providers based on clinical observations, pathology results, and radiologic evidence. Data was split into 60:20:20 for training/validation/test. Pts in the 1st and 4th quartiles of predicted risk from XGB model were defined as low- and high-risk. Predictors were identified from Shapley Additive exPlanations (SHAP)-derived marginal hazard ratios (HR), derived in the 180 days prior to index date, and included demographics, labs, vitals, comorbidities, concomitant medications, procedures, tumor properties, and molecular alterations as features. Results: Among 624 pts (CDK4/6i treated = 519) with median age = 62.5 years and 10.5% black, 46.8% developed TP. The test cumulative dynamic ROC-AUC in the best-performing XGB model was 0.68. The precision at 180 days (%TP = 27) was 40%. The 180-day cumulative incidence of TP in low- and high-risk groups was 11.6% vs 37.7%. Higher line number, lower hemoglobin (HGB), higher alkaline phosphatase (ALP), presence of liver metastasis, and history of thoracic radiation were clinical risk factors. Genomic risk factors were FGF aberrations, alterations in TP53 and in genes within the MAPK, cell cycle, and ESR pathways, and co-alterations in the PIK3CA+TP53, and MAPK+ESR pathways. ALP-by-TP53 interaction was found. ALP level was a risk factor in TP53-wildtype (wt) but not in TP53-mutant (mut) pts. Conclusions: ML on EHR linked with NGS data enables identification of high-risk pts and multimodal predictors of CDK4/6i resistance among women with HR+/HER2- aBC, identifying both known and undescribed interactions between clinical and genomic risk factors as well as co-alteration risk factors. [Table: see text]
Exchanging genomics reports between pathology labs and medical centers using the Minimal Clinical Oncology Data Element (mCODE) FHIR implementation guide.
Authors: Terry May , Anton Howard , Casagni Michelle , Chamala Srikar , Chen James Lin 
Published: June 2023

e13575 Background: The automated transfer of discrete molecular data from pathology labs into the electronic health record (EHR) is rapidly becoming commonplace and is designed to facilitate clinical decision making. Simple genomic changes are represented in existing EHR data transfer standards, but current data standards lack clinical fields that correspond to treatment insights such as associated medications and potential clinical trials. To address this, the mCODE effort defines a consensus oncology data standard that facilitates lightweight transmission of data from patients with cancer. mCODE’s support of genomic data leverages efforts by the HL7 Clinical Genomics Working Group (HL7 CGWG) focusing on oncology. Here, we describe pilot efforts involving both a pathology partner and academic medical centers to extend mCODE FHIR (Fast Healthcare Interoperability Resources) implementation guide (IG) support of genomic data for data interchange and clinical decision support use cases. Methods: From 2021-2022, validation of the mCODE FHIR IG genomic data elements was performed in CodeX, a FHIR accelerator program. These elements encompassed: lab facility, test order information, pathologist, patient demographics, specimen, molecular studies and therapeutic implications. Genomic FHIR data elements were selected for their conciseness and clinical relevance for inclusion in the mCODE FHIR IG under the supervision of two oncology informaticians. As part of the mCODE FHIR IG pilot, synthetic solid tumor molecular patient reports were generated by Tempus Labs. These synthetic molecular reports were transformed into mCODE-conformant FHIR files for transfer. The mCODE IG can be found at http://hl7.org/fhir/us/mcode/ . Results: Over 50 Tempus data elements were analyzed and found to be readily mapped to mCODE genomics elements. Two categories of data elements required additional transformation: 1. Data elements requiring additional genomic database annotations; 2. FHIR syntax constraints like the concatenation of medications in the same drug class. The mCODE file was transmitted to Vanderbilt and the Children's Hospital LA from Tempus for decoding and downstream uses. This effort further informed the HL7 CGWG of needed IG clarifications for compatibility with other standardization efforts and genomic use cases. Conclusions: mCODE’s support of genomic data elements provides a lightweight genomics interchange standard compatible with an existing pathology lab report. Mapping from existing molecular pathology reports to mCODE required nominal alterations. Furthermore, mCODE’s alignment with HL7 genomics efforts ensure FHIR-based compatibility in other clinical domains and with developing international standards. Continued pathology lab and academic medical center collaboration is essential for mCODE refinement and adoption.
Analyzing free-text clinical narratives for veterans with lymphoid malignancies using natural language processing (NLP).
Authors: He Lu , Moldenhauer Matthew , Zheng Kai , Ma Helen 
Published: June 2023

e13576 Background: Free-text clinical narratives contain rich patient information, which is labor-intensive to extract through chart review. We developed an NLP pipeline to enable automatic extraction of performance status (PS), staging, and diagnosis from clinical narratives from Veterans Affairs (VA) patients with lymphoid malignancies (LM). Methods: The rule-based NLP algorithm was developed and iteratively refined using a development corpus of 287 notes independently annotated by two clinicians. The F1-score for PS was 95.8 (precision 98.6, recall 93.2), 92.7 for staging (precision 94.0, recall 81.6), and 67 (precision 80.2, recall 57.9) for diagnosis. The NLP pipeline was then externally validated using an evaluation corpus of 97 notes from another group of 100 veterans with T-cell LM. Results: The results are reported. In the 97 notes, primary diagnosis was most routinely documented, with 2.76 mentions per note. In comparison, staging was most sparsely documented with only 34 mentions (note that 11 patients with large granular lymphocytic leukemia were not staged). The NLP pipeline performed relatively well in extracting PS and staging (F1-scores were 0.74 and 0.72, respectively). It also achieved high precision in extracting diagnosis information (precision 0.93). However, recall (0.44) for diagnosis was poor, likely due to the complexities and inconsistencies of how diagnoses are documented for LM. Frequency of documentation and performance in the external validation set. Conclusions: The pipeline shows promising performance on the external validation set, demonstrating the feasibility of using NLP to extract information from notes of patients with LM for clinical research. The NLP pipeline generally has lower recall than precision, indicating that the pipeline may miss clinical information that should be captured. FPs incorrectly capture entities that are easily confused with the clinical entities of interest, such as nutritional status versus performance status. Future work includes capturing more lexical variations and indicators of documentation, as well as contextual information, such as in which sections of notes elements are likely documented. In addition, we describe how diagnosis may exist as primary, secondary, and in a differential, and we are building an NLP-based classifier to distinguish between these types of diagnoses. We will use results from the rule-based NLP pipeline as labels to fine-tune transformer-based, weak-supervised models to further enhance the performance. [Table: see text]
Explainable AI and machine learning algorithms to predict treatment failures for patients with cancer.
Authors: Farooq Muddassar , Shahid Muhammad Usamah 
Published: June 2023

e13577 Background: Cancer patients may undergo lengthy and painful chemotherapy treatments, comprising several successive regimens or plans. Treatment inefficacy and other adverse events can lead to discontinuation (or failure) of these plans, or prematurely changing them, which results in a significant amount of physical, financial, and emotional toxicity to the patients and their families. In this research work, we build AI driven treatment failure models that utilize the real-world evidence gathered from patients’ profiles available in an oncology EMR/EHR system, with a goal of predicting the likelihood of a plan being discontinued at the time of its prescription. The selected AI models achieve a prediction accuracy of more than 80% and also provide reasons for their inference. Methods: Inclusion and Exclusion Criteria: Deidentified and anonymized electronic health records of patients, with their prescribed chemotherapies, for five different primary cancer diagnoses - ICD10 codes C18, C34, C50, C61 and C90 - that have the highest plan discontinuation rates between the years 2015 and 2022 are analyzed. All patients of other cancer types are excluded. AI Models: Unique features, that influence the treatment failure, for each cancer type are engineered by using therapeutic classification of drugs, diagnoses codes, comorbidity scores, tumor and biomarker information that is extracted from the notes and lab tests. We only use features that are available at the time of selecting a treatment plan. Several machine learning classifiers are investigated, and three tree ensembles - random forests, Xgboost and boosted forests - are further evaluated on the validation set to fine tune learning parameters with an objective to reduce the complexity of decision trees for providing better interpretability without significantly compromising the accuracy. Results: Our pilot studies reveal that boosted forests comprising of 5 random forests, each with 5 trees of depth 10 offer the best compromise between performance and interpretability. The models once trained are evaluated on unseen datasets and four performance measures of AI models are reported. On average, 15 rules are autonomously generated for a treatment failure inference for each cancer type and generally 6 of them have a significant support of 30 samples or greater. Conclusions: Machine learning algorithms for predicting treatment efficacy of chemotherapy regimens by deriving inference from the patients’ EMR/EHR data is an emerging yet challenging research domain. Our studies demonstrate that AI models like boosted forests provide the optimal models for treatment failure use case. In future, we want to validate the system in controlled clinical trials with the help of oncologists. [Table: see text]
Deep learning-based ensemble model using hematoxylin and eosin (H&E) whole slide images (WSIs) for the prediction of MET mutations in non-small cell lung cancer (NSCLC).
Authors: Choi Yoon-La , Park Sehhoon , Jung Hyun Ae , Sun Jong-Mu , Lee Se-Hoon , Ahn Jin Seok , Ahn Myung-Ju , Lee Taebum , Shin Seunghwan , Park Jongchan , Ma Minuk , Pereira Sergio , Park Gahee , Kim Seulki , Ro Juneyoung , Jung Wonkyung , Yoo Donggeun , Ock Chan-Young 
Published: June 2023

e13578 Background: The MET proto-oncogene is involved in tumorigenesis and progression and has emerged as an attractive targetable genomic alteration in NSCLC. However, it has not been actively tested in NSCLC due to the lack of a cost-effective screening tool. We developed a deep-learning based model to predict genomic profiles from H&E WSI and applied it to MET pathologic mutations in NSCLC. The prediction model is based on an ensemble model trained not solely on H&E images but also on multi-layered semantic contents that were produced by a pre-trained AI analyzer, Lunit SCOPE IO. Methods: The model was trained on the MET aberration (exon 14 skipping, n=191; pathogenic mutation, n=109) and paired wild-type dataset (n=500). To extract deep features from raw H&E images, a self-supervised vision transformer was used, and an AI-based pathology profiling analyzer extracted semantic contents such as the spatial information of tumor cells, lymphocytes, cancer epithelium, and cancer stroma. A set of classifiers was trained based on the two features, and the ensemble of these features was used to improve robustness. Following cross-validation, the model was applied to an independent clinical dataset with MET sequencing results from The Cancer Genome Atlas (TCGA) LUAD and LUSC datasets (n=914), Samsung Medical Center (SMC, n=361), and Chonnam National University Hospital (CNUH, n=54). Results: The best cross-validation performances of the models predicting MET aberration measured by mean area under the receiver operating characteristic curve (AUROC) were 0.772 when trained by only H&E images (HE-only), 0.788 by AI semantic content with MLP classifier (AISC-MLP), and 0.803 by AISC with random forest (AI-RF), respectively. An ensemble of the three models showed an increased AUROC of 0.837 in the training dataset by cross-validation. These models were applied to the external validation dataset (n=1,329), including 20 (1.5%) MET exon 14 skipping and known pathologic mutations. The mean AUROC to predict MET aberration by the ensemble model was 0.817 with 95% sensitivity, 64.7% specificity. The AUROC of TCGA, SMC, and CNUH cohorts were 0.815, 0.802, and 0.812, respectively. Conclusions: An AI-based ensemble model combining H&E images with semantic contents extracted from pre-developed AI models significantly improved the accuracy and robustness of MET pathogenic mutation prediction using an H&E sample in NSCLC. These findings allow for cost-effective screening for MET alterations.
Use of an artificial intelligence (AI) –based pre-screening tool for patients with bladder cancer with fibroblast growth factor receptor (FGFR) alteration.
Authors: Shi Guobang , Yu Yanfei , Wang Aodi , Liu Ye , Shi Xiaoliang 
Published: June 2023

e13580 Background: Over 25% of urothelial carcinoma have actionable genetic alterations in FGFR. The clinical trial of the pan-FGFR inhibitor requires thousands of patients with FGFR alterations to be screened; however, the cost is prohibitively expensive. A deep learning model can help predict the occurrence of specific genetic alterations and effectively screen patients with FGFR actionable alterations in bladder cancer. Methods: The prediction model of FGFR oncogenic alterations was trained by using 440 H&E-stained bladder cancer diagnostic slides (from 373 patients) from the OrigiMed Database and validated with 448 slides (from 376 patients) from the additional TCGA-BLCA dataset. The model employs an attention-based, weakly supervised learning method to identify tissue subregions most likely to have the mutations of FGFR1-4, so as to classify the entire slide. Every patch is assigned an attention score, which represents the corresponding contribution to slide classification. The slide-level aggregation rule of attention-based pooling computes a final attention score by weighted average. Results: The FGFR oncogenic alteration predictive AI (FGFRonaP) model was trained, validated and tested on the OrigiMed real-world cohort. The FGFRonaP model could identify patients with FGFR1-4 gene oncogenic aberration on the test set accurately (Precision = 60.0%, Recall = 69.2%, AUROC = 0.839). When the model was tested on the additional TCGA independent cohort, 11 negative samples were included mistakenly while 323 true negative slide and 19 true positive slides were identified correctly. The FGFRonaP model showed an acceptable classification precision (63.3%), much higher than the occurrence rate (26.9%) of FGFR oncogenic alterations, in spite of an imperfect AUROC (0.636) and recall (0.167). Conclusions: FGFRonaP is feasible to serve as an in-house or monocentric application to pre-screen the potential FGFR-altered patients. It could be useful and efficient in preliminary screening to reduce workloads of genetic testing, and thus help pharmaceutical companies improve the proportion of candidates with FGFR-alterations in all patients who undergo genetic testing and cut expensive costs for enrolling patients.
Application of natural language processing to assess the performance status documentation quality metric in patients with non–small-cell lung cancer.
Authors: Maghsoudi Arash , Sada Yvonne H. , Zhu Huili , Yarlagadda Sudha , Guffey Danielle , Nowakowski Sara , Li Ang , Razjouyan Javad 
Published: June 2023

e13582 Background: Performance status (PS) assessment is important for cancer prognosis and treatment decision-making. PS documentation for stage IV non-small cell lung cancer (NSCLC) is a quality measure for ASCO Quality Oncology Practice Initiative (QOPI) Certification Program. We used natural language processing (NLP) to extract PS from unstructured electronic medical records (EMR) clinical notes in patients with metastatic NSCLC to verify PS documentation compliance and disparities in PS documentation. Methods: We identified oncology progress notes of stage IV NSCLC patients from Harris Health System, which is an integrated comprehensive healthcare system for underserved patients in Houston, TX. All progress notes within 60 days of first oncology clinic visit date were filtered based on visit type and author type. A rule-based regular expression NLP algorithm was used to extract the PS, namely Eastern Standard Cooperative Group (ECOG) or Karnofsky Performance Status (KPS). A total of 400 oncology notes were randomly selected from 400 distinct patients and annotated by two clinician reviewers, and discrepancies were resolved by a senior clinician judge. The annotated notes were split randomly into 200 training notes and 200 validation notes, and the performance of the NLP algorithm was evaluated. We used logistic regression to analyze potential disparity in PS documentation. Results: We identified 15,239 progress notes from 757 stage IV NSCLC patients. The NLP algorithm accuracy validated on the test data was 99% with corresponding positive predictive values (PPVs) for ECOG PS 0, 1, 2, 3, 4 of 100%, 100%, 95%, 94%, 94%, respectively. The NLP algorithm identified 85% of patients with appropriate PS documentation (sensitivity 100%, specificity 91%, positive predictive value 98%, negative predictive value 100%). Logistic regression analyses showed no significant differences in association between PS documentation and race/ethnicity, language, or sex. Conclusions: We developed an internally validated NLP algorithm to extract PS from EMR clinical notes. Despite potential language barriers, the QOPI metric of >75% PS documentation was met in our hospital system without significant disparities in clinical care. [Table: see text]