scholarly journals A rule-based approach to identify patient eligibility criteria for clinical trials from narrative longitudinal records

JAMIA Open ◽  
2019 ◽  
Vol 2 (4) ◽  
pp. 521-527
Author(s):  
George Karystianis ◽  
Oscar Florez-Vargas ◽  
Tony Butler ◽  
Goran Nenadic
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Eligibility Criteria ◽  
Rule Based ◽  
Time Period ◽  
Mining Methods ◽  
Artery Disease ◽  
Rule Based Approach ◽  
Clinical Records ◽  
Advanced Coronary Artery Disease

Abstract Objective Achieving unbiased recognition of eligible patients for clinical trials from their narrative longitudinal clinical records can be time consuming. We describe and evaluate a knowledge-driven method that identifies whether a patient meets a selected set of 13 eligibility clinical trial criteria from their longitudinal clinical records, which was one of the tasks of the 2018 National NLP Clinical Challenges. Materials and Methods The approach developed uses rules combined with manually crafted dictionaries that characterize the domain. The rules are based on common syntactical patterns observed in text indicating or describing explicitly a criterion. Certain criteria were classified as “met” only when they occurred within a designated time period prior to the most recent narrative of a patient record and were dealt through their position in text. Results The system was applied to an evaluation set of 86 unseen clinical records and achieved a microaverage F1-score of 89.1% (with a micro F1-score of 87.0% and 91.2% for the patients that met and did not meet the criteria, respectively). Most criteria returned reliable results (drug abuse, 92.5%; Hba1c, 91.3%) while few (eg, advanced coronary artery disease, 72.0%; myocardial infarction within 6 months of the most recent narrative, 47.5%) proved challenging enough. Conclusion Overall, the results are encouraging and indicate that automated text mining methods can be used to process clinical records to recognize whether a patient meets a set of clinical trial criteria and could be leveraged to reduce the workload of humans screening patients for trials.

A Framework for Systematic Assessment of Clinical Trial Population Representativeness Using Electronic Health Records Data

2021 ◽  
Vol 12 (04) ◽  
pp. 816-825
Author(s):  
Yingcheng Sun ◽  
Alex Butler ◽  
Ibrahim Diallo ◽  
Jae Hyun Kim ◽  
Casey Ta ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Electronic Health Records ◽  
The United States ◽  
Design Stage ◽  
Common Data Model ◽  
Free Text ◽  
Eligibility Criteria ◽  
Health Records ◽  
Electronic Health

Abstract Background Clinical trials are the gold standard for generating robust medical evidence, but clinical trial results often raise generalizability concerns, which can be attributed to the lack of population representativeness. The electronic health records (EHRs) data are useful for estimating the population representativeness of clinical trial study population. Objectives This research aims to estimate the population representativeness of clinical trials systematically using EHR data during the early design stage. Methods We present an end-to-end analytical framework for transforming free-text clinical trial eligibility criteria into executable database queries conformant with the Observational Medical Outcomes Partnership Common Data Model and for systematically quantifying the population representativeness for each clinical trial. Results We calculated the population representativeness of 782 novel coronavirus disease 2019 (COVID-19) trials and 3,827 type 2 diabetes mellitus (T2DM) trials in the United States respectively using this framework. With the use of overly restrictive eligibility criteria, 85.7% of the COVID-19 trials and 30.1% of T2DM trials had poor population representativeness. Conclusion This research demonstrates the potential of using the EHR data to assess the clinical trials population representativeness, providing data-driven metrics to inform the selection and optimization of eligibility criteria.

scholarly journals Are clinical trial eligibility criteria an accurate reflection of a real-world population of advanced non-small-cell lung cancer patients?

2018 ◽  
Vol 25 (4) ◽  
Author(s):  
K. Al-Baimani ◽  
H. Jonker ◽  
T. Zhang ◽  
G.D. Goss ◽  
S.A. Laurie ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Clinical Trial ◽  
Clinical Trials ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Advanced Nsclc ◽  
Small Cell ◽  
Small Cell Lung ◽  
Eligibility Criteria ◽  
Ecog Ps

Background Advanced non-small-cell lung cancer (nsclc) represents a major health issue globally. Systemic treatment decisions are informed by clinical trials, which, over years, have improved the survival of patients with advanced nsclc. The applicability of clinical trial results to the broad lung cancer population is unclear because strict eligibility criteria in trials generally select for optimal patients.Methods We performed a retrospective chart review of all consecutive patients with advanced nsclc seen in outpatient consultation at our academic institution between September 2009 and September 2012, collecting data about patient demographics and cancer characteristics, treatment, and survival from hospital and pharmacy records. Two sets of arbitrary trial eligibility criteria were applied to the cohort. Scenario A stipulated Eastern Cooperative Oncology Group performance status (ecog ps) 0–1, no brain metastasis, creatinine less than 120 μmol/L, and no second malignancy. Less-strict scenario B stipulated ecog ps 0–2 and creatinine less than 120 μmol/L. We then used the two scenarios to analyze treatment and survival of patients by trial eligibility status.Results The 528 included patients had a median age of 67 years, with 55% being men and 58% having adenocarcinoma. Of those 528 patients, 291 received at least 1 line of palliative systemic therapy. Using the scenario A eligibility criteria, 73% were trial-ineligible. However, 46% of “ineligible” patients actually received therapy and experienced survival similar to that of the “eligible” treated patients (10.2 months vs. 11.6 months, p = 0.10). Using the scenario B criteria, only 35% were ineligible, but again, the survival of treated patients was similar in the ineligible and eligible groups (10.1 months vs. 10.9 months, p = 0.57).Conclusions Current trial eligibility criteria are often strict and limit the enrolment of patients in clinical trials. Our results suggest that, depending on the chosen drug, its toxicities and tolerability, eligibility criteria could be carefully reviewed and relaxed.

scholarly journals PATH-03. CLINICAL UTILITY OF NEXT GENERATION SEQUENCING IN IDH-WILDTYPE GLIOBLASTOMA: THE DANA-FARBER CANCER INSTITUTE EXPERIENCE

2020 ◽  
Vol 22 (Supplement_2) ◽  
pp. ii164-ii164
Author(s):  
Mary Jane Lim-Fat ◽  
Gilbert Youssef ◽  
Mehdi Touat ◽  
Bryan Iorgulescu ◽  
Eleanor Woodward ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Next Generation Sequencing ◽  
Immune Checkpoint Blockade ◽  
Next Generation ◽  
Single Nucleotide Variants ◽  
Dana Farber Cancer Institute ◽  
Clinical Records ◽  
Ngs Data ◽  
Generation Sequencing

Abstract BACKGROUND Comprehensive next generation sequencing (NGS) is available through many academic institutions and commercial entities, and is incorporated in practice guidelines for glioblastoma (GBM). We retrospective evaluated the practice patterns and utility of incorporating NGS data into routine care of GBM patients at a clinical trials-focused academic center. METHODS We identified 1,011 consecutive adult patients with histologically confirmed GBM with OncoPanel testing, a targeted exome NGS platform of 447 cancer-associated genes at Dana Farber Cancer Institute (DFCI), from 2013-2019. We selected and retrospectively reviewed clinical records of all IDH-wildtype GBM patients treated at DFCI. RESULTS We identified 557 GBM IDH-wildtype patients, of which 227 were male (40.7%). OncoPanel testing revealed 833 single nucleotide variants and indels in 44 therapeutically relevant genes (Tier 1 or 2 mutations) including PIK3CA (n=51), BRAF (n=9), FGFR1 (n=8), MSH2 (n=4), MSH6 (n=2) and MLH1 (n=1). Copy number analysis revealed 509 alterations in 18 therapeutically relevant genes including EGFR amplification (n= 186), PDGFRA amplification (N=39) and CDKN2A/2B homozygous loss (N=223). Median overall survival was 17.5 months for the whole cohort. Seventy-four therapeutic clinical trials accrued 144 patients in the upfront setting (25.9%) and 203 patients (36.4%) at recurrence. Altogether, NGS data for 107 patients (19.2%) were utilized for clinical trial enrollment or targeted therapy indications. High mutational burden (>17mutations/Mb) was identified in 11/464 samples (2.4%); of whom 3/11 received immune checkpoint blockade. Four patients received compassionate use therapy targeting EGFRvIII (rindopepimut, n=2), CKD4/6 (abemaciclib, n=1) and BRAFV600E (dabrafenib/trametinib, n=1). CONCLUSION While NGS has greatly improved diagnosis and molecular classification, we highlight that NGS remains underutilized in selecting therapy in GBM, even in a setting where clinical trials and off-label therapies are relatively accessible. Continued efforts to develop better targeted therapies and efficient clinical trial design are required to maximize the potential benefits of genomically-stratified data.

scholarly journals Safety and Effectiveness of Favipiravir for Novel Coronavirus (COVID-19): A Rapid Review of Available Evidence

2021 ◽  
Author(s):  
Mohammadreza Mobinizadeh ◽  
Morteza Arab-Zozani
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Therapeutic Potential ◽  
Data Extraction ◽  
Rapid Review ◽  
Eligibility Criteria ◽  
Clinical Trial Registration ◽  
Registration System ◽  
Novel Coronavirus ◽  
First Time

Context: Coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) appeared for the first time in December 2019 in Wuhan, China. Due to the lack of unified and integrated evidence for Favipiravir, this study was conducted to rapidly review the existing evidence to help evidence-based decision-making on the therapeutic potential of this drug in the treatment of COVID-19 patients. Evidence Acquisition: This study is a rapid Health Technology Assessment (HTA). By searching pertinent databases, the research team collected relevant articles and tried to create a policy guide through a thematic approach. This rapid review was done in four steps: (1) Searching for evidence through databases; (2) screening the evidence considering eligibility criteria; (3) data extraction; and (4) analyzing the data through thematic analysis. Results: After applying the inclusion criteria, four studies were finally found, including three review studies and a clinical trial that was temporarily removed by its publisher from the journal’s website. After searching the sources mentioned in the articles, two ongoing clinical trials were found in China. Also, by searching the clinical trial website, www.clinicaltrials.gov, five clinical trials were found in the search. The result of the search in the clinical trial registration system in Iran showed a study that is in the process of patient recruitment. A limited number of other articles were found, mostly in the form of reflections from physicians or researchers and letters to editors who have predicted the drug’s performance on SARS-CoV-2, which needs further clinical study to be approved. Conclusions: With the available evidence, it is not possible to make a definite conclusion about the safety and efficacy of Favipiravir in the treatment of patients with COVID-19.

scholarly journals 2166

2017 ◽  
Vol 1 (S1) ◽  
pp. 12-12
Author(s):  
Jianyin Shao ◽  
Ram Gouripeddi ◽  
Julio C. Facelli
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Power Distribution ◽  
Large Scale ◽  
Search Space ◽  
Free Text ◽  
Eligibility Criteria ◽  
Clinical Notes ◽  
Semantic Concepts ◽  
Mimic Iii

OBJECTIVES/SPECIFIC AIMS: This poster presents a detailed characterization of the distribution of semantic concepts used in the text describing eligibility criteria of clinical trials reported to ClincalTrials.gov and patient notes from MIMIC-III. The final goal of this study is to find a minimal set of semantic concepts that can describe clinical trials and patients for efficient computational matching of clinical trial descriptions to potential participants at large scale. METHODS/STUDY POPULATION: We downloaded the free text describing the eligibility criteria of all clinical trials reported to ClinicalTrials.gov as of July 28, 2015, ~195,000 trials and ~2,000,000 clinical notes from MIMIC-III. Using MetaMap 2014 we extracted UMLS concepts (CUIs) from the collected text. We calculated the frequency of presence of the semantic concepts in the texts describing the clinical trials eligibility criteria and patient notes. RESULTS/ANTICIPATED RESULTS: The results show a classical power distribution, Y=210X(−2.043), R2=0.9599, for clinical trial eligibility criteria and Y=513X(−2.684), R2=0.9477 for MIMIC patient notes, where Y represents the number of documents in which a concept appears and X is the cardinal order the concept ordered from more to less frequent. From this distribution, it is possible to realize that from the over, 100,000 concepts in UMLS, there are only ~60,000 and 50,000 concepts that appear in less than 10 clinical trial eligibility descriptions and MIMIC-III patient clinical notes, respectively. This indicates that it would be possible to describe clinical trials and patient notes with a relatively small number of concepts, making the search space for matching patients to clinical trials a relatively small sub-space of the overall UMLS search space. DISCUSSION/SIGNIFICANCE OF IMPACT: Our results showing that the concepts used to describe clinical trial eligibility criteria and patient clinical notes follow a power distribution can lead to tractable computational approaches to automatically match patients to clinical trials at large scale by considerably reducing the search space. While automatic patient matching is not the panacea for improving clinical trial recruitment, better low cost computational preselection processes can allow the limited human resources assigned to patient recruitment to be redirected to the most promising targets for recruitment.

Cancer clinical trials available in the community setting: A 5-year analysis from 1999–2004

2006 ◽  
Vol 24 (18_suppl) ◽  
pp. 6056-6056
Author(s):  
J. K. Keller ◽  
J. Bowman ◽  
J. A. Lee ◽  
M. A. Mathiason ◽  
K. A. Frisby ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Cancer Patients ◽  
Community Setting ◽  
Disease Stage ◽  
Cancer Center ◽  
Newly Diagnosed ◽  
Community Based ◽  
Eligibility Criteria ◽  
Major Barrier

6056 Background: Less than 5% of newly diagnosed cancer patients are accrued into clinical trials. In the community setting, the lack of appropriate clinical trials is a major barrier. Our prospective study in 2004 determined that 58% of newly diagnosed adult cancer patients at our community-based cancer center didn’t have a clinical trial available appropriate for their disease stage. Among those with clinical trials, 23% were subsequently found to be ineligible (Go RS, et al. Cancer 2006, in press). However, the availability of clinical trials may vary from year to year. Methods: A retrospective study was conducted to determine what clinical trials were available for newly diagnosed adult cancer patients at our institution from June 1999-July 2004. The study also investigated the proportions of newly diagnosed patients who had a clinical trial available appropriate for type and stage of disease and patients accrued. Results: Over the 5-year period, 207 (82, 87, 99, 102, 117, years 1–5, respectively) trials were available. Most (50.7%) trials were for the following cancers: breast (15.5%), lung (13.5%), head and neck (7.7%), colorectal (7.2%) and lymphoma (6.8%). ECOG (53%), RTOG (26%), and CTSU (9%) provided the majority of the trials. A total of 5,776 new adult cancer patients were seen during this period. Overall, 60% of the patients had a trial available appropriate for type and stage of their cancer, but only 103 (3%) were enrolled. There was a significant upward trend in the proportions of patients with available trials over the years (60.2%, 55.9%, 59.2%, 60.7%, 63.9%, years 1–5, respectively; Mantel-Haenszel P=.008). The proportion of patients with a trial available was highest for prostate (97.3%), lung (90.9%), and breast (73.9%), and lowest for melanoma (17.1%), renal (11.6%), and bladder (7.2%). The majority of patients accrued to trials had the following cancers: breast (32%), lung (17%), lymphoma (9%), colon (7%), and prostate (5%). Conclusions: Nearly half of the newly diagnosed adult patients at our center had no trials available appropriate for type and stage of their cancers. It is likely that if strict clinical trial eligibility criteria were applied, approximately 2/3 of our patients would not be eligible for a clinical trial. No significant financial relationships to disclose.

Barriers to clinical trial accrual: Clinical trialists' perspectives.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. e18156-e18156
Author(s):  
Edward S. Kim ◽  
Dax Kurbegov ◽  
Patricia A. Hurley ◽  
David Michael Waterhouse
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Cost Benefit ◽  
Trial Participation ◽  
Clinical Trial Participation ◽  
Eligibility Criteria ◽  
Contract Research Organization ◽  
The Public ◽  
Community Forum ◽  
Clinical Trial Accrual

e18156 Background: Oncology clinical trial participation rates remain at historic lows. There are many barriers that impede participation. Understanding those barriers, from the perspective of cancer clinical trialists, will help develop solutions to increase physician and site engagement, with the goal of improving accrual rates and advancing cancer treatment. Methods: Physician investigators and research staff from community-based and academic-based research sites were surveyed during ASCO’s Research Community Forum (RCF) Annual Meeting (N = 159) and through a pre-meeting survey (N = 124) in 2018. Findings and potential solutions were discussed during the meeting. Results: 84% of respondents (n = 84) reported that it took 6-8 months to open a trial and 86% (n = 81) reported that trials had unnecessary delays 70% of the time. The top 10 barriers to accrual identified were: insufficient staffing resources, restrictive eligibility criteria, physician buy-in, site access to trials, burdensome regulatory requirements, difficulty identifying patients, lack of suitable trials, sponsor and contract research organization requirements, patient barriers, and site cost-benefit. Respondents shared strategies to address these barriers. Conclusions: The current state of conducting clinical trials is not sustainable and hinders clinical trial participation. New strategies are needed to ensure patients and practices have access to trials, standardize and streamline processes, reduce inefficiencies, simplify trial activation, reduce regulatory burden, provide sufficient compensation to sites, engage the community and patients, educate the public, and increase collaborations. The ASCO RCF offers resources, available to the public, that offer practical strategies to overcome barriers to clinical trial accrual and has ongoing efforts to facilitate oncology practice participation in clinical trials.

scholarly journals Trial Prospector: Matching Patients with Cancer Research Studies Using an Automated and Scalable Approach

2014 ◽  
Vol 13 ◽  
pp. CIN.S19454 ◽  
Author(s):  
Satya S. Sahoo ◽  
Shiqiang Tao ◽  
Andrew Parchman ◽  
Zhihui Luo ◽  
Licong Cui ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Survival Rates ◽  
Comprehensive Cancer Center ◽  
Cancer Center ◽  
Dramatic Improvement ◽  
Multiple Sources ◽  
Eligibility Criteria ◽  
Physician Participation ◽  
Time Required

Cancer is responsible for approximately 7.6 million deaths per year worldwide. A 2012 survey in the United Kingdom found dramatic improvement in survival rates for childhood cancer because of increased participation in clinical trials. Unfortunately, overall patient participation in cancer clinical studies is low. A key logistical barrier to patient and physician participation is the time required for identification of appropriate clinical trials for individual patients. We introduce the Trial Prospector tool that supports end-to-end management of cancer clinical trial recruitment workflow with (a) structured entry of trial eligibility criteria, (b) automated extraction of patient data from multiple sources, (c) a scalable matching algorithm, and (d) interactive user interface (UI) for physicians with both matching results and a detailed explanation of causes for ineligibility of available trials. We report the results from deployment of Trial Prospector at the National Cancer Institute (NCI)-designated Case Comprehensive Cancer Center (Case CCC) with 1,367 clinical trial eligibility evaluations performed with 100% accuracy.

Barriers and facilitators to oncology clinical trial accrual: Comparing perceptions of community and academic oncologists.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. e18131-e18131
Author(s):  
Andrew R. Wong ◽  
Arti Hurria ◽  
Virginia Sun ◽  
Daneng Li ◽  
Kevin George ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Barriers And Facilitators ◽  
Structured Interviews ◽  
Eligibility Criteria ◽  
Patients With Cancer ◽  
Oncology Clinical Trial ◽  
Medical Oncologists ◽  
Academic Practices ◽  
Clinical Trial Accrual

e18131 Background: Multiple studies have described the barriers and facilitators to oncology clinical trial accrual in academic practices. However, few studies have been done in community settings, even though the majority of patients with cancer receive their care in the community. We examined and compared community and academic oncologists’ perceptions of the barriers and facilitators to cancer clinical trial accrual. Methods: Semi-structured interviews were conducted from March to June 2018 with 44 medical oncologists at City of Hope (24 in academia; 20 in community sites). Purposive sampling was used to ensure participant diversity. Primary measures were oncologists’ self-reported perceptions of the barriers and facilitators to clinical trial accrual. Responses were recorded digitally, transcribed, and de-identified. Data was managed using NVivo v12. Two analysts coded the interview data using thematic content analysis (kappa = 0.74). A third analyst adjudicated discrepancies. Results: Of the 44 participants, 36% were women, and 68% had > 10 years of experience. Compared to academic oncologists, community oncologists more often cited barriers due to the lack of protocols suitable for community patients’ histology and stage (13% vs. 6%) and insufficient trial personnel support (13% vs. 9%). Compared to community oncologists, academic oncologists more often cited barriers due to limited time (14% vs. 8%) and overly stringent eligibility criteria (14% vs. 9%). Community oncologists more commonly reported extrinsic facilitators (e.g. reminders of available protocols from trial support staff) (91% vs. 76%) while academic oncologists more commonly reported intrinsic facilitators for offering clinical trials (e.g. self-motivation to prioritize clinical trials) (24% vs. 9%). Conclusions: Community oncologists more often reported facing barriers to accrual due to limited suitable trials and insufficient personnel support compared to academic oncologists. Additionally, community oncologists cite the need for more infrastructure to support accrual. Interventions to increase trial accrual must be tailored to address the unique needs of both community and academic practices.

Phenotyping of clinical trial eligibility text from cancer studies into computable criteria in electronic health records.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. 6592-6592
Author(s):  
Yun Mai ◽  
Kyeryoung Lee ◽  
Zongzhi Liu ◽  
Meng Ma ◽  
Christopher Gilman ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Clinical Trial ◽  
Clinical Trials ◽  
Knowledge Base ◽  
Small Cell ◽  
Rater Agreement ◽  
Small Cell Lung ◽  
Eligibility Criteria ◽  
Health Records ◽  
Cancer Studies

6592 Background: Clinical trial phenotyping is the process of extracting clinical features and patient characteristics from eligibility criteria. Phenotyping is a crucial step that precedes automated cohort identification from patient electronic health records (EHRs) against trial criteria. We establish a clinical trial phenotyping pipeline to transform clinical trial eligibility criteria into computable criteria and enable high throughput cohort selection in EHRs. Methods: Formalized clinical trial criteria attributes were acquired from a natural-language processing (NLP)-assisted approach. We implemented a clinical trial phenotyping pipeline that included three components: First, a rule-based knowledge engineering component was introduced to annotate the trial attributes into a computable and customizable granularity from EHRs. The second component involved normalizing annotated attributes using standard terminologies and pre-defined reference tables. Third, a knowledge base of computable criteria attributes was built to match patients to clinical trials. We evaluated the pipeline performance by independent manual review. The inter-rater agreement of the annotation was measured on a random sample of the knowledge base. The accuracy of the pipeline was evaluated on a subset of randomly selected matched patients for a subset of randomly selected attributes. Results: Our pipeline phenotyped 2954 clinical trials from five cancer types including Non-Small Cell Lung Cancer, Small Cell Lung Cancer, Prostate Cancer, Breast Cancer, and Multiple Myeloma. We built a knowledge base of 256 computable attributes that included comorbidities, comorbidity-related treatment, previous lines of therapy, laboratory tests, and performance such as ECOG and Karnofsky score. Among 256 attributes, 132 attributes were encoded using standard terminologies and 124 attributes were normalized to customized concepts. The inter-rater agreement of the annotation measured by Cohen’s Kappa coefficient was 0.83. We applied the knowledge base to our EHRs and efficiently identified 33258 potential subjects for cancer clinical trials. Our evaluation on the patient matching indicated the F1 score was 0.94. Conclusions: We established a clinical trial phenotyping pipeline and built a knowledge base of computable criteria attributes that enabled efficient screening of EHRs for patients meeting clinical trial eligibility criteria, providing an automated way to efficiently and accurately identify clinical trial cohorts. The application of this knowledge base to patient matching from EHR data across different institutes demonstrates its generalization capability. Taken together, this knowledge base will be particularly valuable in computer-assisted clinical trial subject selection and clinical trial protocol design in cancer studies based on real-world evidence.

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