Automated Recommendation for Cervical Cancer Screening and Surveillance

Wagholikar, Kavishwar B.; MacLaughlin, Kathy L.; Casey, Petra M.; Kastner, Thomas M.; Henry, Michael R.; Hankey, Ronald A.; Peters, Steve G.; Greenes, Robert A.; Chute, Christopher G.; Liu, Hongfang; Chaudhry, Rajeev

doi:10.4137/cin.s14035

Cited by 6 publications

(10 citation statements)

References 31 publications

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“…al. [44] used a decision tree to create an automated clinical decision support (CDS) system, in which Natural Language Processing identified the parameters from cervical pathology and clinical notes. Again, Nuranna et.…”

Section: Plos Onementioning

confidence: 99%

Digital health interventions for cervical cancer care: A systematic review and future research opportunities

Razzak,

Islam,

Aadeeb

et al. 2023

PLoS ONE

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Background Cervical cancer is a malignancy among women worldwide, which is responsible for innumerable deaths every year. The primary objective of this review study is to offer a comprehensive and synthesized overview of the existing literature concerning digital interventions in cervical cancer care. As such, we aim to uncover prevalent research gaps and highlight prospective avenues for future investigations. Methods This study adopted a Systematic Literature Review (SLR) methodology where a total of 26 articles were reviewed from an initial set of 1110 articles following an inclusion-exclusion criterion. Results The review highlights a deficiency in existing studies that address awareness dissemination, screening facilitation, and treatment provision for cervical cancer. The review also reveals future research opportunities like explore innovative approaches using emerging technologies to enhance awareness campaigns and treatment accessibility, consider diverse study contexts, develop sophisticated machine learning models for screening, incorporate additional features in machine learning research, investigate the impact of treatments across different stages of cervical cancer, and create more user-friendly applications for cervical cancer care. Conclusions The findings of this study can contribute to mitigating the adverse effects of cervical cancer and improving patient outcomes. It also highlights the untapped potential of Artificial Intelligence and Machine Learning, which could significantly impact our society.

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Section: Plos Onementioning

confidence: 99%

Digital health interventions for cervical cancer care: A systematic review and future research opportunities

Razzak,

Islam,

Aadeeb

et al. 2023

PLoS ONE

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“…In earlier works, 13 14 15 16 Wagholikar et al described the implementation of a CDSS for Cervical Cancer Screening and Surveillance (CCSS). The system utilized natural language processing (NLP) to extract variables about cervical cancer screening from cytology reports generated after Pap smear screening and pathology reports from colposcopy biopsies, which are predominantly available in text format.…”

Section: Background and Significancementioning

confidence: 99%

Improving the Accuracy of a Clinical Decision Support System for Cervical Cancer Screening and Surveillance

Ravikumar¹,

MacLaughlin²,

Scheitel³

et al. 2018

Appl Clin Inform

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Keywords► clinical decision support system ► ambulatory care information systems ► testing and evaluation of health information technology ► electronic health records ► knowledge delivery ► knowledge management AbstractBackground Clinical decision support systems (CDSS) for cervical cancer prevention are generally limited to identifying patients who are overdue for their next routine/next screening, and they do not provide recommendations for follow-up of abnormal results. We previously developed a CDSS to automatically provide follow-up recommendations based on the American Society of Colposcopy and Cervical Pathology (ASCCP) guidelines for women with both previously normal and abnormal test results leveraging information available in the electronic medical record (EMR). Objective Enhance the CDSS by improving its accuracy and incorporating changes to reflect the latest revision of the guidelines. Methods After making enhancements to the CDSS, we evaluated the performance of the clinical recommendations on 393 patients selected through stratified sampling from a set of 3,704 patients in a nonclinical setting. We performed chart review of individual patient's record to evaluate the performance of the system. An expert clinician assisted by a resident manually reviewed the recommendation made by the system and verified whether the recommendations were as per the ASCCP guidelines.Results The recommendation accuracy of the enhanced CDSS improved to 93%, which is a substantial improvement over the 84% reported previously. A detailed analysis of errors is presented in this article. We fixed the errors identified in this evaluation that were amenable to correction to further improve the accuracy of the system. The source code of the updated CDSS is available at https://github.com/ohnlp/ MayoNlpPapCdss. Conclusion We made substantial enhancements to our earlier prototype CDSS with the updated ASCCP guidelines and performed a thorough evaluation in a nonclinical setting to improve the accuracy of the CDSS. The CDSS will be further refined as it is utilized in the practice.

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“…It cannot confuse one patient for another, it cannot combine medical records of more than one patient to make a decision, it eliminates medical errors in the absence of human errors, it will not be exhausted or overwhelm to produce a wrong decision, it cannot base its decisions on feelings or instincts, or on the appearance of the patient, it has so many positive outlook and it is facilitating conditions (incorporating workflow, training and integration), ease of use and trust in the knowledge base 89 . In another study testing the impact of CDSS, providers without CDSS assistance needed an average of 1 minute 39 seconds to decide on recommendations for management of abnormal findings 90 . Another study showed that CDSS has the potential to decrease malpractice payments because of its known benefits for quality and safety 91 .…”

Section: Advantages Of Clinical Decision Support Systemsmentioning

confidence: 99%

Clinical decision support system as a risk assessment tool to aid in earlier diagnosis of pancreatic cancer

Adams

Mital

Mehta

2017

IJMEI

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Introduction to the Problem…………………………………………………….…1 1.2 Background and Statement of the Problem……………………………….….…..8 1.3 Significance of the Study…………………………………………………….……13 1.4 Research Goals and Objectives....……………………….………………….…….15 1.5 Research Hypotheses…………………………………………….…………….…..16

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Automated Recommendation for Cervical Cancer Screening and Surveillance

Cited by 6 publications

References 31 publications

Digital health interventions for cervical cancer care: A systematic review and future research opportunities

Digital health interventions for cervical cancer care: A systematic review and future research opportunities

Improving the Accuracy of a Clinical Decision Support System for Cervical Cancer Screening and Surveillance

Clinical decision support system as a risk assessment tool to aid in earlier diagnosis of pancreatic cancer

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