Matching patients to clinical trials using semantically enriched document representation

Hassanzadeh, Hamed; Karimi, Sarvnaz; Nguyen, Anthony

doi:10.1016/j.jbi.2020.103406

Cited by 32 publications

(20 citation statements)

References 35 publications

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“…Study outcomes can be predicted using AI models [ 71 ] which could significantly lower costs of drug development. AI has been used to identify patients for clinical trials [ 72 ] by incorporating inclusion and exclusion criteria to search EHR and identify eligible patients, hence facilitating participant accrual. These systems have shown high accuracy while only requiring a fifth of the time used by manual review [ 73 ].…”

Section: Artificial Intelligence For Cancer Researchmentioning

confidence: 99%

An Overview of Artificial Intelligence in Oncology

et al. 2022

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Cancer is associated with significant morbimortality globally. Advances in screening, diagnosis, management and survivorship were substantial in the last decades, however, challenges in providing personalized and data-oriented care remain. Artificial intelligence (AI), a branch of computer science used for predictions and automation, has emerged as potential solution to improve the healthcare journey and to promote precision in healthcare. AI applications in oncology include, but are not limited to, optimization of cancer research, improvement of clinical practice (eg., prediction of the association of multiple parameters and outcomes – prognosis and response) and better understanding of tumor molecular biology. In this review, we examine the current state of AI in oncology, including fundamentals, current applications, limitations and future perspectives.

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Section: Artificial Intelligence For Cancer Researchmentioning

confidence: 99%

An Overview of Artificial Intelligence in Oncology

et al. 2022

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“…Data-driven ML methods may provide a path forward to mitigating under-representation in clinical trials through automation of subject selection. Researchers have used ML to identify patients eligible for clinical trials from EHR data, through matching of patient characteristics with trial criteria [106,183,287,288] or existing trial participants [176]. Others have sought to identify patients who are most likely to agree to participate in a trial, for more efficient use of recruitment resources [182,255].…”

Section: Evaluation Of Eligibilitymentioning

confidence: 99%

“…Nevertheless, ML has been proposed to assist with clinical trials planning, participant management, data collection, and analysis [268]. Deep learning models have been developed for patienttrial matching, using electronic health records (EHR) data and trial eligibility criteria to recommend suitable patients for certain trials [106,288]. Such systems have performed well in practice; a study found that the IBM Watson for Clinical Trial matching system, used to match breast cancer patients with systemic therapy trials, increased average monthly enrollment by 80% over the 18 months following implementation [101].…”

Section: Introductionmentioning

confidence: 99%

Multi-disciplinary fairness considerations in machine learning for clinical trials

Chien¹,

Deliu²,

Turner³

et al. 2022

Preprint

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While interest in the application of machine learning to improve healthcare has grown tremendously in recent years, a number of barriers prevent deployment in medical practice. A notable concern is the potential to exacerbate entrenched biases and existing health disparities in society. The area of fairness in machine learning seeks to address these issues of equity; however, appropriate approaches are context-dependent, necessitating domain-specific consideration. We focus on clinical trials, i.e., research studies conducted on humans to evaluate medical treatments. Clinical trials are a relatively under-explored application in machine learning for healthcare, in part due to complex ethical, legal, and regulatory requirements and high costs. Our aim is to provide a multi-disciplinary assessment of how fairness for machine learning fits into the context of clinical trials research and practice. We start by reviewing the current ethical considerations and guidelines for clinical trials and examine their relationship with common definitions of fairness in machine learning. We examine potential sources of unfairness in clinical trials, providing concrete examples, and discuss the role machine learning might play in either mitigating potential biases or exacerbating them when applied without care. Particular focus is given to adaptive clinical trials, which may employ machine learning. Finally, we highlight concepts that require further investigation and development, and emphasize new approaches to fairness that may be relevant to the design of clinical trials.

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“…Matching complicated eligibility criteria to potential subjects is a tedious, laborintensive, and difficult task [131]. To automate this, Hassanzadeh et al [132] used natural language processing and a Multi-Layer Perceptron model to extract meaningful information from patient records to help collate evidence for better decision making on the eligibility of patients according to certain inclusion and exclusion criteria,. It achieved an overall micro-F1 score of 84%.…”

Section: In Cancer Researchmentioning

confidence: 99%

Artificial intelligence for assisting cancer diagnosis and treatment in the era of precision medicine

Chen

Lin

et al. 2021

Cancer Communications

123

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Over the past decade, artificial intelligence (AI) has contributed substantially to the resolution of various medical problems, including cancer. Deep learning (DL), a subfield of AI, is characterized by its ability to perform automated feature extraction and has great power in the assimilation and evaluation of large amounts of complicated data. On the basis of a large quantity of medical data and novel computational technologies, AI, especially DL, has been applied in various aspects of oncology research and has the potential to enhance cancer diagnosis and treatment. These applications range from early cancer detection, diagnosis, classification and grading, molecular characterization of tumors, prediction of patient outcomes and treatment responses, personalized treatment, automatic radiotherapy workflows, novel anti‐cancer drug discovery, and clinical trials. In this review, we introduced the general principle of AI, summarized major areas of its application for cancer diagnosis and treatment, and discussed its future directions and remaining challenges. As the adoption of AI in clinical use is increasing, we anticipate the arrival of AI‐powered cancer care.

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Matching patients to clinical trials using semantically enriched document representation

Cited by 32 publications

References 35 publications

An Overview of Artificial Intelligence in Oncology

An Overview of Artificial Intelligence in Oncology

Multi-disciplinary fairness considerations in machine learning for clinical trials

Artificial intelligence for assisting cancer diagnosis and treatment in the era of precision medicine

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