Development of a Machine Learning Model for Survival Risk Stratification of Patients With Advanced Oral Cancer

Tseng, Yi-Ju; Wang, Hsin-Yao; Lin, Ting Wei; Lu, Jang Jih; Hsieh, Chia Hsun; Liao, Chun Ta

doi:10.1001/jamanetworkopen.2020.11768

Cited by 58 publications

(47 citation statements)

References 43 publications

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“…In this study, we propose a novel machine learning (ML)-based predictive model called iPMI for the practical identification of PMI in women with early-stage cervical cancer who are candidates for primary radical surgery. This category of modeling technique is increasingly employed in cancer prognostic model development studies with highly reliable predictive performance [21][22][23]. To validate the effectiveness and robustness of the iPMI model developed by using the random forest (RF) method, we compared its predictive performance with those of conventional logistic regression (LR) and other widely used ML classifiers including decision tree (DT), k-nearest neighbor (kNN), multi-layer perceptron (MLP), naive Bayes (NB), support vector machine (SVM), and extreme gradient boosting (XGB).…”

Section: Introductionmentioning

confidence: 99%

iPMI: Machine Learning-Aided Identification of Parametrial Invasion in Women with Early-Stage Cervical Cancer

et al. 2021

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Radical hysterectomy is a recommended treatment for early-stage cervical cancer. However, the procedure is associated with significant morbidities resulting from the removal of the parametrium. Parametrial cancer invasion (PMI) is found in a minority of patients but the efficient system used to predict it is lacking. In this study, we develop a novel machine learning (ML)-based predictive model based on a random forest model (called iPMI) for the practical identification of PMI in women. Data of 1112 stage IA-IIA cervical cancer patients who underwent primary surgery were collected and considered as the training dataset, while data from an independent cohort of 116 consecutive patients were used as the independent test dataset. Based on these datasets, iPMI-Econ was then developed by using basic clinicopathological data available prior to surgery, while iPMI-Power was also introduced by adding pelvic node metastasis and uterine corpus invasion to the iPMI-Econ. Both 10-fold cross-validations and independent test results showed that iPMI-Power outperformed other well-known ML classifiers (e.g., logistic regression, decision tree, k-nearest neighbor, multi-layer perceptron, naive Bayes, support vector machine, and extreme gradient boosting). Upon comparison, it was found that iPMI-Power was effective and had a superior performance to other well-known ML classifiers in predicting PMI. It is anticipated that the proposed iPMI may serve as a cost-effective and rapid approach to guide important clinical decision-making.

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Section: Introductionmentioning

confidence: 99%

iPMI: Machine Learning-Aided Identification of Parametrial Invasion in Women with Early-Stage Cervical Cancer

et al. 2021

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“…Machine learning (ML) models may also be used to identify prognostic factors. Tseng et al [ 59 ] incorporated clinical, pathological and cancer-related gene features of patients with advanced oral cancer and found that only 6 of 44 genes analyzed are necessary for further prognostic risk stratification. Therefore, costs and resources for molecular analysis could be reduced with targeted requests.…”

Section: Discussionmentioning

confidence: 99%

Laboratory Demand Management Strategies—An Overview

et al. 2021

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Inappropriate laboratory test selection in the form of overutilization as well as underutilization frequently occurs despite available guidelines. There is broad approval among laboratory specialists as well as clinicians that demand management strategies are useful tools to avoid this issue. Most of these tools are based on automated algorithms or other types of machine learning. This review summarizes the available demand management strategies that may be adopted to local settings. We believe that artificial intelligence may help to further improve these available tools.

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“…Multiple ML models that risk-stratify patients with a disease or prepare patients for surgery have been recently developed and validated (16,(20)(21)(22)(23)(24)(25). These ML models have been shown to better predict mortality than conventional logistic regression after liver cancer surgery, aortic aneurysm surgery, and cardiac surgery.…”

Section: Risk Stratificationmentioning

confidence: 99%

Accessing Artificial Intelligence for Clinical Decision-Making

Giordano

Brennan

Mohamed

et al. 2021

Front. Digit. Health

127

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Advancements in computing and data from the near universal acceptance and implementation of electronic health records has been formative for the growth of personalized, automated, and immediate patient care models that were not previously possible. Artificial intelligence (AI) and its subfields of machine learning, reinforcement learning, and deep learning are well-suited to deal with such data. The authors in this paper review current applications of AI in clinical medicine and discuss the most likely future contributions that AI will provide to the healthcare industry. For instance, in response to the need to risk stratify patients, appropriately cultivated and curated data can assist decision-makers in stratifying preoperative patients into risk categories, as well as categorizing the severity of ailments and health for non-operative patients admitted to hospitals. Previous overt, traditional vital signs and laboratory values that are used to signal alarms for an acutely decompensating patient may be replaced by continuously monitoring and updating AI tools that can pick up early imperceptible patterns predicting subtle health deterioration. Furthermore, AI may help overcome challenges with multiple outcome optimization limitations or sequential decision-making protocols that limit individualized patient care. Despite these tremendously helpful advancements, the data sets that AI models train on and develop have the potential for misapplication and thereby create concerns for application bias. Subsequently, the mechanisms governing this disruptive innovation must be understood by clinical decision-makers to prevent unnecessary harm. This need will force physicians to change their educational infrastructure to facilitate understanding AI platforms, modeling, and limitations to best acclimate practice in the age of AI. By performing a thorough narrative review, this paper examines these specific AI applications, limitations, and requisites while reviewing a few examples of major data sets that are being cultivated and curated in the US.

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Development of a Machine Learning Model for Survival Risk Stratification of Patients With Advanced Oral Cancer

Cited by 58 publications

References 43 publications

iPMI: Machine Learning-Aided Identification of Parametrial Invasion in Women with Early-Stage Cervical Cancer

iPMI: Machine Learning-Aided Identification of Parametrial Invasion in Women with Early-Stage Cervical Cancer

Laboratory Demand Management Strategies—An Overview

Accessing Artificial Intelligence for Clinical Decision-Making

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