Development and Application of a Machine Learning Approach to Assess Short-term Mortality Risk Among Patients With Cancer Starting Chemotherapy

Elfiky, Aymen; Pany, Maximilian J.; Parikh, Ravi B.; Obermeyer, Ziad

doi:10.1001/jamanetworkopen.2018.0926

Cited by 114 publications

(101 citation statements)

References 46 publications

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“…Many success have been shown in the areas of image recognition/classification and computer vision by CNN, and natural language processing (NLP) and sequencing data investigation by RNN. Specifically, great performance has also been witnessed in many medical areas, including classification of skin cancer types [72,73], identification of pathological histological slides [74], identification of Aβ plague region in Alzheimer's patients, classification of cancer cells from normal cells using nuclear morphometric measure [75], and extraction information from electronic health records (EHR) to predict hospital readmission [76,77], mortality [78], and clinical outcome [79]. In cancer prognosis studies, CNN has been applied to the classification of cancerous tissue for survival prediction or extraction of feature for downstream prognosis.…”

Section: Cnn-based Modelsmentioning

confidence: 99%

“…Deep learning has made significant improvement in research and started to make changes in our daily lives. In the medical field, many studies have applied deep learning and shown many great successes [78,[113][114][115][116][117][118][119][120][121]. One advantage of using deep learning to train a model is its capability to continue training when more data is available [27].…”

Section: Conclusion and Summarymentioning

confidence: 99%

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The Application of Deep Learning in Cancer Prognosis Prediction

Zhu

Xie

Han

et al. 2020

Cancers

242

160

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Deep learning has been applied to many areas in health care, including imaging diagnosis, digital pathology, prediction of hospital admission, drug design, classification of cancer and stromal cells, doctor assistance, etc. Cancer prognosis is to estimate the fate of cancer, probabilities of cancer recurrence and progression, and to provide survival estimation to the patients. The accuracy of cancer prognosis prediction will greatly benefit clinical management of cancer patients. The improvement of biomedical translational research and the application of advanced statistical analysis and machine learning methods are the driving forces to improve cancer prognosis prediction. Recent years, there is a significant increase of computational power and rapid advancement in the technology of artificial intelligence, particularly in deep learning. In addition, the cost reduction in large scale next-generation sequencing, and the availability of such data through open source databases (e.g., TCGA and GEO databases) offer us opportunities to possibly build more powerful and accurate models to predict cancer prognosis more accurately. In this review, we reviewed the most recent published works that used deep learning to build models for cancer prognosis prediction. Deep learning has been suggested to be a more generic model, requires less data engineering, and achieves more accurate prediction when working with large amounts of data. The application of deep learning in cancer prognosis has been shown to be equivalent or better than current approaches, such as Cox-PH. With the burst of multi-omics data, including genomics data, transcriptomics data and clinical information in cancer studies, we believe that deep learning would potentially improve cancer prognosis.

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Section: Cnn-based Modelsmentioning

confidence: 99%

Section: Conclusion and Summarymentioning

confidence: 99%

The Application of Deep Learning in Cancer Prognosis Prediction

Zhu

Xie

Han

et al. 2020

Cancers

242

160

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show abstract

“…Recently, artificial intelligence (AI) in the medical field has become a research hotpot, and holds the promise to automatically diagnose heterogeneous diseases with high accuracy [ [13] , [14] , [15] , [16] , [17] , [18] ]. In our previous work [ 19 ], four machine learning (ML) algorithms were successfully employed to construct and validate a quantitative histomorphometry to identify gastric cancer patients with high risk of recurrence.…”

Section: Introductionmentioning

confidence: 99%

Driverless artificial intelligence framework for the identification of malignant pleural effusion

Tian

Huang

et al. 2021

Translational Oncology

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Our study aimed to explore the applicability of deep learning and machine learning techniques to distinguish MPE from BPE. We initially used a retrospective cohort with 726 PE patients to train and test the predictive performances of the driverless artificial intelligence (AI), and then stacked with a deep learning and five machine learning models, namely gradient boosting machine (GBM), extreme gradient boosting (XGBoost), extremely randomized trees (XRT), distributed random forest (DRF), and generalized linear models (GLM). Furthermore, a prospective cohort with 172 PE patients was applied to detect the external validity of the predictive models. The area under the curve (AUC) in the training, test and validation set were deep learning (0.995, 0.848, 0.917), GBM (0.981, 0.910, 0.951), XGBoost (0.933, 0.916, 0.935), XRT (0.927, 0.909, 0.963), DRF (0.906, 0.809, 0.969), and GLM (0.898, 0.866, 0.892), respectively. Although the Deep Learning model had the highest AUC in the training set (AUC = 0.995), GBM demonstrated stable and high predictive efficiency in three data sets. The final AI model by stacked ensemble yielded optimal diagnostic performance with AUC of 0.991, 0.912 and 0.953 in the training, test and validation sets, respectively. Using the driverless AI framework based on the routinely collected clinical data could significantly improve diagnostic performance in distinguishing MPE from BPE.

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“…[ 4 ] AI applications to healthcare data now include disease diagnosis, [ 5,6 ] improved clinical prognostics, and mortality prediction. [ 7–16 ]…”

Section: Trends In Artificial Intelligence and Deep Learningmentioning

confidence: 99%

Increasing Complexity to Simplify Clinical Care: High Resolution Mass Spectrometry as an Enabler of AI Guided Clinical and Therapeutic Monitoring

Yee

Drum

2020

Advanced Therapeutics

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Clinical care increasingly relies on individualized, highly multidimensional data for patient phenotyping. This review focusses on two technologies which are contributing to this trend and, in their mutual overlap, may provide a novel and powerful contribution to future clinical care. Artificial intelligence (AI), in particular the technique of deep learning, is steadily making inroads to clinical care and has demonstrated the ability to extract clinically relevant features from complex datasets. Likewise, mass spectrometry (MS), in particular high-resolution MS, is gaining the ability to provide highly multidimensional datasets from patient samples with the quality necessary for clinical decision making. The convergence of these trends has resulted in the nascent application of deep learning analytics to high resolution mass spectra. Due to the large volumes of content provided by high resolution mass spectra and the ability of multilayer neural networks to extract subtle features from complex datasets, future integration of their mutual application is identified as a research area of high priority for clinical care. Factors are identified that have contributed to historical progress for each technology and recommendations are provided to accelerate the mutual application of deep learning and mass spectrometry techniques to patient care.

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Development and Application of a Machine Learning Approach to Assess Short-term Mortality Risk Among Patients With Cancer Starting Chemotherapy

Abstract: This cohort study describes and applies a machine learning model to predict short-term mortality in a general oncology cohort of patients starting new chemotherapy, using only data available before the first day of treatment.

Cited by 114 publications

References 46 publications

The Application of Deep Learning in Cancer Prognosis Prediction

The Application of Deep Learning in Cancer Prognosis Prediction

Driverless artificial intelligence framework for the identification of malignant pleural effusion

Increasing Complexity to Simplify Clinical Care: High Resolution Mass Spectrometry as an Enabler of AI Guided Clinical and Therapeutic Monitoring

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