Structured deep embedding model to generate composite clinical indices from electronic health records for early detection of pancreatic cancer

Park, Jiheum; Artin, Michael G.; Lee, Eunah; Park, Michael; Hur, Chin; Tatonetti, Nicholas P.

doi:10.1016/j.patter.2022.100636

Cited by 3 publications

(5 citation statements)

References 28 publications

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“…Salvatore et al grouped relevant International Classification of Diseases, Tenth Revision (ICD-10) codes into clinically relevant phenotypically related aggregates, “phecodes.” Using co-occurrence analysis, they identified that digestive and neoplasm phecodes were strong predictors of PC (44). Park et al utilized SHapley Additive exPlanations values to identify that kidney, liver function, diabetes, red blood cell, and white blood cell groups contributed the most in predicting PC risk from laboratory results (47). Jia et al (46) ranked features by univariate C-index to identify the independent contributors to PC risk prediction.…”

Section: Resultsmentioning

confidence: 99%

“…We observed that 6 articles did not provide any information about the prediction time window or data exclusion time intervals (20,26,28,31,36,41). Only 12 studies experimented with 1 month-5 years of data exclusion time intervals (21,29,30,33,35,40,(42)(43)(44)(45)(46)(47). The C-index for the models without a curated set of predictors and 1 year lead time or exclusion time interval ranged from 0.71 to 0.83 for internal validations and 0.60-0.78 for external validations.…”

Section: Model Development and Evaluationmentioning

confidence: 99%

“…Six studies that did not rely on a curated set of predictors (42)(43)(44)(45)(46)(47) identified novel risk factors utilizing X-AI techniques (see Supplementary Table 1, http://links.lww.com/AJG/D287). Chen et al (43) utilized XGBoost gains to identify that pancreatic disorders (noncancerous and not relating to diabetes mellitus) were the most important model predictor.…”

Section: Identifying Novel Risk Factors Of Pcmentioning

confidence: 99%

“…If 2 or more models were developed in a study, risk of bias for the best performing model (highest C-index) was assessed using PROBAST. Models from only 4 studies had low risk of bias (33,42,46,47). Supplementary Digital Content (see Supplementary Table 3, http://links.lww.com/AJG/D289) presents a summary of the PROBAST risk of bias and applicability assessment.…”

Section: Risk Of Bias Assessmentmentioning

confidence: 99%

See 3 more Smart Citations

Machine Learning Models for Pancreatic Cancer Risk Prediction Using Electronic Health Record Data—A Systematic Review and Assessment

Mishra,

Chong,

Arunachalam

et al. 2024

Am J Gastroenterol

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Introduction: Accurate risk prediction can facilitate screening and early detection of pancreatic cancer (PC). We conducted a systematic review to critically evaluate effectiveness of machine learning (ML) and artificial intelligence (AI) techniques applied to Electronic Health Records (EHR) for PC risk prediction. Methods: Ovid MEDLINE(R), Ovid EMBASE, Ovid Cochrane Central Register of Controlled Trials, Ovid Cochrane Database of Systematic Reviews, Scopus, and Web of Science were searched for articles that utilized ML/AI techniques to predict PC, published between January 1st, 2012 to February 1st, 2024. Study selection and data extraction were conducted by two independent reviewers. Critical appraisal and data extraction was performed using CHARMS checklist. Risk of bias and applicability was examined using PROBAST. Results: Thirty studies including 169,149 PC cases were identified. Logistic regression was the most frequent modeling method. Twenty studies utilized a curated set of known PC risk predictors or those identified by clinical experts. ML model discrimination performance (C-index) ranged from 0.57 to 1.0. Missing data was underreported, and most studies did not implement explainable-AI techniques or report exclusion time intervals. Discussion: AI/ML models for PC risk prediction using known risk factors perform reasonably well and may have near-term applications in identifying cohorts for targeted PC screening if validated in real-world data sets. The combined use of structured and unstructured EHR data using emerging language models while incorporating explainable-AI techniques has the potential to identify novel PC risk factors and this approach merits further study.

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Section: Resultsmentioning

confidence: 99%

Section: Model Development and Evaluationmentioning

confidence: 99%

Section: Identifying Novel Risk Factors Of Pcmentioning

confidence: 99%

Section: Risk Of Bias Assessmentmentioning

confidence: 99%

See 2 more Smart Citations

Machine Learning Models for Pancreatic Cancer Risk Prediction Using Electronic Health Record Data—A Systematic Review and Assessment

Mishra,

Chong,

Arunachalam

et al. 2024

Am J Gastroenterol

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“…A grouped neural network is proposed in 36 for early diagnosis of pancreatic cancer using laboratory health tracking. The work 37 describes a study that aims to develop new methods to simplify the large volume of patient medical records to improve clinical decision-making. The study uses deep-learning architectures to create simplified patient state representations that are predictive and interpretable to physicians.…”

Section: Related Workmentioning

confidence: 99%

Diagnostic ability of deep learning in detection of pancreatic tumour

Dinesh¹,

Bačanin

Askar

et al. 2023

Sci Rep

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Pancreatic cancer is associated with higher mortality rates due to insufficient diagnosis techniques, often diagnosed at an advanced stage when effective treatment is no longer possible. Therefore, automated systems that can detect cancer early are crucial to improve diagnosis and treatment outcomes. In the medical field, several algorithms have been put into use. Valid and interpretable data are essential for effective diagnosis and therapy. There is much room for cutting-edge computer systems to develop. The main objective of this research is to predict pancreatic cancer early using deep learning and metaheuristic techniques. This research aims to create a deep learning and metaheuristic techniques-based system to predict pancreatic cancer early by analyzing medical imaging data, mainly CT scans, and identifying vital features and cancerous growths in the pancreas using Convolutional Neural Network (CNN) and YOLO model-based CNN (YCNN) models. Once diagnosed, the disease cannot be effectively treated, and its progression is unpredictable. That's why there's been a push in recent years to implement fully automated systems that can sense cancer at a prior stage and improve diagnosis and treatment. The paper aims to evaluate the effectiveness of the novel YCNN approach compared to other modern methods in predicting pancreatic cancer. To predict the vital features from the CT scan and the proportion of cancer feasts in the pancreas using the threshold parameters booked as markers. This paper employs a deep learning approach called a Convolutional Neural network (CNN) model to predict pancreatic cancer images. In addition, we use the YOLO model-based CNN (YCNN) to aid in the categorization process. Both biomarkers and CT image dataset is used for testing. The YCNN method was shown to perform well by a cent percent of accuracy compared to other modern techniques in a thorough review of comparative findings.

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Analyzing the Role of Time Series Analysis in Early Detection of Pancreatic Cancer

Kavitha,

Singh,

Madhusudhan

et al. 2024

Lecture Notes in Electrical Engineering

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Structured deep embedding model to generate composite clinical indices from electronic health records for early detection of pancreatic cancer

Cited by 3 publications

References 28 publications

Machine Learning Models for Pancreatic Cancer Risk Prediction Using Electronic Health Record Data—A Systematic Review and Assessment

Machine Learning Models for Pancreatic Cancer Risk Prediction Using Electronic Health Record Data—A Systematic Review and Assessment

Diagnostic ability of deep learning in detection of pancreatic tumour

Analyzing the Role of Time Series Analysis in Early Detection of Pancreatic Cancer

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