Corrigendum to: Clinician involvement in research on machine learning–based predictive clinical decision support for the hospital setting: A scoping review

Abstract Glioblastoma multiforme (GBM), the most common form of glioma, is a malignant tumor with a high risk of mortality. By providing accurate survival estimates, prognostic models have been identified as promising tools in clinical decision support. In this study, we produced and validated two machine learning-based models to predict survival time for GBM patients. Publicly available clinical and genomic data from The Cancer Genome Atlas (TCGA) and Broad Institute GDAC Firehouse were obtained through cBioPortal. Random forest and multivariate regression models were created to predict survival. Predictive accuracy was assessed and compared through mean absolute error (MAE) and root mean square error (RMSE) calculations. 619 GBM patients were included in the dataset. There were 381 (62.9%) cases of recurrence/progression and 53 (8.7%) cases of disease-free survival. The MAE and RMSE values were 0.553 and 0.887 years respectively for the random forest regression model, and they were 1.756 and 2.451 years respectively for the multivariate regression model. Both models accurately predicted overall survival. Comparison of models through MAE, RMSE, and visual analysis produced higher accuracy values for random forest than multivariate linear regression. Further investigation on feature selection and model optimization may improve predictive power. These findings suggest that using machine learning in GBM prognostic modeling will improve clinical decision support. *Co-first authors.

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Clinical decision support algorithm based on machine learning to assess the clinical response to anti–programmed death-1 therapy in patients with non–small-cell lung cancer

European Journal of Cancer ◽

10.1016/j.ejca.2021.05.019 ◽

2021 ◽

Vol 153 ◽

pp. 179-189

Author(s):

Beung-Chul Ahn ◽

Jea-Woo So ◽

Chun-Bong Synn ◽

Tae Hyung Kim ◽

Jae Hwan Kim ◽

...

Keyword(s):

Machine Learning ◽

Lung Cancer ◽

Decision Support ◽

Small Cell Lung Cancer ◽

Clinical Decision Support ◽

Clinical Decision ◽

Small Cell ◽

Small Cell Lung ◽

Programmed Death ◽

Programmed Death 1

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GUESS: projecting machine learning scores to well-calibrated probability estimates for clinical decision-making

Bioinformatics ◽

10.1093/bioinformatics/bty984 ◽

2018 ◽

Vol 35 (14) ◽

pp. 2458-2465 ◽

Cited By ~ 3

Author(s):

Johanna Schwarz ◽

Dominik Heider

Keyword(s):

Machine Learning ◽

Decision Support ◽

Clinical Decision Support ◽

State Of The Art ◽

Clinical Decision Support Systems ◽

Clinical Decision ◽

Calibration Method ◽

Probability Estimates ◽

Calibration Methods ◽

Novel Method

Abstract Motivation Clinical decision support systems have been applied in numerous fields, ranging from cancer survival toward drug resistance prediction. Nevertheless, clinical decision support systems typically have a caveat: many of them are perceived as black-boxes by non-experts and, unfortunately, the obtained scores cannot usually be interpreted as class probability estimates. In probability-focused medical applications, it is not sufficient to perform well with regards to discrimination and, consequently, various calibration methods have been developed to enable probabilistic interpretation. The aims of this study were (i) to develop a tool for fast and comparative analysis of different calibration methods, (ii) to demonstrate their limitations for the use on clinical data and (iii) to introduce our novel method GUESS. Results We compared the performances of two different state-of-the-art calibration methods, namely histogram binning and Bayesian Binning in Quantiles, as well as our novel method GUESS on both, simulated and real-world datasets. GUESS demonstrated calibration performance comparable to the state-of-the-art methods and always retained accurate class discrimination. GUESS showed superior calibration performance in small datasets and therefore may be an optimal calibration method for typical clinical datasets. Moreover, we provide a framework (CalibratR) for R, which can be used to identify the most suitable calibration method for novel datasets in a timely and efficient manner. Using calibrated probability estimates instead of original classifier scores will contribute to the acceptance and dissemination of machine learning based classification models in cost-sensitive applications, such as clinical research. Availability and implementation GUESS as part of CalibratR can be downloaded at CRAN.

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An Assessment of Federated Machine Learning for Translational Research

Interdisciplinary Approaches to Digital Transformation and Innovation - Advances in E-Business Research ◽

10.4018/978-1-7998-1879-3.ch006 ◽

2020 ◽

pp. 123-142

Author(s):

Manoj A. Thomas ◽

Diya Suzanne Abraham ◽

Dapeng Liu

Keyword(s):

Machine Learning ◽

Decision Making ◽

Decision Support ◽

Translational Research ◽

Clinical Decision Support ◽

Clinical Decision ◽

High Quality ◽

Intermediate Data ◽

Translational Informatics ◽

Data Analytic

Translational research (TR) is the harnessing of knowledge from basic science and clinical research to advance healthcare. As a sister discipline, translational informatics (TI) concerns the application of informatics theories, methods, and frameworks to TR. This chapter builds upon TR concepts and aims to advance the use of machine learning (ML) and data analytics for improving clinical decision support. A federated machine learning (FML) architecture is described to aggregate multiple ML endpoints, and intermediate data analytic processes and products to output high quality knowledge discovery and decision making. The proposed architecture is evaluated for its operational performance based on three propositions, and a case for clinical decision support in the prediction of adult Sepsis is presented. The chapter illustrates contributions to the advancement of FML and TI.

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Corrigendum to ‘machine learning for clinical decision support in infectious diseases: a narrative review of current applications’ clinical microbiology and infection (2020) 584–595

Clinical Microbiology and Infection ◽

10.1016/j.cmi.2020.05.020 ◽

2020 ◽

Vol 26 (8) ◽

pp. 1118

Author(s):

N. Peiffer-Smadja ◽

T.M. Rawson ◽

R. Ahmad ◽

A. Buchard ◽

P. Georgiou ◽

...

Keyword(s):

Machine Learning ◽

Decision Support ◽

Infectious Diseases ◽

Clinical Decision Support ◽

Clinical Microbiology ◽

Clinical Decision ◽

Narrative Review

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Variables Influencing Machine Learning-Based Cardiac Decision Support System: A Systematic Literature Review

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.892.274 ◽

2019 ◽

Vol 892 ◽

pp. 274-283

Author(s):

Mohammed Ashikur Rahman ◽

Afidalina Tumian

Keyword(s):

Machine Learning ◽

Decision Support ◽

Literature Review ◽

Decision Support System ◽

Clinical Decision Support ◽

Support System ◽

Clinical Decision ◽

Machine Learning Techniques ◽

Cardiac Care ◽

Learning Techniques

Now a day, clinical decision support systems (CDSS) are widely used in the cardiac care due to the complexity of the cardiac disease. The objective of this systematic literature review (SLR) is to identify the most common variables and machine learning techniques used to build machine learning-based clinical decision support system for cardiac care. This SLR adopts the Preferred Reporting Item for Systematic Review and Meta-Analysis (PRISMA) format. Out of 530 papers, only 21 papers met the inclusion criteria. Amongst the 22 most common variables are age, gender, heart rate, respiration rate, systolic blood pressure and medical information variables. In addition, our results have shown that Simplified Acute Physiology Score (SAPS), Sequential Organ Failure Assessment (SOFA) and Acute Physiology and Chronic Health Evaluation (APACHE) are some of the most common assessment scales used in CDSS for cardiac care. Logistic regression and support vector machine are the most common machine learning techniques applied in CDSS to predict mortality and other cardiac diseases like sepsis, cardiac arrest, heart failure and septic shock. These variables and assessment tools can be used to build a machine learning-based CDSS.

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Machine learning for clinical decision support in infectious diseases: a narrative review of current applications

Clinical Microbiology and Infection ◽

10.1016/j.cmi.2019.09.009 ◽

2020 ◽

Vol 26 (5) ◽

pp. 584-595 ◽

Cited By ~ 18

Author(s):

N. Peiffer-Smadja ◽

T.M. Rawson ◽

R. Ahmad ◽

A. Buchard ◽

P. Georgiou ◽

...

Keyword(s):

Machine Learning ◽

Decision Support ◽

Infectious Diseases ◽

Clinical Decision Support ◽

Clinical Decision ◽

Narrative Review

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Diagnosis of Rare Diseases: a scoping review of clinical decision support systems

Orphanet Journal of Rare Diseases ◽

10.1186/s13023-020-01536-z ◽

2020 ◽

Vol 15 (1) ◽

Author(s):

Jannik Schaaf ◽

Martin Sedlmayr ◽

Johanna Schaefer ◽

Holger Storf

Keyword(s):

Decision Support ◽

Data Integration ◽

Decision Support Systems ◽

Clinical Decision Support ◽

Clinical Data ◽

Rare Diseases ◽

Scoping Review ◽

Support Systems ◽

Clinical Decision Support Systems ◽

Clinical Decision

Abstract Background Rare Diseases (RDs), which are defined as diseases affecting no more than 5 out of 10,000 people, are often severe, chronic and life-threatening. A main problem is the delay in diagnosing RDs. Clinical decision support systems (CDSSs) for RDs are software systems to support clinicians in the diagnosis of patients with RDs. Due to their clinical importance, we conducted a scoping review to determine which CDSSs are available to support the diagnosis of RDs patients, whether the CDSSs are available to be used by clinicians and which functionalities and data are used to provide decision support. Methods We searched PubMed for CDSSs in RDs published between December 16, 2008 and December 16, 2018. Only English articles, original peer reviewed journals and conference papers describing a clinical prototype or a routine use of CDSSs were included. For data charting, we used the data items “Objective and background of the publication/project”, “System or project name”, “Functionality”, “Type of clinical data”, “Rare Diseases covered”, “Development status”, “System availability”, “Data entry and integration”, “Last software update” and “Clinical usage”. Results The search identified 636 articles. After title and abstracting screening, as well as assessing the eligibility criteria for full-text screening, 22 articles describing 19 different CDSSs were identified. Three types of CDSSs were classified: “Analysis or comparison of genetic and phenotypic data,” “machine learning” and “information retrieval”. Twelve of nineteen CDSSs use phenotypic and genetic data, followed by clinical data, literature databases and patient questionnaires. Fourteen of nineteen CDSSs are fully developed systems and therefore publicly available. Data can be entered or uploaded manually in six CDSSs, whereas for four CDSSs no information for data integration was available. Only seven CDSSs allow further ways of data integration. thirteen CDSS do not provide information about clinical usage. Conclusions Different CDSS for various purposes are available, yet clinicians have to determine which is best for their patient. To allow a more precise usage, future research has to focus on CDSSs RDs data integration, clinical usage and updating clinical knowledge. It remains interesting which of the CDSSs will be used and maintained in the future.

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