Dataset Information

A moment kernel machine for clinical data mining to inform medical decision making.

ABSTRACT: Machine learning-aided medical decision making presents three major challenges: achieving model parsimony, ensuring credible predictions, and providing real-time recommendations with high computational efficiency. In this paper, we formulate medical decision making as a classification problem and develop a moment kernel machine (MKM) to tackle these challenges. The main idea of our approach is to treat the clinical data of each patient as a probability distribution and leverage moment representations of these distributions to build the MKM, which transforms the high-dimensional clinical data to low-dimensional representations while retaining essential information. We then apply this machine to various pre-surgical clinical datasets to predict surgical outcomes and inform medical decision making, which requires significantly less computational power and time for classification while yielding favorable performance compared to existing methods. Moreover, we utilize synthetic datasets to demonstrate that the developed moment-based data mining framework is robust to noise and missing data, and achieves model parsimony giving an efficient way to generate satisfactory predictions to aid personalized medical decision making.

SUBMITTER: Yu YC

PROVIDER: S-EPMC10307844 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature

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A moment kernel machine for clinical data mining to inform medical decision making.

Yu Yao-Chi YC Zhang Wei W O'Gara David D Li Jr-Shin JS Chang Su-Hsin SH

Scientific reports 20230628 1

Machine learning-aided medical decision making presents three major challenges: achieving model parsimony, ensuring credible predictions, and providing real-time recommendations with high computational efficiency. In this paper, we formulate medical decision making as a classification problem and develop a moment kernel machine (MKM) to tackle these challenges. The main idea of our approach is to treat the clinical data of each patient as a probability distribution and leverage moment representa ...[more]

PMID: 37380721

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Project description:ObjectivesTo identify ML tools in hospital settings and how they were implemented to inform decision-making for patient care through a scoping review. We investigated the following research questions: What ML interventions have been used to inform decision-making for patient care in hospital settings? What strategies have been used to implement these ML interventions?DesignA scoping review was undertaken. MEDLINE, Embase, Cochrane Central Register of Controlled Trials (CENTRAL) and the Cochrane Database of Systematic Reviews (CDSR) were searched from 2009 until June 2021. Two reviewers screened titles and abstracts, full-text articles, and charted data independently. Conflicts were resolved by another reviewer. Data were summarised descriptively using simple content analysis.SettingHospital setting.ParticipantAny type of clinician caring for any type of patient.InterventionMachine learning tools used by clinicians to inform decision-making for patient care, such as AI-based computerised decision support systems or "'model-based'" decision support systems.Primary and secondary outcome measuresPatient and study characteristics, as well as intervention characteristics including the type of machine learning tool, implementation strategies, target population. Equity issues were examined with PROGRESS-PLUS criteria.ResultsAfter screening 17 386 citations and 3474 full-text articles, 20 unique studies and 1 companion report were included. The included articles totalled 82 656 patients and 915 clinicians. Seven studies reported gender and four studies reported PROGRESS-PLUS criteria (race, health insurance, rural/urban). Common implementation strategies for the tools were clinician reminders that integrated ML predictions (44.4%), facilitated relay of clinical information (17.8%) and staff education (15.6%). Common barriers to successful implementation of ML tools were time (11.1%) and reliability (11.1%), and common facilitators were time/efficiency (13.6%) and perceived usefulness (13.6%).ConclusionsWe found limited evidence related to the implementation of ML tools to assist clinicians with patient healthcare decisions in hospital settings. Future research should examine other approaches to integrating ML into hospital clinician decisions related to patient care, and report on PROGRESS-PLUS items.FundingCanadian Institutes of Health Research (CIHR) Foundation grant awarded to SES and the CIHR Strategy for Patient Oriented-Research Initiative (GSR-154442). SCOPING REVIEW REGISTRATION: https://osf.io/e2mna.

Project description:BackgroundAlthough microarray technology allows the investigation of the transcriptomic make-up of a tumor in one experiment, the transcriptome does not completely reflect the underlying biology due to alternative splicing, post-translational modifications, as well as the influence of pathological conditions (for example, cancer) on transcription and translation. This increases the importance of fusing more than one source of genome-wide data, such as the genome, transcriptome, proteome, and epigenome. The current increase in the amount of available omics data emphasizes the need for a methodological integration framework.MethodsWe propose a kernel-based approach for clinical decision support in which many genome-wide data sources are combined. Integration occurs within the patient domain at the level of kernel matrices before building the classifier. As supervised classification algorithm, a weighted least squares support vector machine is used. We apply this framework to two cancer cases, namely, a rectal cancer data set containing microarray and proteomics data and a prostate cancer data set containing microarray and genomics data. For both cases, multiple outcomes are predicted.ResultsFor the rectal cancer outcomes, the highest leave-one-out (LOO) areas under the receiver operating characteristic curves (AUC) were obtained when combining microarray and proteomics data gathered during therapy and ranged from 0.927 to 0.987. For prostate cancer, all four outcomes had a better LOO AUC when combining microarray and genomics data, ranging from 0.786 for recurrence to 0.987 for metastasis.ConclusionsFor both cancer sites the prediction of all outcomes improved when more than one genome-wide data set was considered. This suggests that integrating multiple genome-wide data sources increases the predictive performance of clinical decision support models. This emphasizes the need for comprehensive multi-modal data. We acknowledge that, in a first phase, this will substantially increase costs; however, this is a necessary investment to ultimately obtain cost-efficient models usable in patient tailored therapy.

Project description:ObjectiveMachine learning (ML) algorithms, as an early branch of artificial intelligence technology, can effectively simulate human behavior by training on data from the training set. Machine learning algorithms were used in this study to predict patient choice tendencies in medical decision-making. Its goal was to help physicians understand patient preferences and to serve as a resource for the development of decision-making schemes in clinical treatment. As a result, physicians and patients can have better conversations at lower expenses, leading to better medical decisions.MethodPatient medical decision-making tendencies were predicted by primary survey data obtained from 248 participants at third-level grade-A hospitals in China. Specifically, 12 predictor variables were set according to the literature review, and four types of outcome variables were set based on the optimization principle of clinical diagnosis and treatment. That is, the patient's medical decision-making tendency, which is classified as treatment effect, treatment cost, treatment side effect, and treatment experience. In conjunction with the study's data characteristics, three ML classification algorithms, decision tree (DT), k-nearest neighbor (KNN), and support vector machine (SVM), were used to predict patients' medical decision-making tendency, and the performance of the three types of algorithms was compared.ResultsThe accuracy of the DT algorithm for predicting patients' choice tendency in medical decision making is 80% for treatment effect, 60% for treatment cost, 56% for treatment side effects, and 60% for treatment experience, followed by the KNN algorithm at 78%, 66%, 74%, 84%, and the SVM algorithm at 82%, 76%, 80%, 94%. At the same time, the comprehensive evaluation index F1-score of the DT algorithm are 0.80, 0.61, 0.58, 0.60, the KNN algorithm are 0.75, 0.65, 0.71, 0.84, and the SVM algorithm are 0.81, 0.74, 0.73, 0.94.ConclusionAmong the three ML classification algorithms, SVM has the highest accuracy and the best performance. Therefore, the prediction results have certain reference values and guiding significance for physicians to formulate clinical treatment plans. The research results are helpful to promote the development and application of a patient-centered medical decision assistance system, to resolve the conflict of interests between physicians and patients and assist them to realize scientific decision-making.

Dataset Information

A moment kernel machine for clinical data mining to inform medical decision making.

Publications

A moment kernel machine for clinical data mining to inform medical decision making.

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