Artificial intelligence and machine learning in precision and genomic medicine

Quazi, Sameer

doi:10.1007/s12032-022-01711-1

Cited by 153 publications

(53 citation statements)

References 211 publications

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“…23,24 Thrombosis risk in cancer patients can be predicted by ML algorithms through analyzing clinical and genetic data, thus predicting the likelihood of thrombotic events. 25 Notably, this dynamic approach allows for greater personalization among cases, capturing subtleties that traditional risk scores may miss.…”

Section: Discussionmentioning

confidence: 99%

Machine Learning as a Diagnostic and Prognostic Tool for Predicting Thrombosis in Cancer Patients: A Systematic Review

El-Sherbini,

Coroneos,

Zidan

et al. 2024

Semin Thromb Hemost

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Khorana score (KS) is an established risk assessment model for predicting cancer-associated thrombosis. However, it ignores several risk factors and has poor predictability in some cancer types. Machine learning (ML) is a novel technique used for the diagnosis and prognosis of several diseases, including cancer-associated thrombosis, when trained on specific diagnostic modalities. Consolidating the literature on the use of ML for the prediction of cancer-associated thrombosis is necessary to understand its diagnostic and prognostic abilities relative to KS. This systematic review aims to evaluate the current use and performance of ML algorithms to predict thrombosis in cancer patients. This study was conducted per Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines. Databases Medline, EMBASE, Cochrane, and ClinicalTrials.gov, were searched from inception to September 15, 2023, for studies evaluating the use of ML models for the prediction of thrombosis in cancer patients. Search terms “machine learning,” “artificial intelligence,” “thrombosis,” and “cancer” were used. Studies that examined adult cancer patients using any ML model were included. Two independent reviewers conducted study selection and data extraction. Three hundred citations were screened, of which 29 studies underwent a full-text review, and ultimately, 8 studies with 22,893 patients were included. Sample sizes ranged from 348 to 16,407 patients. Thrombosis was characterized as venous thromboembolism (n = 6) or peripherally inserted central catheter thrombosis (n = 2). The types of cancer included breast, gastric, colorectal, bladder, lung, esophageal, pancreatic, biliary, prostate, ovarian, genitourinary, head–neck, and sarcoma. All studies reported outcomes on the ML's predictive capacity. The extreme gradient boosting appears to be the best-performing model, and several models outperform KS in their respective datasets.

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Section: Discussionmentioning

confidence: 99%

Machine Learning as a Diagnostic and Prognostic Tool for Predicting Thrombosis in Cancer Patients: A Systematic Review

El-Sherbini,

Coroneos,

Zidan

et al. 2024

Semin Thromb Hemost

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“…To assist physicians, complex systems are often created that cover diagnosis, risk assessment, treatment, and other tasks (Cardio-S, ASMO-CARDIO, Infective Endocarditis, Cardiologs, Cardiac Care Assistant, escardio.org, HEART Pathway, Cardiovascular Disease Management Tool, FFRCT HeartFlow, Cardio-ECO, Cardio-ANTIB). These approaches provide opportunities for developing more complex and scalable CDSS, adaptable to diverse clinical needs [39,13]. Another type of CDSS are systems that do not directly use knowledge from guidelines and clinical recommendations but apply machine learning models to support decision-making.…”

Section: Current State Of Affairsmentioning

confidence: 99%

Hybrid clinical decision support for cardiology: architectural foundations for integrations

Shakhgeldyan,

Gribova,

Geltser

et al. 2024

Fifth International Conference on Image, Video Processing, and Artificial Intelligence (IVPAI 2023)

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Cardiovascular diseases (CVD) are the leading cause of death in most countries around the world, making the accurate assessment of risks and the selection of individual preventive strategies a current focus in healthcare. In this article, the authors presented a prototype of a Clinical Decision Support System (CDSS) for predicting and preventing cardiovascular risks based on a hybrid architecture that integrates machine learning models and ontological knowledge bases. A microservice architecture based on the Cloud-Edge approach is proposed for optimizing computational resources when processing tabular data, signals, video, and images, as well as for enhancing the effectiveness of integration with various Healthcare Information Systems (HIS). The CDSS supports the formalization not only of medical history data and results of studies but also the rules for interpreting the results of predictions based on machine learning models and methods of explainable artificial intelligence (XAI). The developed CDSS includes widely used tools in clinical cardiology and cardiothoracic surgery for risk assessment, as well as proprietary machine learning models for predicting in-hospital mortality, and others. These models contribute to making informed medical decisions for the diagnosis, prevention, and treatment of CVD. The prototype was implemented at the Medical Center of the Far Eastern Federal University and integrated with the "1C" HIS. The experience of implementing the prototype demonstrated the high potential of hybrid CDSS based on microservice architecture for use in clinical practice.

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“…Genome interpretation allows scientists to understand how DNA changes between people and whether or not genetic variations play a role in the development of disease. According to [43]- [45], high-performance algorithms, such as CNNs and RNNs, are decisive in analyzing highthroughput sequencing methods since they generate terabytes of complex raw data. In addition, this application enables accurate clinical interpretation of biological data, which is essential for recognizing the individual differences underlying precision medicine.…”

Section: ) Rta Evaluationmentioning

confidence: 99%

The Next Generation of eHealth: A Multidisciplinary Survey

et al. 2022

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Over the past two years, the spread of COVID-19 has spurred the use of information and communication technologies (ICT) in aid of healthcare. The need to guarantee continuity to care has promoted research and industry activities aimed at developing solutions for the digitalization of the procedures to be performed to provide health services, even in emergency scenarios. Digital collection, transmission, and processing of health data represent the starting point for fulfilling this innovation process but also bring heterogeneous challenges. These motivations led to the elaboration of this work, which analyzes innovative and technological tools for the development of digital health (eHealth) through the collection of multisectoral literature, produced thanks to the cooperation of varied research groups, thus providing a multidisciplinary survey. Since digital health is expected to be one of the leading applications of the sixth-generation (6G) wireless cellular networks, this paper covers the related telecommunications aspects. Furthermore, the exploitation of artificial intelligence paradigms to elaborate massive amounts of biological data is examined. Given the extreme sensitivity of health data, this paper also investigates security and privacy issues. In particular, the main techniques and approaches to guarantee security properties (i.e., anonymity, responsibility, authentication, confidentiality, integrity, non-repudiation, and revocability) are studied. Applications involving innovative electromagnetic systems for healthcare and assisted living services are described to provide an example of an eHealth scenario leveraging ICT. Finally, the telemedicine-related regulations of the European Commission are analyzed, with particular reference to the General Data Protection Regulation (GDPR).

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Artificial intelligence and machine learning in precision and genomic medicine

Cited by 153 publications

References 211 publications

Machine Learning as a Diagnostic and Prognostic Tool for Predicting Thrombosis in Cancer Patients: A Systematic Review

Machine Learning as a Diagnostic and Prognostic Tool for Predicting Thrombosis in Cancer Patients: A Systematic Review

Hybrid clinical decision support for cardiology: architectural foundations for integrations

The Next Generation of eHealth: A Multidisciplinary Survey

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