Acute myeloid leukemia and artificial intelligence, algorithms and new scores

Radakovich, Nathan; Cortese, Matthew J; Nazha, Aziz

doi:10.1016/j.beha.2020.101192

Cited by 21 publications

(5 citation statements)

References 24 publications

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“… 31 - 33 Artificial intelligence, and more narrowly known as machine-learning (ML), is beginning to expand humanity’s ability to analyze increasingly large and complex datasets, including in medical research and clinical practice. 34 A lot of research did predictive analytics using ML techniques to shed some lights on better decision making in suspected DVT patients. 10 , 35 , 36 Nwosisi et al 37 proposed binary decision trees to predict DVT.…”

Section: Discussionmentioning

confidence: 99%

Cost-Effective Machine Learning Based Clinical Pre-Test Probability Strategy for DVT Diagnosis in Neurological Intensive Care Unit

Luo

Kou

Feng

et al. 2021

Clin Appl Thromb Hemost

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In order to overcome the shortage of the current costly DVT diagnosis and reduce the waste of valuable healthcare resources, we proposed a new diagnostic approach based on machine learning pre-test prediction models using EHRs. We examined the sociodemographic and clinical factors in the prediction of DVT with 518 NICU admitted patients, including 189 patients who eventually developed DVT. We used cross-validation on the training data to determine the optimal parameters, and finally, the applied ROC analysis is adopted to evaluate the predictive strength of each model. Two models (GLM and SVM) with the strongest ROC were selected for DVT prediction, based on which, we optimized the current intervention and diagnostic process of DVT and examined the performance of the proposed approach through simulations. The use of machine learning based pre-test prediction models can simplify and improve the intervention and diagnostic process of patients in NICU with suspected DVT, and reduce the valuable healthcare resource occupation/usage and medical costs.

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Section: Discussionmentioning

confidence: 99%

Cost-Effective Machine Learning Based Clinical Pre-Test Probability Strategy for DVT Diagnosis in Neurological Intensive Care Unit

Luo

Kou

Feng

et al. 2021

Clin Appl Thromb Hemost

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“…A DL model called support vector machine (SVM) was developed, which identified cell subpopulations and predicted patient outcomes based on gene expression profiles. They found that SVM outperformed other clustering and prediction methods in terms of accuracy and identified several genes that were associated with disease progression [ 113 , 114 ]. Recurrent infections and treatment failures are two common occurrences in the management of hematologic malignancies and must be identified early [ 115 ].…”

Section: Ai-assisted Genomic Testing For Hematologic Disordersmentioning

confidence: 99%

Artificial Intelligence-Assisted Diagnostic Cytology and Genomic Testing for Hematologic Disorders

Gedefaw

Liu

et al. 2023

Cells

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Artificial intelligence (AI) is a rapidly evolving field of computer science that involves the development of computational programs that can mimic human intelligence. In particular, machine learning and deep learning models have enabled the identification and grouping of patterns within data, leading to the development of AI systems that have been applied in various areas of hematology, including digital pathology, alpha thalassemia patient screening, cytogenetics, immunophenotyping, and sequencing. These AI-assisted methods have shown promise in improving diagnostic accuracy and efficiency, identifying novel biomarkers, and predicting treatment outcomes. However, limitations such as limited databases, lack of validation and standardization, systematic errors, and bias prevent AI from completely replacing manual diagnosis in hematology. In addition, the processing of large amounts of patient data and personal information by AI poses potential data privacy issues, necessitating the development of regulations to evaluate AI systems and address ethical concerns in clinical AI systems. Nonetheless, with continued research and development, AI has the potential to revolutionize the field of hematology and improve patient outcomes. To fully realize this potential, however, the challenges facing AI in hematology must be addressed and overcome.

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“…Interestingly, machine learning (ML) techniques have been applied in leukemia research and represent a promising auxiliary tool for developing new approaches that aim to improve AML risk stratification. Several studies have implemented unsupervised and unbiased ML methods that demonstrated high accuracy in terms of genomic classification [ 20 ]. These approaches may be further employed to define ambiguous definitions by unmasking unexplored clinico-morphologic and genomic characteristics unique to intermediate-risk AML patients.…”

Section: Intermediate-risk Definition and Prognosismentioning

confidence: 99%

A Focus on Intermediate-Risk Acute Myeloid Leukemia: Sub-Classification Updates and Therapeutic Challenges

Awada

Ali

Thapa

et al. 2022

Cancers

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Acute myeloid leukemia (AML) represents a heterogeneous group of hematopoietic neoplasms deriving from the abnormal proliferation of myeloid progenitors in the bone marrow. Patients with AML may have highly variable outcomes, which are generally dictated by individual clinical and genomic characteristics. As such, the European LeukemiaNet 2017 and 2022 guidelines categorize newly diagnosed AML into favorable-, intermediate-, and adverse-risk groups, based on their molecular and cytogenetic profiles. Nevertheless, the intermediate-risk category remains poorly defined, as many patients fall into this group as a result of their exclusion from the other two. Moreover, further genomic data with potential prognostic and therapeutic influences continue to emerge, though they are yet to be integrated into the diagnostic and prognostic models of AML. This review highlights the latest therapeutic advances and challenges that warrant refining the prognostic classification of intermediate-risk AML.

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Acute myeloid leukemia and artificial intelligence, algorithms and new scores

Cited by 21 publications

References 24 publications

Cost-Effective Machine Learning Based Clinical Pre-Test Probability Strategy for DVT Diagnosis in Neurological Intensive Care Unit

Cost-Effective Machine Learning Based Clinical Pre-Test Probability Strategy for DVT Diagnosis in Neurological Intensive Care Unit

Artificial Intelligence-Assisted Diagnostic Cytology and Genomic Testing for Hematologic Disorders

A Focus on Intermediate-Risk Acute Myeloid Leukemia: Sub-Classification Updates and Therapeutic Challenges

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