Application of machine learning in the management of lymphoma: Current practice and future prospects

Digital Health, Journal Year: 2024, Volume and Issue: 10

Published: Jan. 1, 2024

In the past decade, digitization of medical records and multiomics data analysis in lymphoma has led to accessibility high-dimensional records. The records, visualization extensive volume extracted from images, integration methods into clinical decision-making have produced many datasets. As a promising auxiliary tool, machine learning (ML) intends extract homologous features large-scale sets encode them various patterns complete complicated tasks. At present, artificial intelligence digital mining shown prospects field pathological image analysis. paradigm shift qualitative quantitative makes diagnosis more intelligent results accurate objective. ML can promote provide patients with prognostic information individualized treatment options. Based on above, this comprehensive review general workflow highlights recent advances techniques diagnosis, treatment, prognosis lymphoma, clarifies boundedness future orientation technique practice lymphoma.

Language: Английский

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Defining treatment success in chronic lymphocytic leukemia: exploring surrogate markers, comorbidities, and patient-centered endpoints

Expert Review of Hematology, Journal Year: 2024, Volume and Issue: 17(7), P. 279 - 285

Published: June 10, 2024

Traditionally, the success of chronic lymphocytic leukemia (CLL) treatment has been primarily assessed based on clinical outcomes, such as disease response, progression-free survival (PFS), and overall (OS). However, evolution approaches recognizes importance a patient-centered perspective that includes factors directly affecting patients' quality life well-being.

Language: Английский

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Optimization of diagnosis and treatment of hematological diseases via artificial intelligence

Frontiers in Medicine, Journal Year: 2024, Volume and Issue: 11

Published: Nov. 7, 2024

Background Optimizing the diagnosis and treatment of hematological diseases is a challenging yet crucial research area. Effective plans typically require comprehensive integration cell morphology, immunology, cytogenetics, molecular biology. These also consider patient-specific factors such as disease stage, age, genetic mutation status. With advancement artificial intelligence (AI), more “AI + medical” application models are emerging. In clinical practice, many AI-assisted systems have been successfully applied to diseases, enhancing precision efficiency offering valuable solutions for practice. Objective This study summarizes progress various in with focus on their biology diagnosis, well prognosis prediction treatment. Methods Using PubMed, Web Science, other network search engines, we conducted literature studies from past 5 years using main keywords “artificial intelligence” “hematological diseases.” We classified applications AI according outline summarize current advancements optimizing difficulties challenges promoting standardization this field. Results can significantly shorten turnaround times, reduce diagnostic costs, accurately predict outcomes through image-recognition technology, genomic data analysis, mining, pattern recognition, personalized medicine. However, several remain, including lack product standards, standardized data, medical–industrial collaboration, complexity non-interpretability systems. addition, regulatory gaps lead privacy issues. Therefore, improvements needed fully leverage potential promote diseases. Conclusion Our results serve reference point development offer suggestions further hematology

Language: Английский

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Blockchain in clinical trials: Bibliometric and network studies of applications, challenges, and future prospects based on data analytics

Computer Methods and Programs in Biomedicine, Journal Year: 2024, Volume and Issue: 255, P. 108321 - 108321

Published: July 14, 2024

Language: Английский

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Outcomes of Patients with End-stage Renal Disease Hospitalized with COVID-19 in Ahvaz Razi Hospital from February 2020 to May 2021

Jundishapur Journal of Chronic Disease Care, Journal Year: 2024, Volume and Issue: 13(4)

Published: Sept. 16, 2024

Background: End-stage renal disease patients on maintenance hemodialysis (ESRD-HD) are at very high risk for COVID-19 infections due to their older age and comorbidities such as diabetes hypertension. Objectives: This study aimed investigate the outcomes of in ESRD-HD patients. Methods: was a retrospective conducted aged 18 years who were referred Razi Hospitals Ahvaz from February 2020 May 2021 diagnosed with COVID-19. Patient information extracted retrospectively medical records. Results: A total 180 examined. The average 61.5 years, 118 (65.6%) men. most common underlying condition hypertension (81.1%). prevalent clinical symptom shortness breath (70.6%), followed by cough (47.8%). Seventy-five (41.66%) admitted intensive care unit (ICU), an stay 5 days. Hypertension ischemic heart significantly more among ICU (P = 0.008 0.015, respectively). In-hospital mortality 32.8%. Advanced age, fever, breath, cough, need ventilator significant predictors hospitalized ESRD 0.016, 0.033, 0.001, 0.012, 0.011, Conclusions: Our demonstrated that admission mortality. symptoms predict in-hospital these

Language: Английский

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