(335) Using pharmacogenomics as an effective tool in minimizing drug related side effects in patients with Chronic regional pain syndrome: A feasibility study

Jain, Sameer; Danesh, Arash; Laroche, Marcus; Bakhtiari, Saba; Maxwell, Christina R.; Al-Khalili, Yasir; Cruciani, Ricardo A.

doi:10.1016/j.jpain.2017.02.228

Cited by 2 publications

(1 citation statement)

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“…In one such study, Tang and colleagues implemented an ML-based approach to determine a stable Tacrolimus dose (the immunosuppressive drug) for patients who received a renal transplant to reduce the risk of acute rejection [ 10 ]. The use of machine learning in pharmacogenomics has recently been applied in psychiatry [ 90 ], oncology [ 91 ], bariatrics [ 92 ], and neurology [ 93 ].…”

Section: Ai In Healthcarementioning

confidence: 99%

Machine Learning in Healthcare

Habehh

Gohel

2021

186

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: Recent advancements in artificial intelligence (AI) and machine learning (ML) technology have brought on substantial strides in predicting and identifying health emergencies, disease populations, and predicting disease state and immune response, amongst a few. Although skepticism remains regarding the practical application and interpretation of results from ML-based approaches in healthcare settings, the inclusion of these approaches is increasing at a rapid pace. Here we provide a brief overview of machine learning-based approaches and learning algorithms, including supervised, unsupervised and reinforcement learning, along with examples. Second, we discuss the application ML in several healthcare fields, including radiology, genetics, electronic health records, and neuroimaging. We also briefly discuss the risks and challenges of ML application to healthcare, such as system privacy and ethical concerns and provide suggestions for future applications.

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Section: Ai In Healthcarementioning

confidence: 99%

Machine Learning in Healthcare

Habehh

Gohel

2021

186

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Prediction and Analysis of Digital Health Records, Geonomics, and Radiology Using Machine Learning

Raj,

Agarwal,

Tripathi

et al. 2024

Prediction in Medicine: The Impact of Machine Learning on Healthcare

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Building different machine learning algorithms and their potential applications to enhance healthcare systems is very important. AI has countless uses in healthcare, including the analysis of medical data, early disease diagnosis and detection, evidence-based objectives to minimize human error, reducing errors between and among observers, risk identification and interventions for healthcare management, health monitoring in real-time, helping patients and clinicians choose the right medication, and assessing drug responses. Machine learning techniques have transformed many facets of healthcare, ranging from new tools that allow people to better control their health to new models that assist physicians in making more accurate decisions. Since the advent of the pacemaker and the first computerized records for blood test results and chest X-ray reports by Kaiser in the 1950s, physicians have seen the potential of algorithms to save lives. As new developments in image processing, deep learning, and natural language processing are revolutionizing the healthcare sector, this rich history of machine learning for healthcare feeds innovative research today.It is necessary to comprehend the human effects of machine learning, including transparency, justice, regulation, simplicity of deployment, and integration into clinical processes, in order to use it to enhance patient outcomes. The application of machine learning for risk assessment and diagnosis, illness progression modeling, enhancing clinical workflows, and precision medicine will be covered in this chapter, which starts with an introduction to clinical care and data. We shall include all methodological details for each of these covering topics like algorithmic fairness, causal inference, offpolicy reinforcement learning, interpretability of ML models, and the foundations of deep learning on imaging and natural language.Advances in AI and ML technologies have significantly improved the ability to forecast and recognize health emergencies, disease conditions, disease populations, and immunological responses, to name a few. Even though there is still doubt about the usefulness of ML-based techniques and how to interpret their findings in clinical contexts, their use is spreading quickly. Here, we provide a succinct introduction to machine learning-based methodologies and learning algorithms, such as reinforcement learning, supervised learning, and unsupervised learning, with examples. Subsequently, we explore the applications of machine learning (ML) in various healthcare domains such as genetics, neuroimaging, radiology, and electronic health records. Along with offering ideas for potential future uses, we also skim the surface regarding the dangers and difficulties associated with applying machine learning to the healthcare industry, including issues of privacy and ethics.

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(335) Using pharmacogenomics as an effective tool in minimizing drug related side effects in patients with Chronic regional pain syndrome: A feasibility study

Cited by 2 publications

References 0 publications

Machine Learning in Healthcare

Machine Learning in Healthcare

Prediction and Analysis of Digital Health Records, Geonomics, and Radiology Using Machine Learning

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