How machine learning is used to study addiction in digital healthcare: A systematic review

Chhetri, Bijoy; Goyal, Lalit Mohan; Mittal, Mamta

doi:10.1016/j.jjimei.2023.100175

Cited by 16 publications

(5 citation statements)

References 93 publications

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“…According to research described in [ 63 ], machine learning (ML) algorithms can aid in drug-addiction determination through various factors. Brain-related factors, behavioral phenotypes, and functional differentiation of the brain can express a great deal about disorders.…”

Section: Development Of Novel Interventionsmentioning

confidence: 99%

Exploring Health Informatics in the Battle against Drug Addiction: Digital Solutions for the Rising Concern

Shimu,

Patil,

Dadzie

et al. 2024

JPM

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Drug addiction is a rising concern globally that has deeply attracted the attention of the healthcare sector. The United States is not an exception, and the drug addiction crisis there is even more serious, with 10% of adults having faced substance use disorder, while around 75% of this number has been reported as not having received any treatment. Surprisingly, there are annually over 70,000 deaths reported as being due to drug overdose. Researchers are continually searching for solutions, as the current strategies have been ineffective. Health informatics platforms like electronic health records, telemedicine, and the clinical decision support system have great potential in tracking the healthcare data of patients on an individual basis and provide precise medical support in a private space. Such technologies have been found to be useful in identifying the risk factors of drug addiction among people and mitigating them. Moreover, the platforms can be used to check prescriptions of addictive drugs such as opioids and caution healthcare providers. Programs such as the Prescription Drug Monitoring Program (PDMP) and the Drug and Alcohol Services Information Systems (DASIS) are already in action in the US, but the situation demands more in-depth studies in order to mitigate substance use disorders. Artificial intelligence (AI), when combined with health informatics, can aid in the analysis of large amounts of patient data and aid in classifying nature of addiction to assist in the provision of personalized care.

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Section: Development Of Novel Interventionsmentioning

confidence: 99%

Exploring Health Informatics in the Battle against Drug Addiction: Digital Solutions for the Rising Concern

Shimu,

Patil,

Dadzie

et al. 2024

JPM

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“…Findings from EEG, brain imaging, behavioral and kinematic investigations on ML applications in addiction research were compiled by Chhetri et al (2023). The key conclusions indicated that diagnosing drug-related diseases requires a disease-based strategy.…”

Section: In Healthcare Strategic Managementmentioning

confidence: 99%

“…Their assessment showed that Rule-based and non-rule-based-decision support systems have different benefits and should be combined, especially in cancer diagnosis. Chhetri, Goyal, and Mittal (2023) looked at ML in the context of psychological disorders. Although only based on a review of 26 articles, the results show that ML can help in brain imaging, behavioral kinematics and memory analysis.…”

Section: Introductionmentioning

confidence: 99%

Machine learning in healthcare strategic management: a systematic literature review

Salhout

2023

AGJSR

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PurposeThis study specifically seeks to investigate the strategic implementation of machine learning (ML) algorithms and techniques in healthcare institutions to enhance innovation management in healthcare settings.Design/methodology/approach The papers from 2011 to 2021 were considered following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. First, relevant keywords were identified, and screening was performed. Bibliometric analysis was performed. One hundred twenty-three relevant documents that passed the eligibility criteria were finalized.Findings Overall, the annual scientific production section results reveal that ML in the healthcare sector is growing significantly. Performing bibliometric analysis has helped find unexplored areas; understand the trend of scientific publication; and categorize topics based on emerging, trending and essential. The paper discovers the influential authors, sources, countries and ML and healthcare management keywords.Research limitations/implications The study helps understand various applications of ML in healthcare institutions, such as the use of Internet of Things in healthcare, the prediction of disease, finding the seriousness of a case, natural language processing, speech and language-based classification, etc. This analysis would help future researchers and developers target the healthcare sector areas that are likely to grow in the coming future.Practical implications The study highlights the potential for ML to enhance medical support within healthcare institutions. It suggests that regression algorithms are particularly promising for this purpose. Hospital management can leverage time series ML algorithms to estimate the number of incoming patients, thus increasing hospital availability and optimizing resource allocation. ML has been instrumental in the development of these systems. By embracing telemedicine and remote monitoring, healthcare management can facilitate the creation of online patient surveillance and monitoring systems, allowing for early medical intervention and ultimately improving the efficiency and effectiveness of medical services.Originality/value By offering a comprehensive panorama of ML's integration within healthcare institutions, this study underscores the pivotal role of innovation management in healthcare. The findings contribute to a holistic understanding of ML's applications in healthcare and emphasize their potential to transform and optimize healthcare delivery.

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“…This transition is critical in varied cultural contexts where conventional methods fail to access and reach. Moreover, AI and ML algorithms can sophisticate treatment strategies by customising person-centric treatment models, and continuous monitoring allows real-time adjustment to treatment plans to enhance success rates ( 6 ). This shift compels rigorous research to explore: What patterns and predictors of AUD can AI identify and conventional methods cannot?…”

mentioning

confidence: 99%

Revolutionising alcohol use disorder treatment in developing countries: integrating artificial intelligence and technology-driven approaches

Joseph,

Babu,

Prakash

2024

Front. Psychiatry

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How machine learning is used to study addiction in digital healthcare: A systematic review

Cited by 16 publications

References 93 publications

Exploring Health Informatics in the Battle against Drug Addiction: Digital Solutions for the Rising Concern

Exploring Health Informatics in the Battle against Drug Addiction: Digital Solutions for the Rising Concern

Machine learning in healthcare strategic management: a systematic literature review

Revolutionising alcohol use disorder treatment in developing countries: integrating artificial intelligence and technology-driven approaches

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