Machine learning for predicting opioid use disorder from healthcare data: A systematic review

Garbin, Christian; Marques, Nicholas; Marques, Oge

doi:10.1016/j.cmpb.2023.107573

Cited by 13 publications

(4 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Current MLHC development lifecycles consider "performance improvement" in a given core metric such as area under the precision-recall curve (AUPRC) or accuracy metrics as the primary target [91][92][93]. The field is focused upon defining and carrying out narrow tasks, with the broader context and ultimate impact of the model often considered an afterthought [94].…”

Section: Establishing Consistent Responsibility Standardsmentioning

confidence: 99%

“…MLHC research tends to focus on label prediction accuracy metrics such as sensitivity, specificity, and the precision-recall curve [91][92][93]. However, accuracy when predicting labels on a curated dataset does not always translate to accuracy in real-world clinical settings when deployed, often due to dataset shift [100,101] caused by critical and inescapable differences between the populations used in testing versus deployment [102].…”

Section: Moving On From Clinically Unimportant Metricsmentioning

confidence: 99%

See 1 more Smart Citation

Machine learning for healthcare that matters: Reorienting from technical novelty to equitable impact

Balagopalan,

Baldini,

Celi

et al. 2024

PLOS Digit Health

View full text Add to dashboard Cite

Despite significant technical advances in machine learning (ML) over the past several years, the tangible impact of this technology in healthcare has been limited. This is due not only to the particular complexities of healthcare, but also due to structural issues in the machine learning for healthcare (MLHC) community which broadly reward technical novelty over tangible, equitable impact. We structure our work as a healthcare-focused echo of the 2012 paper “Machine Learning that Matters”, which highlighted such structural issues in the ML community at large, and offered a series of clearly defined “Impact Challenges” to which the field should orient itself. Drawing on the expertise of a diverse and international group of authors, we engage in a narrative review and examine issues in the research background environment, training processes, evaluation metrics, and deployment protocols which act to limit the real-world applicability of MLHC. Broadly, we seek to distinguish between machine learning ON healthcare data and machine learning FOR healthcare—the former of which sees healthcare as merely a source of interesting technical challenges, and the latter of which regards ML as a tool in service of meeting tangible clinical needs. We offer specific recommendations for a series of stakeholders in the field, from ML researchers and clinicians, to the institutions in which they work, and the governments which regulate their data access.

show abstract

Section: Establishing Consistent Responsibility Standardsmentioning

confidence: 99%

Section: Moving On From Clinically Unimportant Metricsmentioning

confidence: 99%

Machine learning for healthcare that matters: Reorienting from technical novelty to equitable impact

Balagopalan,

Baldini,

Celi

et al. 2024

PLOS Digit Health

View full text Add to dashboard Cite

show abstract

“…Machine learning methods for predicting opioid use disorder have been reviewed [ 26 ], proving to be reliable, but it is crucial to integrate multiple data types, as well as identify relevant features or variables, to enhance predictive accuracy.…”

Section: Opioids Beyond the ‘Perfect Storm’mentioning

confidence: 99%

Ethical issues in pain and palliation

Cascella,

Laudani,

Scarpati

et al. 2024

Current Opinion in Anaesthesiology

View full text Add to dashboard Cite

Purpose of review Increased public awareness of ethical issues in pain and palliative care, along with patient advocacy groups, put pressure on healthcare systems and professionals to address these concerns. Our aim is to review the ethics dilemmas concerning palliative care in ICU, artificial intelligence applications in pain therapy and palliative care, and the opioids epidemics. Recent findings In this focus review, we highlighted state of the art papers that were published in the last 18 months, on ethical issues in palliative care within the ICU, artificial intelligence trajectories, and how opioids epidemics has impacted pain management practices (see Visual Abstract). Summary Palliative care in the ICU should involve a multidisciplinary team, to mitigate patients suffering and futility. Providing spiritual support in the ICU is an important aspect of holistic patient care too. Increasingly sophisticated tools for diagnosing and treating pain, as those involving artificial intelligence, might favour disparities in access, cause informed consent problems, and surely, they need prudence and reproducibility. Pain clinicians worldwide continue to face the ethical dilemma of prescribing opioids for patients with chronic noncancer pain. Balancing the need for effective pain relief with the risk of opioid misuse, addiction, and overdose is a very controversial task.

show abstract

“…However, the lack of details and transparency in creating ML models limits the usefulness of the research. Improvements in documentation and sharing of source code are recommended to advance in this crucial healthcare field [78].…”

Section: Postoperative Carementioning

confidence: 99%

The Role of Artificial Intelligence in Anterior Cruciate Ligament Injuries: Current Concepts and Future Perspectives

Andriollo,

Picchi,

Sangaletti

et al. 2024

Healthcare

View full text Add to dashboard Cite

The remarkable progress in data aggregation and deep learning algorithms has positioned artificial intelligence (AI) and machine learning (ML) to revolutionize the field of medicine. AI is becoming more and more prevalent in the healthcare sector, and its impact on orthopedic surgery is already evident in several fields. This review aims to examine the literature that explores the comprehensive clinical relevance of AI-based tools utilized before, during, and after anterior cruciate ligament (ACL) reconstruction. The review focuses on current clinical applications and future prospects in preoperative management, encompassing risk prediction and diagnostics; intraoperative tools, specifically navigation, identifying complex anatomic landmarks during surgery; and postoperative applications in terms of postoperative care and rehabilitation. Additionally, AI tools in educational and training settings are presented. Orthopedic surgeons are showing a growing interest in AI, as evidenced by the applications discussed in this review, particularly those related to ACL injury. The exponential increase in studies on AI tools applicable to the management of ACL tears promises a significant future impact in its clinical application, with growing attention from orthopedic surgeons.

show abstract

Machine learning for predicting opioid use disorder from healthcare data: A systematic review

Cited by 13 publications

References 25 publications

Machine learning for healthcare that matters: Reorienting from technical novelty to equitable impact

Machine learning for healthcare that matters: Reorienting from technical novelty to equitable impact

Ethical issues in pain and palliation

The Role of Artificial Intelligence in Anterior Cruciate Ligament Injuries: Current Concepts and Future Perspectives

Contact Info

Product

Resources

About