Decision curve analysis as a framework to estimate the potential value of screening or other decision‐making aids

Martin, Michael S.; Wells, George A.; Crocker, Anne G.; Potter, Beth K.; Colman, Ian

doi:10.1002/mpr.1601

Cited by 3 publications

(8 citation statements)

References 48 publications

(67 reference statements)

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“…While others have observed that individuals of an ethnic or racial minority are more likely to have a complex pathway to mental health care, including the police or other parts of the justice system (which could result in a higher prevalence), [47][48][49] a lower prevalence of mental illness among minority inmates (13%) than white (25%) or Aboriginal (23%) inmates was previously reported. 30 We did not observe other hypothesized ethnic disparities. For example, similar treatment access by white and Indigenous inmates is inconsistent with findings indicating lower treatment rates by Indigenous persons in New Zealand prisons.…”

Section: Discussioncontrasting

confidence: 53%

Section: Discussionmentioning

confidence: 99%

“…Our prior work 30 found that screening in Canadian prisons performs at sensitivity (75%) and specificity (71%) that are comparable to the best-studied tools for use with prisoners. 15,31 However, while 79% of inmates with mental illness had contact with a mental health professional, only 46% received sustained treatment, 32 a figure that is comparable to access rates internationally.…”

mentioning

confidence: 87%

“…For example, newly admitted inmates at intake units (as opposed to regular prison wings) reported higher rates of preferring to care for themselves, unawareness of how to access services, and worries about how other inmates and staff would perceive them. 29 Our prior work 30 found that screening in Canadian prisons performs at sensitivity (75%) and specificity (71%) that are comparable to the best-studied tools for use with prisoners. 15,31 However, while 79% of inmates with mental illness had contact with a mental health professional, only 46% received sustained treatment, 32 a figure that is comparable to access rates internationally.…”

mentioning

confidence: 96%

“…15,31 However, while 79% of inmates with mental illness had contact with a mental health professional, only 46% received sustained treatment, 32 a figure that is comparable to access rates internationally. 8 –10 This may be caused in part by lower sensitivity of screening among those who did not self-report receiving treatment immediately prior to their incarceration and for racial minorities 30 ; sex, age, and regional differences were not considered. Barriers to care discussed previously may also explain why roughly one-third of those with clinical contact did not remain in treatment.…”

mentioning

confidence: 99%

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Mental Health Screening and Differences in Access to Care among Prisoners

et al. 2018

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Section: Discussioncontrasting

confidence: 53%

Section: Discussionmentioning

confidence: 99%

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confidence: 87%

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confidence: 96%

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confidence: 99%

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Mental Health Screening and Differences in Access to Care among Prisoners

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Two machine learning-based nomogram to predict risk and prognostic factors for liver metastasis from pancreatic neuroendocrine tumors: a multicenter study

Li¹,

Huang²,

Liao³

et al. 2023

BMC Cancer

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Background Pancreatic neuroendocrine tumors (PNETs) are one of the most common endocrine tumors, and liver metastasis (LMs) are the most common location of metastasis from PNETS; However, there is no valid nomogram to predict the diagnosis and prognosis of liver metastasis (LMs) from PNETs. Therefore, we aimed to develop a valid predictive model to aid physicians in making better clinical decisions. Methods We screened patients in the Surveillance, Epidemiology, and End Results (SEER) database from 2010–2016. Feature selection was performed by machine learning algorithms and then models were constructed. Two nomograms were constructed based on the feature selection algorithm to predict the prognosis and risk of LMs from PNETs. We then used the area under the curve (AUC), receiver operating characteristic (ROC) curve, calibration plot and consistency index (C-index) to evaluate the discrimination and accuracy of the nomograms. Kaplan-Meier (K-M) survival curves and decision curve analysis (DCA) were also used further to validate the clinical efficacy of the nomograms. In the external validation set, the same validation is performed. Results Of the 1998 patients screened from the SEER database with a pathological diagnosis of PNET, 343 (17.2%) had LMs at the time of diagnosis. The independent risk factors for the occurrence of LMs in PNET patients included histological grade, N stage, surgery, chemotherapy, tumor size and bone metastasis. According to Cox regression analysis, we found that histological subtype, histological grade, surgery, age, and brain metastasis were independent prognostic factors for PNET patients with LMs. Based on these factors, the two nomograms demonstrated good performance in model evaluation. Conclusion We developed two clinically significant predictive models to aid physicians in personalized clinical decision-makings.

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Care for the Mind Amid Chronic Diseases: An Interpretable AI Approach Using IoT

Xie¹,

Zhao²,

Liu³

et al. 2022

Preprint

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Health sensing for chronic disease management creates immense benefits for social welfare. Existing health sensing studies primarily focus on the prediction of physical chronic diseases.Depression, a widespread complication of chronic diseases, is however understudied. We draw on the medical literature to support depression prediction using motion sensor data. To connect human expertise in the decision-making, safeguard trust for this high-stake prediction, and ensure algorithm transparency, we develop an interpretable deep learning model: Temporal Prototype Network (TempPNet). TempPNet is built upon the emergent prototype learning models. To accommodate the temporal characteristic of sensor data and the progressive property of depression, TempPNet differs from existing prototype learning models in its capability of capturing the temporal progression of depression. Extensive empirical analyses using real-world motion sensor data show that TempPNet outperforms state-of-the-art benchmarks in depression prediction. Moreover, TempP-Net interprets its predictions by visualizing the temporal progression of depression and its corresponding symptoms detected from sensor data. We further conduct a user study to demonstrate its superiority over the benchmarks in interpretability. This study offers an algorithmic solution for impactful social good -collaborative care of chronic diseases and depression in health sensing.Methodologically, it contributes to extant literature with a novel interpretable deep learning model for depression prediction from sensor data. Patients, doctors, and caregivers can deploy our model on mobile devices to monitor patients' depression risks in real-time. Our model's interpretability also allows human experts to participate in the decision-making by reviewing the interpretation of prediction outcomes and making informed interventions.

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Decision curve analysis as a framework to estimate the potential value of screening or other decision‐making aids

Abstract: Although screening has potential to increase detection of treatment, important subgroup differences exist. Greater consideration of responses to positive screens or alternatives to screening are needed to maximize the impact of efforts to improve detection and treatment of mental illness.

Cited by 3 publications

References 48 publications

Mental Health Screening and Differences in Access to Care among Prisoners

Mental Health Screening and Differences in Access to Care among Prisoners

Two machine learning-based nomogram to predict risk and prognostic factors for liver metastasis from pancreatic neuroendocrine tumors: a multicenter study

Care for the Mind Amid Chronic Diseases: An Interpretable AI Approach Using IoT

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