A partially observable Markov chain framework to estimate overdiagnosis risk in breast cancer screening: Incorporating uncertainty in patients adherence behaviors

Molani, Sevda; Madadi, Mahboubeh; Wilkes, Wesley

doi:10.1016/j.omega.2018.09.009

Cited by 29 publications

(20 citation statements)

References 37 publications

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“…Also, numerous other fertile research areas can be applied to study through mining textual data. Such areas include human behavior during disaster and the impact of behavioral signature [115], human behavior concerning resource management [116], patient behavior [117], and big data retrieval in social action [118].…”

Section: Discussionmentioning

confidence: 99%

Identification and Prediction of Human Behavior through Mining of Unstructured Textual Data

et al. 2020

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The identification of human behavior can provide useful information across multiple job spectra. Recent advances in applying data-based approaches to social sciences have increased the feasibility of modeling human behavior. In particular, studying human behavior by analyzing unstructured textual data has recently received considerable attention because of the abundance of textual data. The main objective of the present study was to discuss the primary methods for identifying and predicting human behavior through the mining of unstructured textual data. Of the 823 articles analyzed, 87 met the predefined inclusion criteria and were included in the literature review. Our results show that the included articles could be symmetrically classified into two groups. The first group of articles attempted to identify the leading indicators of human behavior in unstructured textual data. In this group, the data-based approaches had three main components: (1) collecting self-reported survey data, (2) collecting data from social media and extracting data features, and (3) applying correlation analysis to evaluate the relationship between two sets of data. In contrast, the second group focused on the accuracy of data-based approaches for predicting human behavior. In this group, the data-based approaches could be categorized into (1) approaches based on labeled unstructured textual data and (2) approaches based on unlabeled unstructured textual data. The review provides a comprehensive insight into unstructured textual data mining to identify and predict human behavior and personality traits.

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Section: Discussionmentioning

confidence: 99%

Identification and Prediction of Human Behavior through Mining of Unstructured Textual Data

et al. 2020

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“…Molani et al [34] developed POMC models to quantify the age and stage-specific overdiagnosis risks while considering the uncertainty in a patient's adherence behavior. Cevik et al [14] proposed a POMDP model to maximize the total expected QALYs of a patient when there is a constraint on the number of mammograms the patient can undergo.…”

Section: Relevant Literaturementioning

confidence: 99%

“…To validate the estimated parameters, we calculate i) the lifetime risk of developing breast cancer from the proposed model, ii) five-year and ten-year risks of developing breast cancer from the proposed model, and iii) lifetime mortality risk of breast cancer with some adjustments to the model proposed by Molani et al [34] to incorporate breast density.…”

Section: Parameters Estimation and Model Validationmentioning

confidence: 99%

“…Madadi et al [30] developed a discrete-time POMC model to evaluate mammography screening policies in terms of the expected QALYs and lifetime breast cancer mortality risk while incorporating the uncertainty in women’s adherence behaviors. Molani et al [34] developed POMC models to quantify the age and stage-specific overdiagnosis risks while considering the uncertainty in a patient’s adherence behavior. Cevik et al [14] proposed a POMDP model to maximize the total expected QALYs of a patient when there is a constraint on the number of mammograms the patient can undergo.…”

Section: Introductionmentioning

confidence: 99%

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Investigating the Effectiveness of Breast Cancer Supplemental Screening Considering Radiologists’ Bias

Molani

Madadi

Williams

2020

Preprint

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Breast density is known to increase breast cancer risk and decrease mammography screening sensitivity. Breast density notification laws (enacted in 38 states as of September 2020), require physicians to inform women with high breast density of these potential risks. The laws usually require healthcare providers to notify patients of the possibility of using more sensitive supplemental screening tests (e.g., ultrasound). Since the enactment of the laws, there have been controversial debates over i) their implementations due to the potential radiologists bias in breast density classification of mammogram images and ii) the necessity of supplemental screenings for all patients with high breast density. In this study, we formulate a finite-horizon, discrete-time partially observable Markov chain (POMC) to investigate the effectiveness of supplemental screening and the impact of radiologists’ bias on patients’ outcomes. We consider the conditional probability of eventually detecting breast cancer in early states given that the patient develops breast cancer in her lifetime as the primary and the expected number of supplemental tests as the secondary patient’s outcome. Our results indicate that referring patients to a supplemental test solely based on their breast density may not necessarily improve their health outcomes and other risk factors need to be considered when making such referrals. Additionally, average-skilled radiologists’ performances are shown to be comparable with the performance of a perfect radiologist (i.e., 100% accuracy in breast density classification). However, a significant bias in breast density classification (i.e., consistent upgrading or downgrading of breast density classes) can negatively impact a patient’s health outcomes.

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“…Studies based on statistical or stochastic models [13][14][15] have quantified the influence of various factors such as age, screening frequency and adherence behavior on the benefits and harmful effects of mammography screening based on different data sources or trials other than the CNBSS. Most of transition probabilities in these models were held constant or age-dependent, and thus did not include the effects of tumor heterogeneity across patients.…”

Section: Introductionmentioning

confidence: 99%

Is mammography screening beneficial: An individual-based stochastic model for breast cancer incidence and mortality

Adler

2020

PLoS Comput Biol

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The benefits of mammography screening have been controversial, with conflicting findings from various studies. We hypothesize that unmeasured heterogeneity in tumor aggressiveness underlies these conflicting results. Based on published data from the Canadian National Breast Screening Study (CNBSS), we develop and parameterize an individualbased mechanistic model for breast cancer incidence and mortality that tracks five stages of breast cancer progression and incorporates the effects of age on breast cancer incidence and all-cause mortality. The model accurately reproduces the reported outcomes of the CNBSS. By varying parameters, we predict that the benefits of mammography depend on the effectiveness of cancer treatment and tumor aggressiveness. In particular, patients with the most rapidly growing or potentially largest tumors have the highest benefit and least harm from the screening, with only a relatively small effect of age. However, the model predicts that confining mammography to populations with a high risk of acquiring breast cancer increases the screening benefit only slightly compared with the full population.

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A partially observable Markov chain framework to estimate overdiagnosis risk in breast cancer screening: Incorporating uncertainty in patients adherence behaviors

Cited by 29 publications

References 37 publications

Identification and Prediction of Human Behavior through Mining of Unstructured Textual Data

Identification and Prediction of Human Behavior through Mining of Unstructured Textual Data

Investigating the Effectiveness of Breast Cancer Supplemental Screening Considering Radiologists’ Bias

Is mammography screening beneficial: An individual-based stochastic model for breast cancer incidence and mortality

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