Performance evaluation of a prescription medication image classification model: an observational cohort.
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ABSTRACT:
SUBMITTER: Lester CA
PROVIDER: S-EPMC8316316 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:ObjectiveTo develop key performance indicators that evaluate the effectiveness of a prescription medication system.MethodsA modified RAND/UCLA appropriateness method was used to develop key performance indicators (KPIs) for a prescription medication system. A broad list of potential KPIs was compiled. A multidisciplinary group composed of 21 experts rated the potential KPIs. A face-to-face meeting was held following the first rating exercise to discuss each potential KPI individually. The expert panel undertook a final rating of KPIs. The final set of KPIs were those indicators where at least 80 percent of experts rated the indicator highly i.e. rating of ≥ 7 on a scale from 1 to 9.Results292 KPIs were identified from the published literature. After removing duplicates and combining similar indicators 71 KPIs were included. The final ranking resulted in six indicators being ranked 7 or higher by 80% of the respondents and an additional seven indicators being ranked 7 or higher by ≥70 but ≤80% of respondents. The six selected indicators include four specific disease areas, measure structural and process aspects of health service delivery, and assessed three of the domains of healthcare quality: efficiency, effectiveness, and safety.ConclusionsThese indicators are recommended as a starting point to assess the current performance of prescription medication systems. Consideration should be given to developing indicators in additional disease areas as well as indicators that measure the domains of timeliness and patient-centeredness. Future work should focus on the feasibility of measuring these indicators.
| S-EPMC6333341 | biostudies-literature
Project description:ObjectiveThe increasing global prevalence of obesity, coupled with its association with chronic health conditions and rising healthcare costs, highlights the need for effective interventions; however, despite the availability of treatment options, the ongoing success of primary interventions in maintaining long-term weight loss remains limited. This study examined the prescription medication dispensing changes following sleeve gastrectomy in Australians aged 45 years and over.MethodsIn a retrospective analysis of 847 bariatric surgery patients from the New South Wales 45 and Up Study, the assessment of medication patterns categorizing into three groups: gastrointestinal, metabolic, cardiorespiratory, musculoskeletal, and nervous systems was conducted. Each drug class was analyzed, focusing on patients with dispensing records within the 12 months before surgery. This study employed interrupted time-series analysis to compare pre- and post-surgery medication usage.ResultsWith a predominantly female population (76.9%) and an average age of 57.2 (standard deviation 5.71), there were statistically significant reductions in both unique medications (12.5% decrease, p = 0.004) and total medications dispensed (15.9% decrease, p = 0.003) from 12 months before surgery to 13-24 months after bariatric surgery. All medication categories, except opioids, showed reductions. Notably, the most significant reductions were observed in diabetes (38.6%), agents acting on the renin-angiotensin system (40.4%), lipid modifying agents (26.5%), anti-inflammatory products (46.3%), and obstructive airway diseases (53.3%) medications during this time frame.ConclusionThese findings suggest that sleeve gastrectomy provides an effective therapeutic intervention for patients with comorbidities requiring multiple medications, especially for obesity-related diseases such as diabetes, cardiovascular, respiratory and musculoskeletal disorders.
| S-EPMC10863745 | biostudies-literature
Project description:In medical image classification tasks, it is common to find that the number of normal samples far exceeds the number of abnormal samples. In such class-imbalanced situations, reliable training of deep neural networks continues to be a major challenge, therefore biasing the predicted class probabilities toward the majority class. Calibration has been proposed to alleviate some of these effects. However, there is insufficient analysis explaining whether and when calibrating a model would be beneficial. In this study, we perform a systematic analysis of the effect of model calibration on its performance on two medical image modalities, namely, chest X-rays and fundus images, using various deep learning classifier backbones. For this, we study the following variations: (i) the degree of imbalances in the dataset used for training; (ii) calibration methods; and (iii) two classification thresholds, namely, default threshold of 0.5, and optimal threshold from precision-recall (PR) curves. Our results indicate that at the default classification threshold of 0.5, the performance achieved through calibration is significantly superior (p < 0.05) to using uncalibrated probabilities. However, at the PR-guided threshold, these gains are not significantly different (p > 0.05). This observation holds for both image modalities and at varying degrees of imbalance. The code is available at https://github.com/sivaramakrishnan-rajaraman/Model_calibration.
| S-EPMC8794113 | biostudies-literature
Project description:The aim of this study was to examine the prescribing patterns of medications quantified by the performance measures for acute myocardial infarction (AMI).Current performance measures for AMI are designed to improve quality by quantifying the use of evidence-based treatments. However, these measures only assess medication prescription. Whether patients receive optimal dosing of secondary prevention medications at the time of and after discharge after AMI is unknown.We assessed treatment doses of beta-blockers, statins, and angiotensin-converting enzyme inhibitors (ACEI)/angiotensin II receptor blockers (ARBs) at discharge and 12 months after AMI among 6,748 patients from 31 hospitals enrolled in 2 U.S. registries (2003 to 2008). Prescribed doses were categorized as none, low (<50% target [defined from seminal clinical trials]), moderate (50% to 74% target), or goal (? 75% target). Patients with contraindications were excluded from analyses for that medication.Most eligible patients (>87%) were prescribed some dose of each medication at discharge, although only 1 in 3 patients were prescribed these medications at goal doses. Of patients not discharged on goal doses, up-titration during follow-up occurred infrequently (approximately 25% of patients for each medication). At 12 months, goal doses of beta-blockers, statins, and ACEI/ARBs were achieved in only 12%, 26%, and 32% of eligible patients, respectively. After multivariable adjustment, prescription of goal dose at discharge was strongly associated with being at goal dose at follow-up: beta-blockers, adjusted odds ratio (OR): 6.08 (95% confidence interval [CI]: 3.70 to 10.01); statins, adjusted OR: 8.22 (95% CI: 6.20 to 10.90); ACEI/ARBs, adjusted OR: 5.80 (95% CI: 2.56 to 13.16); p < 0.001 for each.Although nearly all patients after an AMI are discharged on appropriate secondary prevention medications, dose increases occur infrequently, and most patients are prescribed doses below those with proven efficacy in clinical trials. Integration of dose intensity into performance measures might help improve the use of optimal medical therapy after AMI.
| S-EPMC3819453 | biostudies-literature
Project description:Raster image correlation spectroscopy (RICS) is a fluorescence image analysis method for extracting the mobility, concentration, and stoichiometry of diffusing fluorescent molecules from confocal image stacks. The method works by calculating a spatial correlation function for each image and analyzing the average of those by model fitting. Rules of thumb exist for RICS image acquisitioning, yet a rigorous theoretical approach to predict the accuracy and precision of the recovered parameters has been lacking. We outline explicit expressions to reveal the dependence of RICS results on experimental parameters. In terms of imaging settings, we observed that a twofold decrease of the pixel size, e.g., from 100 to 50 nm, decreases the error on the translational diffusion constant (D) between three- and fivefold. For D = 1 ?m2 s-1, a typical value for intracellular measurements, ?25-fold lower mean-squared relative error was obtained when the optimal scan speed was used, although more drastic improvements were observed for other values of D. We proposed a slightly modified RICS calculation that allows correcting for the significant bias of the autocorrelation function at small (?50 × 50 pixels) sizes of the region of interest. In terms of sample properties, at molecular brightness E = 100 kHz and higher, RICS data quality was sufficient using as little as 20 images, whereas the optimal number of frames for lower E scaled pro rata. RICS data quality was constant over the nM-?M concentration range. We developed a bootstrap-based confidence interval of D that outperformed the classical least-squares approach in terms of coverage probability of the true value of D. We validated the theory via in vitro experiments of enhanced green fluorescent protein at different buffer viscosities. Finally, we outline robust practical guidelines and provide free software to simulate the parameter effects on recovery of the diffusion coefficient.
| S-EPMC7018992 | biostudies-literature
Project description:We demonstrate that GPT-4V(ision), a large multimodal model, exhibits strong one-shot learning ability, generalizability, and natural language interpretability in various biomedical image classification tasks, including classifying cell types, tissues, cell states, and disease status. Such features resemble human-like performance and distinguish GPT-4V from conventional image classification methods, which typically require large cohorts of training data and lack interpretability.
| S-EPMC10802384 | biostudies-literature
Project description:BackgroundPrescription medication (PM) misuse/abuse has emerged as a national crisis in the United States, and social media has been suggested as a potential resource for performing active monitoring. However, automating a social media-based monitoring system is challenging-requiring advanced natural language processing (NLP) and machine learning methods. In this paper, we describe the development and evaluation of automatic text classification models for detecting self-reports of PM abuse from Twitter.MethodsWe experimented with state-of-the-art bi-directional transformer-based language models, which utilize tweet-level representations that enable transfer learning (e.g., BERT, RoBERTa, XLNet, AlBERT, and DistilBERT), proposed fusion-based approaches, and compared the developed models with several traditional machine learning, including deep learning, approaches. Using a public dataset, we evaluated the performances of the classifiers on their abilities to classify the non-majority "abuse/misuse" class.ResultsOur proposed fusion-based model performs significantly better than the best traditional model (F1-score [95% CI]: 0.67 [0.64-0.69] vs. 0.45 [0.42-0.48]). We illustrate, via experimentation using varying training set sizes, that the transformer-based models are more stable and require less annotated data compared to the other models. The significant improvements achieved by our best-performing classification model over past approaches makes it suitable for automated continuous monitoring of nonmedical PM use from Twitter.ConclusionsBERT, BERT-like and fusion-based models outperform traditional machine learning and deep learning models, achieving substantial improvements over many years of past research on the topic of prescription medication misuse/abuse classification from social media, which had been shown to be a complex task due to the unique ways in which information about nonmedical use is presented. Several challenges associated with the lack of context and the nature of social media language need to be overcome to further improve BERT and BERT-like models. These experimental driven challenges are represented as potential future research directions.
| S-EPMC7835447 | biostudies-literature
Project description:ObjectiveDelirium is a common condition associated with increased morbidity and mortality. Medication side effects are a possible source of modifiable delirium risk and provide an opportunity to improve delirium predictive models. This study characterized the risk for delirium diagnosis by applying a previously validated algorithm for calculating central nervous system adverse effect burden arising from a full medication list.MethodUsing a cohort of hospitalized adult (age 18-65) patients from the Massachusetts All-Payers Claims Database, we calculated medication burden following hospital discharge and characterized risk of new coded delirium diagnosis over the following 90 days. We applied the resulting model to a held-out test cohort.ResultsThe cohort included 62,180 individuals of whom 1.6% (1019) went on to have a coded delirium diagnosis. In the training cohort (43,527 individuals), the medication burden feature was positively associated with delirium diagnosis (OR = 5.75, 95% CI 4.34-7.63) and this association persisted (aOR = 1.95; 1.31-2.92) after adjusting for demographics, clinical features, prescribed medications, and anticholinergic risk score. In the test cohort, the trained model produced an area under the curve of 0.80 (0.78-0.82). This performance was similar across subgroups of age and gender.ConclusionAggregating brain-related medication adverse effects facilitates identification of individuals at high risk of subsequent delirium diagnosis.
| S-EPMC8249339 | biostudies-literature
Project description:Classification methods used in microarray studies for gene expression are diverse in the way they deal with the underlying complexity of the data, as well as in the technique used to build the classification model. The MAQC II study on cancer classification problems has found that performance was affected by factors such as the classification algorithm, cross validation method, number of genes, and gene selection method. In this paper, we study the hypothesis that the disease under study significantly determines which method is optimal, and that additionally sample size, class imbalance, type of medical question (diagnostic, prognostic or treatment response), and microarray platform are potentially influential. A systematic literature review was used to extract the information from 48 published articles on non-cancer microarray classification studies. The impact of the various factors on the reported classification accuracy was analyzed through random-intercept logistic regression. The type of medical question and method of cross validation dominated the explained variation in accuracy among studies, followed by disease category and microarray platform. In total, 42% of the between study variation was explained by all the study specific and problem specific factors that we studied together.
| S-EPMC4000205 | biostudies-literature
Project description:Background: Prescription medication sharing is an inappropriate medication use behavior that can lead to medication errors and adverse drug events, posing a public health threat. The reported prevalence of prescription medication lending and borrowing varies by country, ranging from 6%-23% and 5%-52%, respectively. However, research on medication sharing is scant in Asian countries. Therefore, this study aimed to describe the rate of prescription medication sharing practices and investigate the associated behavioral factors, types of shared medications, and reasons for sharing among adults in South Korea. Methods: A cross-sectional study was conducted using an online self-administered survey of 1,000 adults (aged 19-69 years; November 2020). A stratified sampling method was used to select survey participants from a nationwide consumer panel, which ensured a representative distribution of the Korean population by age, gender, and region. Descriptive and logistic regression analyses were used to evaluate the information related to sharing behavior. Results: A total of 1,000 respondents participated in this study. The mean age of the respondents was 44.7 years (standard deviation [SD], 13.4), ranging from 20 to 69 years. The rate of medication sharing was 52.4%. The most prevalently shared medications were analgesic, antipyretic, and antimigraine medications. Prescription medications were shared mostly between family and relatives. Older age was a predictive factor for sharing analgesics. Lower educational level was a predictive factor for sharing ophthalmic medications. Conclusions: Approximately one in two respondents in our study have experienced medication sharing in their lifetime. Future studies are needed to establish evidence-based strategies for patient education and improve the medication use process. Healthcare professionals should assess patients' needs for accessing medications and be ready to educate and guide them with specific action plans. Policymakers should consider patient empowerment strategies including public education and campaigns to avoid potential adverse outcomes of medication sharing.
| S-EPMC8854370 | biostudies-literature