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Medical advancements have been improving the quality of life, but are experiencing an exponential increase in information. Navigating this vast knowledge base is becoming increasingly difficult for doctors without the help of diagnostic assistance tools. Many theorize that advances in deep learning may be poised to transform the field of dermatology, either by helping a general practitioner make a simple dermatological diagnosis or by helping an experienced dermatologist better understand rare or difficult to diagnose skin conditions. While deep learning technology has shown remarkable success in many fields, it still frequently suffers from difficult to explain failures on what appear to be simple input stimuli. Further, these models are forced to make a categorical classification prediction even when an input sample is random noise. We introduce the concept of a "don't know" prediction for dermatological assistance. We firstly modify deep models by replacing neural network weights in the penultimate layer with both random variables and dropout during inference. Statistical ambiguity and entropy features are extracted from a skin lesion classifier and fed into an uncertainty detection model for final prediction estimates. We then present an alternative approach to use machine learning to learn the uncertainty features. Our methods utilize machine learned features without traditional hard-thresholding approaches while minimizing false-positives from skin-lesion inputs on our own skin-lesion dataset as well as the popular SD-198 skin-disease dataset.

Allergic rhinitis (AR) is a chronic disease with a substantial clinical and economic burden. This study estimated the potential budget impact (BI) associated with market entry of Sweet Vernal, Orchard, Perennial Rye, Timothy, and Kentucky Blue Grass Mixed Pollens Allergen Extract Tablet for Sublingual Use ('5-grass SLIT tablet') for patients aged 10-65 with grass pollen-induced AR.A budget impact model was constructed to estimate the potential BI from a US payer perspective. The model calculated pharmacy, medical, and total (pharmacy + medical) costs per-member-per-month (PMPM) with and without market entry of the 5-grass SLIT tablet, considering a 3-year time horizon. The target population was determined using an epidemiological approach and existing literature. The treatment market shares without 5-grass SLIT tablet entry were derived from an analysis of de-identified insurance claims data. Pharmacy costs and medical utilization rates and costs were obtained from the claims data analysis and existing literature. One-way sensitivities were conducted for key model inputs.Using an illustrative example of a hypothetical health plan with one million members, the estimated target population of AR patients aged 10-65 was 26,320. On a PMPM basis, pharmacy costs increased by $0.36, $0.44, and $0.51, while total costs (after medical cost offsets) increased by $0.15, $0.18, and $0.22 in the first, second, and third years following entry of the 5-grass SLIT tablet, respectively. Results were most sensitive to changes in the compliance rate, treatment duration, and price. The BI will vary from the base case example when alternative, payer-specific inputs are used.Using base case inputs, use of the 5-grass SLIT tablet to treat grass pollen-induced AR increased the pharmacy budget for a hypothetical third-party payer. Higher pharmacy costs were partially offset by lower medical budget due to reduced resource use compared with existing treatments.

To estimate the incremental change in pharmacy per-member-permonth (PMPM) costs, according to various formulary designs, for a new interferon beta-1a product (IB1a2) using administrative claims data.Cross-sectional sex- and age-specific disease prevalence and treatment rates for relapsing, remitting multiple sclerosis (RRMS) patients were measured using integrated medical and pharmacy claims data from a 500,000- member employer group in the southern United States. Migration to IB1a2 from other drugs in the class was based on market-share data for new and existing RRMS patients. Duration of therapy was estimated by analyzing claims for current RRMS therapies. Daily therapy cost was provided by the manufacturer of IB1a2, adjusted for migration from other therapies, and multiplied by estimated volume to predict incremental and total PMPM cost impact. Market-share estimates were used to develop a PMPM cost forecast for the next 2 years. PMPM cost estimates were calculated for preferred (copayment tier 2) and nonpreferred (copayment tier 3) formulary designs with and without prior authorization (PA). One-way sensitivity analysis was performed to assess the influence of product pricing, duration of therapy, and other market factors.Annual incremental PMPM change was $0.047 for the scenario of third copayment tier with PA. The incremental change was greatest for those aged 55 to 65 years ($0.056 PMPM) and did not vary greatly by benefit design. Duration of therapy had the greatest impact on the PMPM estimate across benefit designs.IB1a2 will not cause a significant change in managed care pharmacy budgets under a variety of formulary conditions, according to this crosssectional analysis of current care-seeking behavior by RRMS patients. Economic impact may differ if IB1a2 expands RRMS patients. treatment-seeking behavior.