Biological Pathway Involvement in Melanoma Heterogeneity and Drug-induced Resistance

Tumors develop resistance to numerous drug therapies, and this remains a major obstacle in treating many types of non-surgical cancers. Melanoma provides a good model system for studying drug resistance in cancer due to its high propensity to incur resistance after a significant initial response to a drug. Genes that are highly expressed in melanoma cancer cells have been studied, but in order to further understand the collective function of these highly expressed genes we must analyze gene sets, or pathways. A single gene’s function is rarely independent of other genes, and pathway analysis takes this into account. Our objective is to simplify single-cell RNA sequence data to model pathways and pinpoint which unique pathways are up-regulated and down-regulated in drug resistant and nonresistant melanoma cell phenotypes. Identifying these important pathways provides a more accurate depiction of melanoma heterogeneity and informs us of the pathways that are likely to be effective targets for new drug therapies, bringing us closer to overcoming drug-induced resistance

Medical Care Expenditure Indexes: A Comparison of Indexes using MarketScan and Pharmetrics Data

In recent years, healthcare service utilization has undergone several shifts, having potentially important implications for the cost of medical care.

Measuring Health Care Costs of Individuals with Employer-Sponsored Health Insurance in the U.S.: A Comparison of Survey and Claims Data

As the core nationally representative health expenditure survey in the United States, the Medical Expenditure Panel Survey (MEPS) is increasingly being used by statistical agencies to track expenditures by disease. However, while MEPS provides a wealth of data, its small sample size precludes examination of spending on all but the most prevalent health conditions. To overcome this issue, statistical agencies have turned to other public data sources, such as Medicare and Medicaid claims data, when available. No comparable publicly available data exist for those with employer-sponsored insurance. While large proprietary claims databases may be an option, the relative accuracy of their spending estimates is not known. This study compared MEPS and MarketScan estimates of annual per person health care spending on individuals with employer-sponsored insurance coverage. Both total spending and the distribution of annual per person spending differed across the two data sources, with MEPS estimates 10 percent lower on average than estimates from MarketScan. These differences appeared to be a function of both underrepresentation of high expenditure cases and underestimation across the remaining distribution of spending.

Learning with Deictic Representation

Most reinforcement learning methods operate on propositional representations of the world state. Such representations are often intractably large and generalize poorly. Using a deictic representation is believed to be a viable alternative: they promise generalization while allowing the use of existing reinforcement-learning methods. Yet, there are few experiments on learning with deictic representations reported in the literature. In this paper we explore the effectiveness of two forms of deictic representation and a naive propositional representation in a simple blocks-world domain. We find, empirically, that the deictic representations actually worsen performance. We conclude with a discussion of possible causes of these results and strategies for more effective learning in domains with objects

Alternative Price Indexes for Medical Care: Evidence from the MEPS Survey

Spending on medical care is a large and growing component of GDP. There are wellknown measurement problems that are estimated to overstate inflation and understate real growth for this sector by as much as 1-1/2 percentage points per year. Because of its size, this would translate into an overstatement of inflation for the overall economy of about ¼ percentage point with an equal understatement in real GDP growth. In this paper, we use data from the Medical Expenditure Panel Survey to obtain new, more comprehensive estimates for this bias and to explore a possible adjustment to existing official price indexes. The MEPS data show an upward bias to price growth in this sector of 1 percentage point, which translates into an overstatement of overall inflation of .2 percentage point and an understatement of GDP growth of the same amount. We also find that an adjustment recently used in Bradley et al provides a useful approximation to the indexes advocated by health economists.

Real-time automated failure identification in the Control Center Complex (CCC)

A system which will provide real-time failure management support to the Space Station Freedom program is described. The system's use of a simplified form of model based reasoning qualifies it as an advanced automation system. However, it differs from most such systems in that it was designed from the outset to meet two sets of requirements. First, it must provide a useful increment to the fault management capabilities of the Johnson Space Center (JSC) Control Center Complex (CCC) Fault Detection Management system. Second, it must satisfy CCC operational environment constraints such as cost, computer resource requirements, verification, and validation, etc. The need to meet both requirement sets presents a much greater design challenge than would have been the case had functionality been the sole design consideration. The choice of technology, discussing aspects of that choice and the process for migrating it into the control center is overviewed

The different clinical faces of obstructive sleep apnoea: a cluster analysis.

To access publisher's full text version of this article click on the hyperlink at the bottom of the pageAlthough commonly observed in clinical practice, the heterogeneity of obstructive sleep apnoea (OSA) clinical presentation has not been formally characterised. This study was the first to apply cluster analysis to identify subtypes of patients with OSA who experience distinct combinations of symptoms and comorbidities. An analysis of baseline data from the Icelandic Sleep Apnoea Cohort (822 patients with newly diagnosed moderate-to-severe OSA) was performed. Three distinct clusters were identified. They were classified as the "disturbed sleep group" (cluster 1), "minimally symptomatic group" (cluster 2) and "excessive daytime sleepiness group" (cluster 3), consisting of 32.7%, 24.7% and 42.6% of the entire cohort, respectively. The probabilities of having comorbid hypertension and cardiovascular disease were highest in cluster 2 but lowest in cluster 3. The clusters did not differ significantly in terms of sex, body mass index or apnoea-hypopnoea index. Patients with OSA have different patterns of clinical presentation, which need to be communicated to both the lay public and the professional community with the goal of facilitating care-seeking and early identification of OSA. Identifying distinct clinical profiles of OSA creates a foundation for offering more personalised therapies in the future

Distinguishing Death from Disenrollment in Claims Data Using a Readily Implemented Machine Learning Algorithm

Background: The inability to identify dates of death in insurance claims data is a major limitation to retrospective claims based research. If not an outcome, death is a competing risk and poses a threat to validity when treated as non-informative right censoring. Objectives: We aim to develop a user-friendly public algorithm to predict death within the year of disenrollment using an administrative claims database. Methods: We identified adults (18+ years) with at least 2 years of continuous enrollment prior to disenrollment between 01/2007 and 01/2018. Leveraging unique linkages in addition to data that are typically unavailable in the publicly licensed data, we ascertained date of death from the Social Security Death Index, inpatient discharge status, and death indicators in the administrative data. Models including candidate predictors for age, sex, Census region, month of disenrollment, year of disenrollment, chronic condition indicators (components of the Elixhauser score), and prior healthcare utilization were estimated using used elastic net regression tuned by 5-fold cross-validation and final models evaluated in an independent testing set. Weighted analysis adjusts for rare outcome (i.e., class imbalance). Sensitivity, specificity, and ROC associated with various thresholds of predicted probability to classify death at disenrollment were calculated. Results: Overall, we identified 13,360,460 beneficiaries who disenrolled during the study period, with 5% of patients who died within the year of disenrollment. The strongest predictors of death were age at disenrollment, diagnosis of metastatic cancer in the year prior to death, and type of care received (e.g., inpatient stay, hospice care). Using a prediction threshold of 30%, the algorithm classified death at disenrollment with a sensitivity of 0.684 and specificity of 0.985 (ROC=0.97. At the same prediction threshold, the weighted algorithm classified death with a sensitivity of .947 and a specificity of 0.898 (ROC=.973). Conclusions: Our algorithm uses publicly defined chronic conditions and utilization patterns that are easy to implement in claims data and predicts death at disenrollment with high specificity and varying sensitivity depending on the chosen prediction threshold. Users can easily implement the algorithm and can choose the prediction threshold (balancing sensitivity and specificity) to meet the needs of the specific study at hand

External validation of a machine learning algorithm to distinguish death from disenrollment in claims data

Poster presentation from the 38th International Conference on Pharmacoepidemiology & Therapeutic Risk Managemen