scholarly journals Predicting the Cost and Pace of Pharmacogenomic Advances: An Evidence-Based Study

2013 ◽  
Vol 59 (4) ◽  
pp. 649-657 ◽  
Author(s):  
Ramy Arnaout ◽  
Thomas P Buck ◽  
Paulvalery Roulette ◽  
Vikas P Sukhatme
Keyword(s):  
Monte Carlo Model ◽  
Adverse Outcomes ◽  
Data Driven ◽  
Genomic Research ◽  
Limiting Factor ◽  
Single Digit ◽  
Genomic Variants ◽  
Prescription Drug Use ◽  
Priming Phase ◽  
The Cost

BACKGROUND Adverse outcomes associated with prescription drug use are common and costly. Many adverse outcomes can be avoided through pharmacogenomics: choosing and dosing of existing drugs according to a person's genomic variants. Finding and validating associations between outcomes and genomic variants and developing guidelines for avoiding drug-related adverse outcomes will require further research; however, no data-driven estimates yet exist for the time or money required for completing this research. METHODS We identified examples of associations between adverse outcomes and genomic variants. We used these examples to estimate the time and money required to identify and confirm other associations, including the cost of failures, and to develop and validate pharmacogenomic dosing guidelines for them. We built a Monte Carlo model to estimate the time and financial costs required to cut the overall rate of drug-related adverse outcomes by meaningful amounts. We analyzed the model's predictions for a broad range of assumptions. RESULTS AND CONCLUSIONS Our model projected that the development of guidelines capable of cutting overall drug-related adverse outcomes by 25%–50% with current approaches will require investment of single-digit billions of dollars and take 20 years. The model forecasts a pump-priming phase of 5–7 years, which would require expenditures of hundreds of millions of dollars, with little apparent return on investment. The single most important parameter was the extent to which genomic variants cause adverse outcomes. The size of the labor force was not a limiting factor. A “50 000 Pharmacogenomes Project” could speed progress. Our approach provides a template for other areas of genomic research.

scholarly journals Machine learning applied to simulations of collisions between rotating, differentiated planets

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Miles L. Timpe ◽  
Maria Han Veiga ◽  
Mischa Knabenhans ◽  
Joachim Stadel ◽  
Stefano Marelli
Keyword(s):  
Machine Learning ◽  
Planet Formation ◽  
Critical Role ◽  
Ensemble Methods ◽  
Terrestrial Planet ◽  
Data Driven ◽  
Analytic Methods ◽  
Accurate Treatment ◽  
Post Impact ◽  
The Cost

AbstractIn the late stages of terrestrial planet formation, pairwise collisions between planetary-sized bodies act as the fundamental agent of planet growth. These collisions can lead to either growth or disruption of the bodies involved and are largely responsible for shaping the final characteristics of the planets. Despite their critical role in planet formation, an accurate treatment of collisions has yet to be realized. While semi-analytic methods have been proposed, they remain limited to a narrow set of post-impact properties and have only achieved relatively low accuracies. However, the rise of machine learning and access to increased computing power have enabled novel data-driven approaches. In this work, we show that data-driven emulation techniques are capable of classifying and predicting the outcome of collisions with high accuracy and are generalizable to any quantifiable post-impact quantity. In particular, we focus on the dataset requirements, training pipeline, and classification and regression performance for four distinct data-driven techniques from machine learning (ensemble methods and neural networks) and uncertainty quantification (Gaussian processes and polynomial chaos expansion). We compare these methods to existing analytic and semi-analytic methods. Such data-driven emulators are poised to replace the methods currently used in N-body simulations, while avoiding the cost of direct simulation. This work is based on a new set of 14,856 SPH simulations of pairwise collisions between rotating, differentiated bodies at all possible mutual orientations.

scholarly journals Cost–effectiveness and cost-benefit analysis of oliceridine in the treatment of acute pain

2021 ◽  
Author(s):  
Kit N Simpson ◽  
Michael J Fossler ◽  
Linda Wase ◽  
Mark A Demitrack
Keyword(s):  
Cost Benefit Analysis ◽  
Cost Savings ◽  
Cost Benefit ◽  
Cost Effective ◽  
Adverse Outcomes ◽  
Protein Signaling ◽  
Opioid Receptor Agonist ◽  
Cost Effective Alternative ◽  
Μ Opioid Receptor ◽  
The Cost

Aim: Oliceridine, a new class of μ-opioid receptor agonist, is selective for G-protein signaling (analgesia) with limited recruitment of β-arrestin (associated with adverse outcomes) and may provide a cost-effective alternative versus conventional opioid morphine for postoperative pain. Patients & methods: Using a decision tree with a 24-h time horizon, we calculated costs for medication and management of three most common adverse events (AEs; oxygen saturation <90%, vomiting and somnolence) following postoperative oliceridine or morphine use. Results: Using oliceridine, the cost for managing AEs was US$528,424 versus $852,429 for morphine, with a net cost savings of $324,005. Conclusion: Oliceridine has a favorable overall impact on the total cost of postoperative care compared with the use of the conventional opioid morphine.

scholarly journals A Cost-Sensitive Diagnosis Method Based on the Operation and Maintenance Data of UAV

2021 ◽  
Vol 11 (23) ◽  
pp. 11116
Author(s):  
Ke Zheng ◽  
Guozhu Jia ◽  
Linchao Yang ◽  
Chunting Liu
Keyword(s):  
Fault Diagnosis ◽  
Data Distribution ◽  
Imbalanced Data ◽  
Data Driven ◽  
Total Cost ◽  
Operation And Maintenance ◽  
Diagnosis Method ◽  
Overall Performance ◽  
Diagnosis Model ◽  
The Cost

In the fault diagnosis of UAVs, extremely imbalanced data distribution and vast differences in effects of fault modes can drastically affect the application effect of a data-driven fault diagnosis model under the limitation of computing resources. At present, there is still no credible approach to determine the cost of the misdiagnosis of different fault modes that accounts for the interference of data distribution. The performance of the original cost-insensitive flight data-driven fault diagnosis models also needs to be improved. In response to this requirement, this paper proposes a two-step ensemble cost-sensitive diagnosis method based on the operation and maintenance data of UAV. According to the fault criticality from FMECA information, we defined a misdiagnosis hazard value and calculated the misdiagnosis cost. By using the misdiagnosis cost, a static cost matrix could be set to modify the diagnosis model and to evaluate the performance of the diagnosis results. A two-step ensemble cost-sensitive method based on the MetaCost framework was proposed using stratified bootstrapping, choosing LightGBM as meta-classifiers, and adjusting the ensemble form to enhance the overall performance of the diagnosis model and reduce the occupation of the computing resources while optimizing the total misdiagnosis cost. The experimental results based on the KPG component data of a large fixed-wing UAV show that the proposed cost-sensitive model can effectively reduce the total cost incurred by misdiagnosis, without putting forward excessive requirements on the computing equipment under the condition of ensuring a certain overall level of diagnosis performance.

Manual Assembly of 400um Bumped-Die GaN Power Semiconductor Devices

2012 ◽  
Vol 2012 (1) ◽  
pp. 000514-000523
Author(s):  
Stephan W. Henning ◽  
Luke Jenkins ◽  
Sidni Hale ◽  
Christopher G. Wilson ◽  
John Tennant ◽  
...  
Keyword(s):  
Semiconductor Devices ◽  
Thermal Dissipation ◽  
Single Digit ◽  
Manual Assembly ◽  
Power Semiconductors ◽  
Power Semiconductor ◽  
Residue Removal ◽  
Power Semiconductor Devices ◽  
Package Size ◽  
The Cost

Until recently, power semiconductors were usually produced as TO, power-PAK, and D-PAK style packaging, due to die size, thermal dissipation requirements, and the vertical flow of current through the devices. The introduction of GaN to power semiconductors has allowed manufactures to produce devices with approximately 9% the footprint of similar rated D-PAK Si MOSFETs. In addition, GaN semiconductors have much better theoretical limits of specific on-resistance to breakdown voltage, when compared to Si and SiC. As of now, GaN devices offer very good performance at much less the cost of SiC, very small footprints, no reverse recovery losses of a body diode, very low RDS(ON), and very fast turn-on and turn-off times due to QGS in single-digit nC range. GaN semiconductors are expected to make vast improvements over the next decade. Unfortunately, this decrease in package size has made design prototyping significantly more challenging. Traditional manual solder iron assembly is not sufficient for these devices. Difficulties include board design, device handling, alignment, solder reflow, flux residue removal, and post-assembly inspection. The EPC 2014 and 2015 devices both have a 4mm pitch and are 1.85mm2 and 6.70mm2, respectively. In many situations, the decreased pitch and small overall size of these devices mandate the use of automated assembly equipment, such as a pick & place, to ensure quality and repeatability of assembly. However, this may not be feasible for initial prototyping, due to cost and time constraints. Here we will present a technique for manual assembly of these chip scale devices, applied specifically to the EPC 2014 and 2015. This should decrease the cost and turn time for prototype assembly when utilizing these types of chip scale packaged power semiconductor devices.

scholarly journals Sepsis in the era of data-driven medicine: personalizing risks, diagnoses, treatments and prognoses

2019 ◽  
Vol 21 (4) ◽  
pp. 1182-1195
Author(s):  
Andrew C Liu ◽  
Krishna Patel ◽  
Ramya Dhatri Vunikili ◽  
Kipp W Johnson ◽  
Fahad Abdu ◽  
...  
Keyword(s):  
At Risk ◽  
Data Science ◽  
Drug Repositioning ◽  
Healthcare Delivery ◽  
Heterogeneous Data ◽  
Machine Intelligence ◽  
Adverse Outcomes ◽  
Data Driven ◽  
Biomedical Data ◽  
Prolonged Stay

Abstract Sepsis is a series of clinical syndromes caused by the immunological response to infection. The clinical evidence for sepsis could typically attribute to bacterial infection or bacterial endotoxins, but infections due to viruses, fungi or parasites could also lead to sepsis. Regardless of the etiology, rapid clinical deterioration, prolonged stay in intensive care units and high risk for mortality correlate with the incidence of sepsis. Despite its prevalence and morbidity, improvement in sepsis outcomes has remained limited. In this comprehensive review, we summarize the current landscape of risk estimation, diagnosis, treatment and prognosis strategies in the setting of sepsis and discuss future challenges. We argue that the advent of modern technologies such as in-depth molecular profiling, biomedical big data and machine intelligence methods will augment the treatment and prevention of sepsis. The volume, variety, veracity and velocity of heterogeneous data generated as part of healthcare delivery and recent advances in biotechnology-driven therapeutics and companion diagnostics may provide a new wave of approaches to identify the most at-risk sepsis patients and reduce the symptom burden in patients within shorter turnaround times. Developing novel therapies by leveraging modern drug discovery strategies including computational drug repositioning, cell and gene-therapy, clustered regularly interspaced short palindromic repeats -based genetic editing systems, immunotherapy, microbiome restoration, nanomaterial-based therapy and phage therapy may help to develop treatments to target sepsis. We also provide empirical evidence for potential new sepsis targets including FER and STARD3NL. Implementing data-driven methods that use real-time collection and analysis of clinical variables to trace, track and treat sepsis-related adverse outcomes will be key. Understanding the root and route of sepsis and its comorbid conditions that complicate treatment outcomes and lead to organ dysfunction may help to facilitate identification of most at-risk patients and prevent further deterioration. To conclude, leveraging the advances in precision medicine, biomedical data science and translational bioinformatics approaches may help to develop better strategies to diagnose and treat sepsis in the next decade.

scholarly journals Reducing the Energy Budget in WSN Using Time Series Models

2020 ◽  
Vol 2020 ◽  
pp. 1-15 ◽  
Author(s):  
Felicia Engmann ◽  
Ferdinand Apietu Katsriku ◽  
Jamal-Deen Abdulai ◽  
Kofi Sarpong Adu-Manu
Keyword(s):  
Data Transmission ◽  
Energy Savings ◽  
Data Driven ◽  
Wireless Sensor ◽  
Time Series Models ◽  
Duty Cycling ◽  
Data Driven Approach ◽  
Save Energy ◽  
The Cost ◽  
Prediction Approach

Energy conservation is critical in the design of wireless sensor networks since it determines its lifetime. Reducing the frequency of transmission is one way of reducing the cost, but it must not tamper with the reliability of the data received at the sink. In this paper, duty cycling and data-driven approaches have been used together to influence the prediction approach used in reducing data transmission. While duty cycling ensures nodes that are inactive for longer periods to save energy, the data-driven approach ensures features of the data that are used in predicting the data that the network needs during such inactive periods. Using the grey series model, a modified rolling GM(1,1) is proposed to improve the prediction accuracy of the model. Simulations suggest a 150% energy savings while not compromising on the reliability of the data received.

scholarly journals Data-Driven Approaches Can Overcome the Cost–Accuracy Trade-Off in Multireference Diagnostics

2020 ◽  
Vol 16 (7) ◽  
pp. 4373-4387 ◽  
Author(s):  
Chenru Duan ◽  
Fang Liu ◽  
Aditya Nandy ◽  
Heather J. Kulik
Keyword(s):  
Data Driven ◽  
Trade Off ◽  
The Cost

scholarly journals Beliefs We Can Believe in: Replacing Assumptions with Data in Real-Time Search

2020 ◽  
Vol 34 (06) ◽  
pp. 9827-9834
Author(s):  
Maximilian Fickert ◽  
Tianyi Gu ◽  
Leonhard Staut ◽  
Wheeler Ruml ◽  
Joerg Hoffmann ◽  
...  
Keyword(s):  
Real Time ◽  
Heuristic Search ◽  
Search Algorithm ◽  
Search Algorithms ◽  
Experimental Results ◽  
Data Driven ◽  
Heuristic Search Algorithms ◽  
Original Presentation ◽  
Performance Statistics ◽  
The Cost

Suboptimal heuristic search algorithms can benefit from reasoning about heuristic error, especially in a real-time setting where there is not enough time to search all the way to a goal. However, current reasoning methods implicitly or explicitly incorporate assumptions about the cost-to-go function. We consider a recent real-time search algorithm, called Nancy, that manipulates explicit beliefs about the cost-to-go. The original presentation of Nancy assumed that these beliefs are Gaussian, with parameters following a certain form. In this paper, we explore how to replace these assumptions with actual data. We develop a data-driven variant of Nancy, DDNancy, that bases its beliefs on heuristic performance statistics from the same domain. We extend Nancy and DDNancy with the notion of persistence and prove their completeness. Experimental results show that DDNancy can perform well in domains in which the original assumption-based Nancy performs poorly.

Progress in sensor technology - progress in process control? Part I: Sensor property investigation and classification

2003 ◽  
Vol 47 (2) ◽  
pp. 103-112 ◽  
Author(s):  
L. Rieger ◽  
J. Alex ◽  
S. Winkler ◽  
M. Boehler ◽  
M. Thomann ◽  
...  
Keyword(s):  
Control Strategies ◽  
Sensor Technology ◽  
Limiting Factor ◽  
Test Environment ◽  
Sensor Property ◽  
Sensor Models ◽  
The Cost ◽  
And Control ◽  
Measurement And Control

To ensure correctly operating control systems, the measurement and control equipment in WWTPs must be mutually consistent. The dynamic simulation of activated sludge systems could offer a suitable tool for designing and optimising control strategies. Ideal or simplified sensor models represent a limiting factor for comparability with field applications. More realistic sensor models are therefore required. Two groups of sensor models are proposed on the basis of field and laboratory tests: one for specific sensors and another for a classification of sensor types to be used with the COST simulation benchmark environment. This should lead to a more realistic test environment and allow control engineers to define the requirements of the measuring equipment as a function of the selected control strategy.

The Transfusion Services Committee—Responsibilities and Response to Adverse Transfusion Events

Hematology ◽  
2005 ◽  
Vol 2005 (1) ◽  
pp. 483-490 ◽  
Author(s):  
Ira A. Shulman ◽  
Sunita Saxena
Keyword(s):  
Performance Improvement ◽  
Review Process ◽  
High Volume ◽  
The United States ◽  
Adverse Outcomes ◽  
Data Driven ◽  
Medicaid Program ◽  
And Performance ◽  
To Receive

Abstract Healthcare institutions in the United States must review blood transfusion practices and adverse outcomes in order to receive payments from the Centers for Medicare/Medicaid program, but it is not required for a specific committee to be assigned to oversee the review process. Regardless of the group or individuals responsible, the review process must include a program of quality assessment and performance improvement that is ongoing, hospital-wide, and data-driven, reflects the complexity of the hospital’s organization and services, and involves all hospital departments and services (including those contracted). To be most effective, the performance improvement activity should be prioritized around high-risk, high-volume activities and/or in problem-prone areas. Even if a hospital elects not to receive payments from Medicare, it must still comply with applicable sections of the Code of Federal Regulations pertaining to transfusion services such as the follow up of adverse outcomes of transfusion.

Sign in / Sign up

Export Citation Format

Share Document