12 research outputs found
NIPS - Not Even Wrong? A Systematic Review of Empirically Complete Demonstrations of Algorithmic Effectiveness in the Machine Learning and Artificial Intelligence Literature
Objective: To determine the completeness of argumentative steps necessary to
conclude effectiveness of an algorithm in a sample of current ML/AI supervised
learning literature.
Data Sources: Papers published in the Neural Information Processing Systems
(NeurIPS, n\'ee NIPS) journal where the official record showed a 2017 year of
publication.
Eligibility Criteria: Studies reporting a (semi-)supervised model, or
pre-processing fused with (semi-)supervised models for tabular data.
Study Appraisal: Three reviewers applied the assessment criteria to determine
argumentative completeness. The criteria were split into three groups,
including: experiments (e.g real and/or synthetic data), baselines (e.g
uninformed and/or state-of-art) and quantitative comparison (e.g. performance
quantifiers with confidence intervals and formal comparison of the algorithm
against baselines).
Results: Of the 121 eligible manuscripts (from the sample of 679 abstracts),
99\% used real-world data and 29\% used synthetic data. 91\% of manuscripts did
not report an uninformed baseline and 55\% reported a state-of-art baseline.
32\% reported confidence intervals for performance but none provided references
or exposition for how these were calculated. 3\% reported formal comparisons.
Limitations: The use of one journal as the primary information source may not
be representative of all ML/AI literature. However, the NeurIPS conference is
recognised to be amongst the top tier concerning ML/AI studies, so it is
reasonable to consider its corpus to be representative of high-quality
research.
Conclusion: Using the 2017 sample of the NeurIPS supervised learning corpus
as an indicator for the quality and trustworthiness of current ML/AI research,
it appears that complete argumentative chains in demonstrations of algorithmic
effectiveness are rare
Three cachexia phenotypes and the impact of fat-only loss on survival in FOLFIRINOX therapy for pancreatic cancer
BACKGROUND:
By the traditional definition of unintended weight loss, cachexia develops in ~80% of patients with pancreatic ductal adenocarcinoma (PDAC). Here, we measure the longitudinal body composition changes in patients with advanced PDAC undergoing 5-fluorouracil, leucovorin, irinotecan, and oxaliplatin therapy.
METHODS:
We performed a retrospective review of 53 patients with advanced PDAC on 5-fluorouracil, leucovorin, irinotecan, and oxaliplatin as first line therapy at Indiana University Hospital from July 2010 to August 2015. Demographic, clinical, and survival data were collected. Body composition measurement by computed tomography (CT), trend, univariate, and multivariate analysis were performed.
RESULTS:
Among all patients, three cachexia phenotypes were identified. The majority of patients, 64%, had Muscle and Fat Wasting (MFW), while 17% had Fat-Only Wasting (FW) and 19% had No Wasting (NW). NW had significantly improved overall median survival (OMS) of 22.6 months vs. 13.0 months for FW and 12.2 months for MFW (P = 0.02). FW (HR = 5.2; 95% confidence interval = 1.5-17.3) and MFW (HR = 1.8; 95% confidence interval = 1.1-2.9) were associated with an increased risk of mortality compared with NW. OMS and risk of mortality did not differ between FW and MFW. Progression of disease, sarcopenic obesity at diagnosis, and primary tail tumours were also associated with decreased OMS. On multivariate analysis, cachexia phenotype and chemotherapy response were independently associated with survival. Notably, CT-based body composition analysis detected tissue loss of >5% in 81% of patients, while the traditional definition of >5% body weight loss identified 56.6%.
CONCLUSIONS:
Distinct cachexia phenotypes were observed in this homogeneous population of patients with equivalent stage, diagnosis, and first-line treatment. This suggests cellular, molecular, or genetic heterogeneity of host or tumour. Survival among patients with FW was as poor as for MFW, indicating adipose tissue plays a crucial role in cachexia and PDAC mortality. Adipose tissue should be studied for its mechanistic contributions to cachexia
Distinct cachexia phenotypes and the importance of adipose tissue loss on survival of patients with advanced pancreatic cancer on FOLFIRINOX chemotherapy
IndianapolisBy the traditional definition of unintended weight loss, cachexia develops in ~80% of patients with pancreatic ductal adenocarcinoma (PDAC). Here we measure the longitudinal body composition changes in patients with advanced PDAC undergoing FOLFIRINOX therapy. We performed a retrospective review of 53 patients with advanced PDAC on FOLFIRINOX as first line therapy at Indiana University Hospital from July 2010 to August 2015. Demographic, clinical, and survival data were collected. Body composition measurement, trend, univariate and multivariate analysis were performed. Three cachexia phenotypes were identified. The majority of patients, 64%, had Muscle-and-Fat Wasting (MFW), while 17% had Fat-Only Wasting (FW) and 19% had No Wasting (NW). NW had significantly improved overall median survival (OMS) of 22.6 months vs. 13.0 months for FW and 12.2 months for MFW (p=0.02). FW (HR=5.2; 95%CI=1.5-17.3) and MFW (HR=1.8; 95%CI=1.1-2.9) were associated with an increased risk of mortality compared to NW. OMS and risk of mortality did not differ between FW and MFW. Progression of disease, sarcopenic obesity at diagnosis, and primary tail tumors were also associated with decreased OMS. On multivariate analysis cachexia phenotype and chemotherapy response were independently associated with survival. Three phenotypes of cachexia were observed. Moreover, three phenotypes suggests molecular or genetic heterogeneity of host or tumor. Identifying these differences will be vital to defining optimal treatment for cachexia. Survival among FW was as poor as MFW suggesting adipose tissue plays a crucial role in cachexia. Blunting or possibly preventing cachexia may confer a significant survival advantage in patients with advanced PDAC
Competing Bandits: The Perils of Exploration Under Competition
Most online platforms strive to learn from interactions with users, and many
engage in exploration: making potentially suboptimal choices for the sake of
acquiring new information. We study the interplay between exploration and
competition: how such platforms balance the exploration for learning and the
competition for users. Here users play three distinct roles: they are customers
that generate revenue, they are sources of data for learning, and they are
self-interested agents which choose among the competing platforms.
We consider a stylized duopoly model in which two firms face the same
multi-armed bandit problem. Users arrive one by one and choose between the two
firms, so that each firm makes progress on its bandit problem only if it is
chosen. Through a mix of theoretical results and numerical simulations, we
study whether and to what extent competition incentivizes the adoption of
better bandit algorithms, and whether it leads to welfare increases for users.
We find that stark competition induces firms to commit to a "greedy" bandit
algorithm that leads to low welfare. However, weakening competition by
providing firms with some "free" users incentivizes better exploration
strategies and increases welfare. We investigate two channels for weakening the
competition: relaxing the rationality of users and giving one firm a
first-mover advantage. Our findings are closely related to the "competition vs.
innovation" relationship, and elucidate the first-mover advantage in the
digital economy.Comment: merged and extended version of arXiv:1702.08533 and arXiv:1902.0559