scholarly journals Long-Term Impacts of Fair Machine Learning

2019 ◽  
Vol 28 (3) ◽  
pp. 7-11
Author(s):  
Xueru Zhang ◽  
Mohammad Mahdi Khalili ◽  
Mingyan Liu
Keyword(s):  
Machine Learning ◽  
Real World ◽  
Human Beings ◽  
Learning Models ◽  
Real World Data ◽  
World Data ◽  
Fairness Concerns ◽  
Fairness Constraints ◽  
Machine Learning Models

Machine learning models developed from real-world data can inherit potential, preexisting bias in the dataset. When these models are used to inform decisions involving human beings, fairness concerns inevitably arise. Imposing certain fairness constraints in the training of models can be effective only if appropriate criteria are applied. However, a fairness criterion can be defined/assessed only when the interaction between the decisions and the underlying population is well understood. We introduce two feedback models describing how people react when receiving machine-aided decisions and illustrate that some commonly used fairness criteria can end with undesirable consequences while reinforcing discrimination.

scholarly journals Regression under Human Assistance

2020 ◽  
Vol 34 (03) ◽  
pp. 2611-2620
Author(s):  
Abir De ◽  
Paramita Koley ◽  
Niloy Ganguly ◽  
Manuel Gomez-Rodriguez
Keyword(s):  
Machine Learning ◽  
Greedy Algorithm ◽  
Real World ◽  
Medical Diagnosis ◽  
Submodular Functions ◽  
Learning Models ◽  
Real World Data ◽  
Alternative Representation ◽  
The Greedy Algorithm ◽  
Machine Learning Models

Decisions are increasingly taken by both humans and machine learning models. However, machine learning models are currently trained for full automation—they are not aware that some of the decisions may still be taken by humans. In this paper, we take a first step towards the development of machine learning models that are optimized to operate under different automation levels. More specifically, we first introduce the problem of ridge regression under human assistance and show that it is NP-hard. Then, we derive an alternative representation of the corresponding objective function as a difference of nondecreasing submodular functions. Building on this representation, we further show that the objective is nondecreasing and satisfies α-submodularity, a recently introduced notion of approximate submodularity. These properties allow a simple and efficient greedy algorithm to enjoy approximation guarantees at solving the problem. Experiments on synthetic and real-world data from two important applications—medical diagnosis and content moderation—demonstrate that the greedy algorithm beats several competitive baselines.

878-P: Real-World Data from U.S. Patients Using a Long-Term Implantable Continuous Glucose Monitoring (CGM) System: Age Effect on Glycemic Control

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 878-P
Author(s):  
KATHERINE TWEDEN ◽  
SAMANWOY GHOSH-DASTIDAR ◽  
ANDREW D. DEHENNIS ◽  
FRANCINE KAUFMAN
Keyword(s):  
Glycemic Control ◽  
Continuous Glucose Monitoring ◽  
Real World ◽  
Glucose Monitoring ◽  
Age Effect ◽  
Real World Data ◽  
World Data

Long-term outcome of minimally invasive mitral valve annuloplasty in disproportionate mitral regurgitation

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
M Albano ◽  
S Nagumo ◽  
M Vanderheyden ◽  
J Bartunek ◽  
C Collet ◽  
...  
Keyword(s):  
Mitral Valve ◽  
Mitral Regurgitation ◽  
Minimally Invasive ◽  
Real World ◽  
Independent Predictor ◽  
Real World Data ◽  
Long Term Outcome ◽  
World Data ◽  
Mitral Valve Annuloplasty

Abstract Background Hypothetical concept of disproportionate secondary mitral regurgitation (SMR) has been recently introduced to facilitate patient's selection for mitral valve intervention. However, real world data validating this concept are unavailable. Purpose To investigate long-term effects of minimally invasive mitral valve annuloplasty (MVA) in patients with disproportionate (dSMR) versus proportionate SMR. Methods The study population consisted of 44 consecutive patients (age 67±9,5 years; 64% males) on guidelines-directed therapy with advanced heart failure (HF), reduced LV ejection fraction (EF) (32±9,7%) and SMR undergoing isolated mini-invasive MVA. Patients with organic mitral regurgitation or concomitant myocardial revascularization were excluded. To assess SMR disproportionality, the PISA-derived effective regurgitant orifice area (EROA) and regurgitant volume (RV) were compared to the estimated EROA and RV by using Gorlin formula and pooled real world data. Results According to EROA, a total of 20 (46%) and 24 (54%) patients, respectively, had dSMR and proportionate SMR (pSMR). According to RV, a total of 17 (39%) had dSMR and 27 (61%) had pSMR. Patients with dSMR showed significantly lower prevalence of male gender and higher prevalence of diabetes mellitus than patients with pSMR (p<0,001). Moreover, we observed smaller LV end-diastolic volume, larger EROA and RV (both p<0,01) and higher LV EF (p=0,02) in the dSMR versus the pSMR group. Other baseline characteristics were similar. During median follow up of 4.39 y (IQR 2,2–9,96y), a total of 25 (56%) patients died from any cause while 21 (47%) individuals were readmitted for worsening HF. Patients with dSMR versus pSMR according to both EROA and RV showed significantly lower rate of HF readmissions (both p<0.05) (Figure 1, 2). In Cox regression analysis combining clinical and imaging parameters, dSMR was the only independent predictor of HF readmissions (HR 0.20, 95% CI 0.07–0.60, p=0.004). In contrast, mortality was similar between dSMR and pSMR (NS) with age as the only independent predictor (HR 1,10; 95% CI 1,03–1,18, p=0,003). Conclusions Minimally invasive MVA is associated with significant reduction of HF readmissions in patients with dSMR versus pSMR while the mortality is similar. This suggests the importance of other parameters, i.e. age and degree of LV remodeling, to guide clinical management in SMR. Funding Acknowledgement Type of funding source: None

Fr518 LONG-TERM SAFETY OF TOFACITINIB: REAL WORLD DATA FROM A TERTIARY CENTER

2021 ◽  
Vol 160 (6) ◽  
pp. S-342-S-343
Author(s):  
Nathaniel A. Cohen ◽  
Joshua M. Steinberg ◽  
Alexa Silfen ◽  
Cindy Traboulsi ◽  
Jorie Singer ◽  
...  
Keyword(s):  
Real World ◽  
Real World Data ◽  
World Data ◽  
Tertiary Center

A Survey on Data-driven Network Intrusion Detection

2022 ◽  
Vol 54 (9) ◽  
pp. 1-36
Author(s):  
Dylan Chou ◽  
Meng Jiang
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Real World ◽  
Data Driven ◽  
Network Intrusion Detection ◽  
Large Network ◽  
Learning Models ◽  
Simulated Environments ◽  
Network Intrusion ◽  
Machine Learning Models

Data-driven network intrusion detection (NID) has a tendency towards minority attack classes compared to normal traffic. Many datasets are collected in simulated environments rather than real-world networks. These challenges undermine the performance of intrusion detection machine learning models by fitting machine learning models to unrepresentative “sandbox” datasets. This survey presents a taxonomy with eight main challenges and explores common datasets from 1999 to 2020. Trends are analyzed on the challenges in the past decade and future directions are proposed on expanding NID into cloud-based environments, devising scalable models for large network data, and creating labeled datasets collected in real-world networks.

scholarly journals "Smart Tanks": An Intelligent High-Throughput Intervention Testing Platform in Daphnia

2021 ◽  
Author(s):  
Yongmin Cho ◽  
Rachael A Jonas-Closs ◽  
Lev Y Yampolsky ◽  
Marc W Kirschner ◽  
Leonid Peshkin
Keyword(s):  
Machine Learning ◽  
Chronological Age ◽  
Complex Behavior ◽  
Learning Models ◽  
Short Term ◽  
Health Span ◽  
Response Profile ◽  
Automated Phenotyping ◽  
Machine Learning Models

We present a novel platform for testing the effect of interventions on life- and health-span of a short-lived semi transparent freshwater organism, sensitive to drugs with complex behavior and physiology - the planktonic crustacean Daphnia magna. Within this platform, dozens of complex behavioural features of both routine motion and response to stimuli are continuously accurately quantified for large homogeneous cohorts via an automated phenotyping pipeline. We build predictive machine learning models calibrated using chronological age and extrapolate onto phenotypic age. We further apply the model to estimate the phenotypic age under pharmacological perturbation. Our platform provides a scalable framework for drug screening and characterization in both life-long and instant assays as illustrated using long term dose response profile of metformin and short term assay of such well-studied substances as caffeine and alcohol.

scholarly journals Data-Driven Approach for Predicting and Explaining the Risk of Long-Term Unemployment

2020 ◽  
Vol 214 ◽  
pp. 01023
Author(s):  
Linan (Frank) Zhao
Keyword(s):  
Machine Learning ◽  
Age Groups ◽  
Learning Models ◽  
Public Authorities ◽  
Ensemble Machine Learning ◽  
European Public ◽  
Data Driven Approach ◽  
Using Data ◽  
Machine Learning Models

Long-term unemployment has significant societal impact and is of particular concerns for policymakers with regard to economic growth and public finances. This paper constructs advanced ensemble machine learning models to predict citizens’ risks of becoming long-term unemployed using data collected from European public authorities for employment service. The proposed model achieves 81.2% accuracy on identifying citizens with high risks of long-term unemployment. This paper also examines how to dissect black-box machine learning models by offering explanations at both a local and global level using SHAP, a state-of-the-art model-agnostic approach to explain factors that contribute to long-term unemployment. Lastly, this paper addresses an under-explored question when applying machine learning in the public domain, that is, the inherent bias in model predictions. The results show that popular models such as gradient boosted trees may produce unfair predictions against senior age groups and immigrants. Overall, this paper sheds light on the recent increasing shift for governments to adopt machine learning models to profile and prioritize employment resources to reduce the detrimental effects of long-term unemployment and improve public welfare.

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