scholarly journals On Assessing Trustworthy AI in Healthcare. Machine Learning as a Supportive Tool to Recognize Cardiac Arrest in Emergency Calls

2021 ◽  
Vol 3 ◽  
Author(s):  
Roberto V. Zicari ◽  
James Brusseau ◽  
Stig Nikolaj Blomberg ◽  
Helle Collatz Christensen ◽  
Megan Coffee ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cardiac Arrest ◽  
Best Practice ◽  
Ethical Trade ◽  
Emergency Calls ◽  
Trade Offs ◽  
Ai Ethics ◽  
Set Up ◽  
High Level ◽  
Applications Of Ai

Artificial Intelligence (AI) has the potential to greatly improve the delivery of healthcare and other services that advance population health and wellbeing. However, the use of AI in healthcare also brings potential risks that may cause unintended harm. To guide future developments in AI, the High-Level Expert Group on AI set up by the European Commission (EC), recently published ethics guidelines for what it terms “trustworthy” AI. These guidelines are aimed at a variety of stakeholders, especially guiding practitioners toward more ethical and more robust applications of AI. In line with efforts of the EC, AI ethics scholarship focuses increasingly on converting abstract principles into actionable recommendations. However, the interpretation, relevance, and implementation of trustworthy AI depend on the domain and the context in which the AI system is used. The main contribution of this paper is to demonstrate how to use the general AI HLEG trustworthy AI guidelines in practice in the healthcare domain. To this end, we present a best practice of assessing the use of machine learning as a supportive tool to recognize cardiac arrest in emergency calls. The AI system under assessment is currently in use in the city of Copenhagen in Denmark. The assessment is accomplished by an independent team composed of philosophers, policy makers, social scientists, technical, legal, and medical experts. By leveraging an interdisciplinary team, we aim to expose the complex trade-offs and the necessity for such thorough human review when tackling socio-technical applications of AI in healthcare. For the assessment, we use a process to assess trustworthy AI, called 1Z-Inspection® to identify specific challenges and potential ethical trade-offs when we consider AI in practice.

Applying The Best Practice For Internal Quality Control (IQC) Management, Consolidation And Review Process In Sidra Medicine Clinical Chemistry Division

2020 ◽  
Vol 154 (Supplement_1) ◽  
pp. S91-S91
Author(s):  
J M Asinas
Keyword(s):  
Quality Control ◽  
Real Time ◽  
Best Practice ◽  
Clinical Chemistry ◽  
Internal Quality Control ◽  
Internal Quality ◽  
Allowable Error ◽  
Chemistry Division ◽  
Set Up ◽  
High Level

Abstract Introduction/Objective The management of internal quality control (IQC) in Sidra Medicine Clinical Chemistry Division has been evaluated in order to promote a more consolidated and efficient process of IQC management. The statistical data produced from Cerner QC Module are transferred to IQC review templates consisting of formulas to auto- calculate parameters such as multiple of expected QC failure frequency and desirable comparison limit between analyzers. The IQC review and documentation process using the in-house excel template requires several hours to complete, hence a faster and more efficient IQC management module is required. The main objective of this study is to improve the initial IQC management set up, work flow and review procedures and to implement Biorad Unity Real Time (URT) program to develop a more efficient IQC management system. Methods The URT software has been recently configured and implemented to consolidate and streamline IQC management. URT is built through Sidra Medicine IT Enterprise level which allows multiple users to login. IQC data are downloaded using scripts from Cerner which are filtered through Biorad Unity Connect (UC) software. Additional quality tools are also explored such as various user defined statistical reports, IQC analysis using peer reviewed total allowable error (TeA) and assignment of the most appropriate Westgard rules. Determination of sigma metrics and uncertainty of measurement is also performed using the URT application. Results The generation of any IQC report is less cumbersome and time consuming as compared with the previous process. However, some user defined formulas in the IQC templates are not found on the URT reports. The URT Levey Jennings chart are also more user friendly and directly compares the daily IQC data with Unity inter-laboratory peers enabling the production of instant and monthly reports through QCNet site when assay investigation is required and for IQC report documentation. Conclusion The combination of Cerner IQC, Unity Real-time, QCNet Inter-laboratory reports and in house IQC templates produce a high level and very detailed IQC review which effectively evaluate assay performance to assist on IQC troubleshooting and root cause analysis to be able to apply the most appropriate corrective actions.

scholarly journals Reply letter to "Machine learning as a supportive tool to recognize cardiac arrest in emergency calls"

Resuscitation ◽  
2019 ◽  
Vol 144 ◽  
pp. 205-206
Author(s):  
Stig Nikolaj Blomberg ◽  
Fredrik Folke ◽  
Annette Kjær Ersbøll ◽  
Helle Collatz Christensen ◽  
Christian Torp-Pedersen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cardiac Arrest ◽  
Emergency Calls

scholarly journals Machine learning as a supportive tool to recognize cardiac arrest in emergency calls

Resuscitation ◽  
2019 ◽  
Vol 138 ◽  
pp. 322-329 ◽  
Author(s):  
Stig Nikolaj Blomberg ◽  
Fredrik Folke ◽  
Annette Kjær Ersbøll ◽  
Helle Collatz Christensen ◽  
Christian Torp-Pedersen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cardiac Arrest ◽  
Emergency Calls

scholarly journals An Empirical Model For Validity And Verification Of Ai Behavior: Overcoming Ai Hazards In Neural Networks

2021 ◽  
Vol 21 ◽  
pp. 44-52
Author(s):  
Ayse K Arslan
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Neural Networks ◽  
Empirical Model ◽  
Learning Systems ◽  
Rapid Progress ◽  
Research Directions ◽  
Harmful Behavior ◽  
High Level ◽  
Applications Of Ai

Rapid progress in machine learning and artificial intelligence (AI) has brought increasing attention to the potential impacts of AI technologies on society. This paper discusses hazards in machine learning systems, defined as unintended and harmful behavior that may emerge from poor design of real-world AI systems with a particular focus on ANN. The paper provides a review of previous work in these areas as well as suggesting research directions with a focus on relevance to cutting-edge AI systems with a focus on neural networks. Finally, the paper considers the high-level question of how to think most productively about the safety of forward-looking applications of AI.

BAND NN: A Deep Learning Framework For Energy Prediction and Geometry Optimization of Organic Small Molecules

2019 ◽  
Author(s):  
Siddhartha Laghuvarapu ◽  
Yashaswi Pathak ◽  
U. Deva Priyakumar
Keyword(s):  
Machine Learning ◽  
Density Functional ◽  
Computational Cost ◽  
Geometry Optimization ◽  
Dft Methods ◽  
Energy Prediction ◽  
Machine Learning Model ◽  
Equilibrium Structures ◽  
High Level ◽  
Non Equilibrium

Recent advances in artificial intelligence along with development of large datasets of energies calculated using quantum mechanical (QM)/density functional theory (DFT) methods have enabled prediction of accurate molecular energies at reasonably low computational cost. However, machine learning models that have been reported so far requires the atomic positions obtained from geometry optimizations using high level QM/DFT methods as input in order to predict the energies, and do not allow for geometry optimization. In this paper, a transferable and molecule-size independent machine learning model (BAND NN) based on a chemically intuitive representation inspired by molecular mechanics force fields is presented. The model predicts the atomization energies of equilibrium and non-equilibrium structures as sum of energy contributions from bonds (B), angles (A), nonbonds (N) and dihedrals (D) at remarkable accuracy. The robustness of the proposed model is further validated by calculations that span over the conformational, configurational and reaction space. The transferability of this model on systems larger than the ones in the dataset is demonstrated by performing calculations on select large molecules. Importantly, employing the BAND NN model, it is possible to perform geometry optimizations starting from non-equilibrium structures along with predicting their energies.

Pakistan's Export Possibilities

1973 ◽  
Vol 12 (1) ◽  
pp. 1-30
Author(s):  
Syed Nawab Haider Naqvi
Keyword(s):  
Economic Growth ◽  
Foreign Aid ◽  
Policy Maker ◽  
Time Horizon ◽  
Price Level ◽  
Balance Of Payments ◽  
Domestic Saving ◽  
Trade Offs ◽  
High Level

The recent uncertainties about aid flows have underscored the need for achieving an early independence from foreign aid. The Perspective Plan (1,965-85) had envisaged the termination of Pakistan's dependence on foreign aid by 1985. However, in the context of West Pakistan alone the time horizon can now be advanced by several years with considerable confidence in its economy to pull the trick. The difficulties of achieving independence from foreign aid can be seen by reference to the fact that aid flows make it possible for the policy-maker to pursue such ostensibly incompatible objectives as a balance in international payments (i.e., foreign aid finances the balance of payments), higher rates of economic growth (Lei, it pulls up domestic saving and investment levels), a high level of employment (i.e., it keeps the industries working at a fuller capacity than would otherwise be the case), and a reasonably stable price level (i.e., it lets a higher level of imports than would otherwise be possible). Without aid, then a simultaneous attainment of all these objectives at the former higher levels together with the balance in foreign payments may become well-nigh impos¬sible. Choices are, therefore, inevitable not for definite places in the hierarchy of values, but rather for occasional "trade-offs". That is to say, we will have to" choose how much to sacrifice for the attainment of one goal for the sake of somewhat better realization of another.

COVID-19 Outbreak Prediction with Machine Learning

2020 ◽  
Author(s):  
Sina Faizollahzadeh Ardabili ◽  
Amir Mosavi ◽  
Pedram Ghamisi ◽  
Filip Ferdinand ◽  
Annamaria R. Varkonyi-Koczy ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Fuzzy Inference ◽  
Control Measures ◽  
Future Research ◽  
Complex Nature ◽  
Inference System ◽  
Wide Range ◽  
Standard Models ◽  
High Level

Several outbreak prediction models for COVID-19 are being used by officials around the world to make informed-decisions and enforce relevant control measures. Among the standard models for COVID-19 global pandemic prediction, simple epidemiological and statistical models have received more attention by authorities, and they are popular in the media. Due to a high level of uncertainty and lack of essential data, standard models have shown low accuracy for long-term prediction. Although the literature includes several attempts to address this issue, the essential generalization and robustness abilities of existing models needs to be improved. This paper presents a comparative analysis of machine learning and soft computing models to predict the COVID-19 outbreak as an alternative to SIR and SEIR models. Among a wide range of machine learning models investigated, two models showed promising results (i.e., multi-layered perceptron, MLP, and adaptive network-based fuzzy inference system, ANFIS). Based on the results reported here, and due to the highly complex nature of the COVID-19 outbreak and variation in its behavior from nation-to-nation, this study suggests machine learning as an effective tool to model the outbreak. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research. Paper further suggests that real novelty in outbreak prediction can be realized through integrating machine learning and SEIR models.

Sign in / Sign up

Export Citation Format

Share Document