This document discusses the use of Multiple Classification Lot Quality Assurance Sampling (MC-LQAS) to assess malaria prevalence and target interventions in Uganda. MC-LQAS was used to classify parishes into categories of low, middle, or high malaria prevalence based on rapid diagnostic test results. The study found high malaria prevalence and anemia rates with variations between parishes. MC-LQAS provided reliable classification that can help target cost-effective malaria control strategies. The results provide a baseline and evidence to inform intervention priorities.
Highly Reliable Hepatitis Diagnosis with multi classifiersahmedbohy
The diagnosis of some diseases like hepatitis is very difficult task for a doctor, where doctors usually determine decision by comparing the current test results of patients with another one who has the same condition. Hepatitis is one of the most common diseases among Egyptians; as it represents 22% of hepatitis cases around the world. This motivates us for suggesting new methods to improve the outcomes of existing approaches, as well as to help doctors and specialists to diagnose hepatitis disease survival
Classification techniques; Fusion; WEKA.
Hepatitis diagnosis based on Artificial Intelligence Using Single And multi c...ahmedbohy
The goal of our paper is to obtain superior accuracy of different classifiers or multi-classifiers fusion in diagnosing Hepatitis using world wide data set from Ljubljana University. We present an implementation among some of the classification methods, which are defined as the best algorithms in medical field. Then we apply a fusion between classifiers to get the best multi-classifier fusion approach. By using confusion matrix to get classification accuracy which built in 10-fold cross validation technique. The experimental results show that using multi-classifiers fusion achieved better accuracy than the single one
A monitoring and evaluation system is needed to assess both structural and health sector components of the response to HIV in key populations. It is critical that these systems are practical, not overly complicated, and that they collect information that is current, useful and readily used
Risk Based Monitoring in Clinical trials_Aishwarya Janjale.pptxClinosolIndia
Risk-Based Monitoring (RBM) in clinical trials represents a departure from traditional, one-size-fits-all monitoring approaches. This innovative strategy tailors monitoring activities to the specific risks associated with a trial, optimizing resource utilization and enhancing data quality. This article explores the key principles, benefits, and challenges of RBM, illustrating its transformative impact on the landscape of clinical trial oversight.
Key Principles:
Risk Identification and Assessment:
RBM begins with a comprehensive assessment of potential risks to data integrity, patient safety, and study endpoints. These risks are identified based on factors such as study complexity, patient population, and investigational product characteristics.
Every hospital and health care system is significantly impacted by readmission policies mandated by new regulations.
And every facility must implement strategies to reduce the number of costly and unnecessary readmissions.
During this presentation you will discover how to decrease your readmission rates and take advantage of incentives, rather than suffer penalties that can significantly impact your bottom line.
Highly Reliable Hepatitis Diagnosis with multi classifiersahmedbohy
The diagnosis of some diseases like hepatitis is very difficult task for a doctor, where doctors usually determine decision by comparing the current test results of patients with another one who has the same condition. Hepatitis is one of the most common diseases among Egyptians; as it represents 22% of hepatitis cases around the world. This motivates us for suggesting new methods to improve the outcomes of existing approaches, as well as to help doctors and specialists to diagnose hepatitis disease survival
Classification techniques; Fusion; WEKA.
Hepatitis diagnosis based on Artificial Intelligence Using Single And multi c...ahmedbohy
The goal of our paper is to obtain superior accuracy of different classifiers or multi-classifiers fusion in diagnosing Hepatitis using world wide data set from Ljubljana University. We present an implementation among some of the classification methods, which are defined as the best algorithms in medical field. Then we apply a fusion between classifiers to get the best multi-classifier fusion approach. By using confusion matrix to get classification accuracy which built in 10-fold cross validation technique. The experimental results show that using multi-classifiers fusion achieved better accuracy than the single one
A monitoring and evaluation system is needed to assess both structural and health sector components of the response to HIV in key populations. It is critical that these systems are practical, not overly complicated, and that they collect information that is current, useful and readily used
Risk Based Monitoring in Clinical trials_Aishwarya Janjale.pptxClinosolIndia
Risk-Based Monitoring (RBM) in clinical trials represents a departure from traditional, one-size-fits-all monitoring approaches. This innovative strategy tailors monitoring activities to the specific risks associated with a trial, optimizing resource utilization and enhancing data quality. This article explores the key principles, benefits, and challenges of RBM, illustrating its transformative impact on the landscape of clinical trial oversight.
Key Principles:
Risk Identification and Assessment:
RBM begins with a comprehensive assessment of potential risks to data integrity, patient safety, and study endpoints. These risks are identified based on factors such as study complexity, patient population, and investigational product characteristics.
Every hospital and health care system is significantly impacted by readmission policies mandated by new regulations.
And every facility must implement strategies to reduce the number of costly and unnecessary readmissions.
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Top 5 tips for managing risks in your clinical studies - PepgraPEPGRA Healthcare
Fronting ever-increasing costs of running a clinical trial, sponsors must guarantee they are correctly directing their financial plan and resolving the highest risk areas while preserving patient safety and data reliability in Patient recruitment for clinical trials. In this blog, Pepgra provides five tips for significant risk levels in clinical studies like:
1. Outlining your levels of risks
2. Evaluating and categorizing risk
3. Concentrating on essential areas of risk
4. Observing and controlling risks
5. Estimating the efficiency of risk management
Read More: http://bit.ly/3bb4j6h
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Journal for Healthcare QualityQuartile Dashboards Transla.docxcroysierkathey
Journal for Healthcare Quality
Quartile Dashboards: Translating Large
Data Sets into Performance Improvement
Priorities
Diane Storer Brown, Carolyn E. Aydin, Nancy Donaldson
Abstract: Quality professionals are the first to understand chal-
lenges of transforming data into meaningful information for
frontline staff, operational managers, and governing bodies.To
understand an individual facility, service, or patient care unit's
comparative performance from within large data sets, priori-
tization and focused data presentation are needed.This article
presents a methodology for translating data from large data
sets into dashboards for setting performance improvement
priorities, in a simple way that takes advantage of tools readily
available and easily used by support staff.This methodology is
illustrated with examples from a large nursing quality data set,
the California Nursing Outcomes Coalition.
Key Words
benchmarking
dashboard
prioritization
radar diagrams
Dashboards have transformed the way that
healthcare professionals and senior leaders
iiionitor organizational perfonnance and pri-
oritize the design of improvement interven-
lions (Donaldson, Brown, Aydin, Bolton, 8c
RLiilcdgc, 2005; Rosow, Adam, Coulombe, Race,
8c Anderson, 2003). Dashboards provide data
on structure, process, and outcome variables;
report cards provide final leporLs on (jutcomes
and are often intended for external audiences
(Gregg, 2002). Recent public reporting initia-
tives and tJie pay-for-perforinance demonstra-
tion project funded by tbe Centers for Medicare
and Medicaid Semces represent tbe report card
strategy in whicb liospital performance isjudged
by external constituents incoiporating incentives
for performance improvement {Lindenauer,
Remus, 8c Roman, 2007). In order to improve
performance on public report cards, hospitals
construct internal dashboards to review perfor-
mance and identify areas in need oí change.
Benchmarking with similar hospitals in a confi-
dential context is an important clement in this
proce.ss (Brown, Donaldson, Aydin, Sc C^aiison,
2001; Gregg, 2002).
Understanding Performance Data
Traditionally, large quality data sets have been
for Healthcare Quality summarized using descriptive statistics such as
rcQuality
frequencies, averages, and standard deviations
placed in tables, bar graphs, or line graphs to
track key metrics over time. Those operationally
accountable to improve patient care quality and
saiety depend on quality professionals to trans-
late data into usable information, which is then
used to determine performance thresholds foi'
(Irilkiown analyses oi" benchmarks and perfor-
mance goals to understand relative comparative
performance. This article uses common defini-
tions for perlbrmance metrics as follows from
Merriam Webster Online Dictionary (2007): Goal is
the end toward which effort is directed (where
you want your perfonnance to be) and is synony-
mous with target, a goal to be acbieved; threshoùl
is a leve ...
Quale cooperazione nel futuro? Ong 2.0 e Pillole di open developmentOng 2.0
Presentazione realizzata in occasione del workshop sulle ICT4D organizzato dalla Fondazione Think nel quadro dell'Osservatorio sulle ICT per il Non Profit
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The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024Neo4j
Neha Bajwa, Vice President of Product Marketing, Neo4j
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Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
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Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Dr. Sean Tan, Head of Data Science, Changi Airport Group
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zkStudyClub - Reef: Fast Succinct Non-Interactive Zero-Knowledge Regex ProofsAlex Pruden
This paper presents Reef, a system for generating publicly verifiable succinct non-interactive zero-knowledge proofs that a committed document matches or does not match a regular expression. We describe applications such as proving the strength of passwords, the provenance of email despite redactions, the validity of oblivious DNS queries, and the existence of mutations in DNA. Reef supports the Perl Compatible Regular Expression syntax, including wildcards, alternation, ranges, capture groups, Kleene star, negations, and lookarounds. Reef introduces a new type of automata, Skipping Alternating Finite Automata (SAFA), that skips irrelevant parts of a document when producing proofs without undermining soundness, and instantiates SAFA with a lookup argument. Our experimental evaluation confirms that Reef can generate proofs for documents with 32M characters; the proofs are small and cheap to verify (under a second).
Paper: https://eprint.iacr.org/2023/1886
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Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
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Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
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LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
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GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
3. Background and classic LQAS
MC-LQAS
Application to malaria control within the research
activity con
4.
5. LQAS today is a statistical quality control method
Developed in the 1920’s attributable to Dodge and
Romig’s work. Mainly to control quality of
industrially produced goods on the principle that:
◦ Supervisor inspects a lot of goods from a production unit
or assembly line
◦ If number of defective goods exceeds a pre-determined
allowable number, then the lot is rejected; otherwise
classified as acceptable quality
◦ Number of allowable defective goods is based on a
production standard and statistically determined sample
size
6. Transitioned into health systems to assess health
care services, health behaviors and disease
burden.
Production standard is a predetermined population
coverage target set by managers
Lot consists of a supervision area e.g. a
community or health facility catchment
LQAS data collected at multiple time points can be
used to measure the spatial variation or behavior
change
8. Implemented as part of stratified random sampling
design
Uses small samples often 19 per strata or lot
Sample determines whether coverage by a health
intervention reaches a specific target by using a
statistically determined decision rule(DR)
DR is the minimum number of individuals in the
sample that should have received an intervention
9. Classic LQAS uses one decision rule, sample size 19
and 2 threshold values, that define lower and upper
regions.
Each lot is then classified as ‘Acceptable’ or
‘Unacceptable’ against the target.
Since LQAS is based on rigorous random sampling,
results from the catchment area can be aggregated for
provincial or national level coverage.
Statistical underpinning is the operating characteristic
(OC)curve
10. Operating Characteristic Curve for
Probability to Accept
Sample of 19 and Decion Rule of 13
1
0.8
0.6
0.4
0.2
0
1 0.9 0.8 0.7 0.6 0.5 0.4 0.3
Supervision Area Coverage
11.
12. Popular tool
◦ Ease of use
◦ Straight forward implementation
◦ Rapidity of results
◦ Sound statistical underpinning
13. A
Reached
B
Target
C
D
E
Below the Target
F G Or Below Average
Valadez 2011
14. Maintain the program at the
current level
Identify Supervisors and Health Reached
Workers that can help other Health Target
Workers improve their performance
Identify the reasons for
program problems
Below the Target
Develop targeted Or Below Average
solutions
Valadez 2011
15. Control of misclassification (α-alpha error & β-beta
error)
Requirement for finer classification in disease
control and treatment recommendations e.g. WHO
treatment guidelines for schistosomiasis are linked
to three way classification of prevalence of
infection
Inevitable extension to LQAS
16. Focuses into three classification of ‘low’, ‘middle’,
‘high’
Defines two decision rules e.g. ( d and d ) to yield
1 2
least misclassification error for a given sample size
(n)
Probability of correct classification remains high at
upper and lower thresholds
On analysis, classify ‘low’ if the successes x from
total n observations is less than or equal to d1
;classify ‘high’ if x is greater than d2; otherwise,
classify ‘middle’
17.
18. Uses sample size of n=28, decision rules d1=2 and
d2=10
With d2=10, elicits grey region around the upper
threshold of 40% favouring classification of
category 3( high) over category 2 (Middle).
Thus, grey regions ranging from 0.06 to 0.15 and
0.30 to 0.45 respectively. That is a better trade-
off , on divides of producer and consumer risks
19. Sample of 28, if 2 or fewer of these observation are
malaria RDT+, then the area is classified as
category 1, termed ‘low’.
For 10 or more counts malaria RDT+, area is
classified as ‘category 3 termed ’high’
Counts between 3 and 9 classify area as category 2
termed ‘Middle’.
Design gives 80% chance of correctly classifying a
given locale at each of the listed thresholds. A
double sample size of 56 increases the power but
often obtain similar results
20. Malaria prevalence threshold values are set at
PfPR of 10% and 40%.
Locale with below 10% is of low prevalence, 10%-
40% is moderate prevalence, above 40% is high
prevalence
MC-LQAS methodology classifies areas into these
three categories using RDTs for PfPR.
MC-LQAS measures malaria intervention indicators
and classify locale.
MC-LQAS data maps locale malaria prevalence
21. Classifications of ‘low’, ‘middle’ and ‘high’ for link
interventions to the prevalence detected
Category 3(high) is targeted for complete set of
malaria interventions(IPT, ITNs, case management
and IRS)
Category 2 (middle) receive ITNs, IPT and case
management
Category 3 (low) maintain strategies towards
elimination agenda.
The reverse is true for performance indicators
measured in terms of achieving set targets.
22. • Reliable malaria density data is lacking in most
programs at levels where management decisions
are made.
• Research contributes to M&E of the malaria control
program’s impact on the prevalence at sub-district
or lower levels (parish), classifying these areas to
target cost-effective control interventions.
• Test MC-LQAS for malaria control (1st Time Use)
23. Aim : To assess malaria prevalence for priority cost-
effective and targeted interventions
Objectives
1. To classify and map malaria prevalence at the parish
level within the district.
2. To validate the utility of Multiple Classification LQAS
(MC-LQAS) during the survey.
3. To measure malaria control performance indicators
and coverage within the sub- counties and parishes.
4. To disseminate findings as evidence for decisions to
prioritize malaria intervention strategies.
24.
25. Ethical application completed and community
assent sought
Trained research assistants
Data collection through questionnaires and blood
samples for malaria test and Hb estimation
Sampling conducted to identify eligible child of
ages 6months to 9 years.
26.
27.
28.
29.
30.
31.
32. Analyzed 448 cases, 6 months to 9 years
◦ Malaria prevalence
◦ Malaria outcome indicators
Demonstrated high prevalence of malaria &
anemia, low coverage of interventions and their
performance, all with marked variations
Such variations is often masked from aggregate
measures reported in large country surveys
33.
34.
35.
36.
37.
38.
39.
40. MC-LQAS is effective to monitor malaria
endemicity and control interventions providing
reliable data and classification that can aid target
interventions.
It can be replicated
41. Use data generated as baseline and re-define
targets to monitor progress
Draw attention of the malaria situation to the
malaria control program
Replicate the studies
42. CCM management and staff
CCM Executive Director, Filippo Spagnuolo & Head of
Programs, Valeria Pecchioni
Professor Joseph Valadez, LSTM
Dr Olives, University of Washington
Professor Feiko ter Kuile, LSTM
ChildFund International, Uganda
MSH Uganda
Uganda Christian University, Mukono
Family support