MULTI MODEL DATA MINING APPROACH FOR HEART FAILURE PREDICTIONIJDKP
Developing predictive modelling solutions for risk estimation is extremely challenging in health-care
informatics. Risk estimation involves integration of heterogeneous clinical sources having different
representation from different health-care provider making the task increasingly complex. Such sources are
typically voluminous, diverse, and significantly change over the time. Therefore, distributed and parallel
computing tools collectively termed big data tools are in need which can synthesize and assist the physician
to make right clinical decisions. In this work we propose multi-model predictive architecture, a novel
approach for combining the predictive ability of multiple models for better prediction accuracy. We
demonstrate the effectiveness and efficiency of the proposed work on data from Framingham Heart study.
Results show that the proposed multi-model predictive architecture is able to provide better accuracy than
best model approach. By modelling the error of predictive models we are able to choose sub set of models
which yields accurate results. More information was modelled into system by multi-level mining which has
resulted in enhanced predictive accuracy.
List out the challenges of ml ai for delivering clinical impact - PubricaPubrica
Pubrica explores the main challenges and limitations of AI in healthcare and considers the steps required to translate these potentially transformative technologies from research to clinical practice.
Continue Reading: https://bit.ly/3o4hjPT
Reference: https://pubrica.com/services/research-services/biostatistics-and-statistical-programming-services/
Why Pubrica?
When you order our services, Plagiarism free|on Time|outstanding customer support|Unlimited Revisions support|High-quality Subject Matter Experts.
Contact us :
Web: https://pubrica.com/
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WhatsApp : +91 9884350006
United Kingdom: +44- 74248 10299
A comprehensive study on disease risk predictions in machine learning IJECEIAES
Over recent years, multiple disease risk prediction models have been developed. These models use various patient characteristics to estimate the probability of outcomes over a certain period of time and hold the potential to improve decision making and individualize care. Discovering hidden patterns and interactions from medical databases with growing evaluation of the disease prediction model has become crucial. It needs many trials in traditional clinical findings that could complicate disease prediction. A Comprehensive study on different strategies used to predict disease is conferred in this paper. Applying these techniques to healthcare data, has improvement of risk prediction models to find out the patients who would get benefit from disease management programs to reduce hospital readmission and healthcare cost, but the results of these endeavors have been shifted.
Modernizing Legacy Systems in Healthcare: A Comprehensive GuideLucy Zeniffer
Modernizing Legacy Systems in Healthcare: A Comprehensive Guide" offers practical insights into upgrading outdated healthcare technology. Exploring strategies, challenges, and benefits, this guide empowers healthcare professionals with the knowledge to navigate the complexities of system modernization. From enhancing efficiency to improving patient care, it provides a roadmap for embracing innovation in healthcare IT infrastructure.
INTEGRATING MACHINE LEARNING IN CLINICAL DECISION SUPPORT SYSTEMShiij
This review article examines the role of machine learning (ML) in enhancing Clinical Decision Support
Systems (CDSSs) within the modern healthcare landscape. Focusing on the integration of various ML
algorithms, such as regression, random forest, and neural networks, the review aims to showcase their
potential in advancing patient care. A rapid review methodology was utilized, involving a survey of recent
articles from PubMed and Google Scholar on ML applications in healthcare. Key findings include the
demonstration of ML's predictive power in patient outcomes, its ability to augment clinician knowledge,
and the effectiveness of ensemble algorithmic approaches. The review highlights specific applications of
diverse ML models, including moment kernel machines in predicting surgical outcomes, k-means clustering
in simplifying disease phenotypes, and extreme gradient boosting in estimating injury risk. Emphasizing
the potential of ML to tackle current healthcare challenges, the article highlights the critical role of ML in
evolving CDSSs for improved clinical decision-making and patient care. This comprehensive review also
addresses the challenges and limitations of integrating ML into healthcare systems, advocating for a
collaborative approach to refine these systems for safety, efficacy, and equity.
INTEGRATING MACHINE LEARNING IN CLINICAL DECISION SUPPORT SYSTEMShiij
This review article examines the role of machine learning (ML) in enhancing Clinical Decision Support
Systems (CDSSs) within the modern healthcare landscape. Focusing on the integration of various ML
algorithms, such as regression, random forest, and neural networks, the review aims to showcase their
potential in advancing patient care. A rapid review methodology was utilized, involving a survey of recent
articles from PubMed and Google Scholar on ML applications in healthcare. Key findings include the
demonstration of ML's predictive power in patient outcomes, its ability to augment clinician knowledge,
and the effectiveness of ensemble algorithmic approaches. The review highlights specific applications of
diverse ML models, including moment kernel machines in predicting surgical outcomes, k-means clustering
in simplifying disease phenotypes, and extreme gradient boosting in estimating injury risk. Emphasizing
the potential of ML to tackle current healthcare challenges, the article highlights the critical role of ML in
evolving CDSSs for improved clinical decision-making and patient care. This comprehensive review also
addresses the challenges and limitations of integrating ML into healthcare systems, advocating for a
collaborative approach to refine these systems for safety, efficacy, and equity.
MULTI MODEL DATA MINING APPROACH FOR HEART FAILURE PREDICTIONIJDKP
Developing predictive modelling solutions for risk estimation is extremely challenging in health-care
informatics. Risk estimation involves integration of heterogeneous clinical sources having different
representation from different health-care provider making the task increasingly complex. Such sources are
typically voluminous, diverse, and significantly change over the time. Therefore, distributed and parallel
computing tools collectively termed big data tools are in need which can synthesize and assist the physician
to make right clinical decisions. In this work we propose multi-model predictive architecture, a novel
approach for combining the predictive ability of multiple models for better prediction accuracy. We
demonstrate the effectiveness and efficiency of the proposed work on data from Framingham Heart study.
Results show that the proposed multi-model predictive architecture is able to provide better accuracy than
best model approach. By modelling the error of predictive models we are able to choose sub set of models
which yields accurate results. More information was modelled into system by multi-level mining which has
resulted in enhanced predictive accuracy.
List out the challenges of ml ai for delivering clinical impact - PubricaPubrica
Pubrica explores the main challenges and limitations of AI in healthcare and considers the steps required to translate these potentially transformative technologies from research to clinical practice.
Continue Reading: https://bit.ly/3o4hjPT
Reference: https://pubrica.com/services/research-services/biostatistics-and-statistical-programming-services/
Why Pubrica?
When you order our services, Plagiarism free|on Time|outstanding customer support|Unlimited Revisions support|High-quality Subject Matter Experts.
Contact us :
Web: https://pubrica.com/
Blog: https://pubrica.com/academy/
Email: sales@pubrica.com
WhatsApp : +91 9884350006
United Kingdom: +44- 74248 10299
A comprehensive study on disease risk predictions in machine learning IJECEIAES
Over recent years, multiple disease risk prediction models have been developed. These models use various patient characteristics to estimate the probability of outcomes over a certain period of time and hold the potential to improve decision making and individualize care. Discovering hidden patterns and interactions from medical databases with growing evaluation of the disease prediction model has become crucial. It needs many trials in traditional clinical findings that could complicate disease prediction. A Comprehensive study on different strategies used to predict disease is conferred in this paper. Applying these techniques to healthcare data, has improvement of risk prediction models to find out the patients who would get benefit from disease management programs to reduce hospital readmission and healthcare cost, but the results of these endeavors have been shifted.
Modernizing Legacy Systems in Healthcare: A Comprehensive GuideLucy Zeniffer
Modernizing Legacy Systems in Healthcare: A Comprehensive Guide" offers practical insights into upgrading outdated healthcare technology. Exploring strategies, challenges, and benefits, this guide empowers healthcare professionals with the knowledge to navigate the complexities of system modernization. From enhancing efficiency to improving patient care, it provides a roadmap for embracing innovation in healthcare IT infrastructure.
INTEGRATING MACHINE LEARNING IN CLINICAL DECISION SUPPORT SYSTEMShiij
This review article examines the role of machine learning (ML) in enhancing Clinical Decision Support
Systems (CDSSs) within the modern healthcare landscape. Focusing on the integration of various ML
algorithms, such as regression, random forest, and neural networks, the review aims to showcase their
potential in advancing patient care. A rapid review methodology was utilized, involving a survey of recent
articles from PubMed and Google Scholar on ML applications in healthcare. Key findings include the
demonstration of ML's predictive power in patient outcomes, its ability to augment clinician knowledge,
and the effectiveness of ensemble algorithmic approaches. The review highlights specific applications of
diverse ML models, including moment kernel machines in predicting surgical outcomes, k-means clustering
in simplifying disease phenotypes, and extreme gradient boosting in estimating injury risk. Emphasizing
the potential of ML to tackle current healthcare challenges, the article highlights the critical role of ML in
evolving CDSSs for improved clinical decision-making and patient care. This comprehensive review also
addresses the challenges and limitations of integrating ML into healthcare systems, advocating for a
collaborative approach to refine these systems for safety, efficacy, and equity.
INTEGRATING MACHINE LEARNING IN CLINICAL DECISION SUPPORT SYSTEMShiij
This review article examines the role of machine learning (ML) in enhancing Clinical Decision Support
Systems (CDSSs) within the modern healthcare landscape. Focusing on the integration of various ML
algorithms, such as regression, random forest, and neural networks, the review aims to showcase their
potential in advancing patient care. A rapid review methodology was utilized, involving a survey of recent
articles from PubMed and Google Scholar on ML applications in healthcare. Key findings include the
demonstration of ML's predictive power in patient outcomes, its ability to augment clinician knowledge,
and the effectiveness of ensemble algorithmic approaches. The review highlights specific applications of
diverse ML models, including moment kernel machines in predicting surgical outcomes, k-means clustering
in simplifying disease phenotypes, and extreme gradient boosting in estimating injury risk. Emphasizing
the potential of ML to tackle current healthcare challenges, the article highlights the critical role of ML in
evolving CDSSs for improved clinical decision-making and patient care. This comprehensive review also
addresses the challenges and limitations of integrating ML into healthcare systems, advocating for a
collaborative approach to refine these systems for safety, efficacy, and equity.
Changing Medical profession with Artifical Intelligence what it means to us Dr.T.V.Rao MD
•Artificial Intelligence fast penetrating to every system and modality of human living However the implications of Artificial Intelligence is truly different from other professions we should be more aware of the ongoing matters and chose what is good in Human and health care ?
•Dr.T.V.Rao MD
•Former professor of Microbiology
•Adviser and Member Associate Elsevier research Netherlands
Big data, RWE and AI in Clinical Trials made simpleHadas Jacoby
Technology is slowly but surely penetrating the healthcare industry in general and the clinical trials sector in particular. New and advanced solutions offer a variety of possibilities aimed to both improving existing processes and creating new and more efficient ones. And on top of all stands the desire to make clinical trials more patient centric.
In all of this, even though some of the technologies have yet to mature enough to meet the high quality standards necessary, it is important to know them and begin imagining the promise they hold for clinical trials.
Artificial Intelligence in Healthcare Future Outlook.pdfSoluLab1231
Artificial Intelligence (AI) has emerged as a transformative force in healthcare, revolutionizing how medical professionals diagnose, treat, and manage patient care. AI is making a significant impact on multiple facets of the healthcare industry:
Enhanced Diagnostics: AI-driven diagnostic tools sift through extensive databases, identifying subtle patterns and anomalies, leading to earlier disease detection and improved patient outcomes.
Personalized Treatment Plans: AI algorithms analyze vast amounts of data to tailor treatment strategies to individual needs, considering factors such as genetics, lifestyle, and medical history.
Virtual Health Assistants: AI-powered virtual health assistants offer real-time symptom analysis, medication reminders, and preliminary health advice, enhancing accessibility to healthcare services and facilitating proactive self-care.
Drug Discovery and Development: AI expedites the drug discovery process by analyzing chemical databases and predicting potential drug candidates, reducing the time and cost associated with traditional drug development.
ICU Patient Deterioration Prediction : A Data-Mining Approachcsandit
A huge amount of medical data is generated every da
y, which presents a challenge in analysing
these data. The obvious solution to this challenge
is to reduce the amount of data without
information loss. Dimension reduction is considered
the most popular approach for reducing
data size and also to reduce noise and redundancies
in data. In this paper, we investigate the
effect of feature selection in improving the predic
tion of patient deterioration in ICUs. We
consider lab tests as features. Thus, choosing a su
bset of features would mean choosing the
most important lab tests to perform. If the number
of tests can be reduced by identifying the
most important tests, then we could also identify t
he redundant tests. By omitting the redundant
tests, observation time could be reduced and early
treatment could be provided to avoid the risk.
Additionally, unnecessary monetary cost would be av
oided. Our approach uses state-of-the-art
feature selection for predicting ICU patient deteri
oration using the medical lab results. We
apply our technique on the publicly available MIMIC
-II database and show the effectiveness of
the feature selection. We also provide a detailed a
nalysis of the best features identified by our
approach.
ICU PATIENT DETERIORATION PREDICTION: A DATA-MINING APPROACHcscpconf
A huge amount of medical data is generated every day, which presents a challenge in analysing
these data. The obvious solution to this challenge is to reduce the amount of data without
information loss. Dimension reduction is considered the most popular approach for reducing
data size and also to reduce noise and redundancies in data. In this paper, we investigate the
effect of feature selection in improving the prediction of patient deterioration in ICUs. We
consider lab tests as features. Thus, choosing a subset of features would mean choosing the
most important lab tests to perform. If the number of tests can be reduced by identifying the
most important tests, then we could also identify the redundant tests. By omitting the redundant
tests, observation time could be reduced and early treatment could be provided to avoid the risk.
Additionally, unnecessary monetary cost would be avoided. Our approach uses state-of-the-art
feature selection for predicting ICU patient deterioration using the medical lab results. We
apply our technique on the publicly available MIMIC-II database and show the effectiveness of
the feature selection. We also provide a detailed analysis of the best features identified by our
approach.
Machine literacy and artificial intelligence( AI) have become potent tools that are transforming several industries, including healthcare. The integration of AI and machine literacy in healthcare has opened up new possibilities, transubstantiating the way medical professionals diagnose, treat, and watch for cases. These technologies have the eventuality to enhance delicacy, effectiveness, and patient issues while also addressing difficulties in healthcare delivery. From individual and imaging analysis to prophetic analytics and substantiated treatment, AI and machine literacy offer promising advancements. Still, along with the openings come ethical debates, sequestration enterprises, and the need for official fabrics to insure responsible and transparent use of these technologies. In this composition, we will claw into the role of AI and machine literacy in healthcare, exploring their significance, benefits, and impact on colorful aspects of the assiduity.
An AI-based Decision Platform built using unified data model, incorporating systems biology topics for unit analysis using semi-supervised learning models
The convergence of separate health systems has led to
a great increase in data, which some organisations are
struggling to get to grips with. Harnessing analytic tools
and sharing knowledge is the best way forward
A Personal Privacy Data Protection Scheme for Encryption and Revocation of Hi...Shakas Technologies
A Personal Privacy Data Protection Scheme for Encryption and Revocation of High-Dimensional Attri
Shakas Technologies ( Galaxy of Knowledge)
#11/A 2nd East Main Road,
Gandhi Nagar,
Vellore - 632006.
Mobile : +91-9500218218 / 8220150373| land line- 0416- 3552723
Shakas Training & Development | Shakas Sales & Services | Shakas Educational Trust|IEEE projects | Research & Development | Journal Publication |
Email : info@shakastech.com | shakastech@gmail.com |
website: www.shakastech.com
Facebook: https://www.facebook.com/pages/Shakas-Technologies
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Changing Medical profession with Artifical Intelligence what it means to us Dr.T.V.Rao MD
•Artificial Intelligence fast penetrating to every system and modality of human living However the implications of Artificial Intelligence is truly different from other professions we should be more aware of the ongoing matters and chose what is good in Human and health care ?
•Dr.T.V.Rao MD
•Former professor of Microbiology
•Adviser and Member Associate Elsevier research Netherlands
Big data, RWE and AI in Clinical Trials made simpleHadas Jacoby
Technology is slowly but surely penetrating the healthcare industry in general and the clinical trials sector in particular. New and advanced solutions offer a variety of possibilities aimed to both improving existing processes and creating new and more efficient ones. And on top of all stands the desire to make clinical trials more patient centric.
In all of this, even though some of the technologies have yet to mature enough to meet the high quality standards necessary, it is important to know them and begin imagining the promise they hold for clinical trials.
Artificial Intelligence in Healthcare Future Outlook.pdfSoluLab1231
Artificial Intelligence (AI) has emerged as a transformative force in healthcare, revolutionizing how medical professionals diagnose, treat, and manage patient care. AI is making a significant impact on multiple facets of the healthcare industry:
Enhanced Diagnostics: AI-driven diagnostic tools sift through extensive databases, identifying subtle patterns and anomalies, leading to earlier disease detection and improved patient outcomes.
Personalized Treatment Plans: AI algorithms analyze vast amounts of data to tailor treatment strategies to individual needs, considering factors such as genetics, lifestyle, and medical history.
Virtual Health Assistants: AI-powered virtual health assistants offer real-time symptom analysis, medication reminders, and preliminary health advice, enhancing accessibility to healthcare services and facilitating proactive self-care.
Drug Discovery and Development: AI expedites the drug discovery process by analyzing chemical databases and predicting potential drug candidates, reducing the time and cost associated with traditional drug development.
ICU Patient Deterioration Prediction : A Data-Mining Approachcsandit
A huge amount of medical data is generated every da
y, which presents a challenge in analysing
these data. The obvious solution to this challenge
is to reduce the amount of data without
information loss. Dimension reduction is considered
the most popular approach for reducing
data size and also to reduce noise and redundancies
in data. In this paper, we investigate the
effect of feature selection in improving the predic
tion of patient deterioration in ICUs. We
consider lab tests as features. Thus, choosing a su
bset of features would mean choosing the
most important lab tests to perform. If the number
of tests can be reduced by identifying the
most important tests, then we could also identify t
he redundant tests. By omitting the redundant
tests, observation time could be reduced and early
treatment could be provided to avoid the risk.
Additionally, unnecessary monetary cost would be av
oided. Our approach uses state-of-the-art
feature selection for predicting ICU patient deteri
oration using the medical lab results. We
apply our technique on the publicly available MIMIC
-II database and show the effectiveness of
the feature selection. We also provide a detailed a
nalysis of the best features identified by our
approach.
ICU PATIENT DETERIORATION PREDICTION: A DATA-MINING APPROACHcscpconf
A huge amount of medical data is generated every day, which presents a challenge in analysing
these data. The obvious solution to this challenge is to reduce the amount of data without
information loss. Dimension reduction is considered the most popular approach for reducing
data size and also to reduce noise and redundancies in data. In this paper, we investigate the
effect of feature selection in improving the prediction of patient deterioration in ICUs. We
consider lab tests as features. Thus, choosing a subset of features would mean choosing the
most important lab tests to perform. If the number of tests can be reduced by identifying the
most important tests, then we could also identify the redundant tests. By omitting the redundant
tests, observation time could be reduced and early treatment could be provided to avoid the risk.
Additionally, unnecessary monetary cost would be avoided. Our approach uses state-of-the-art
feature selection for predicting ICU patient deterioration using the medical lab results. We
apply our technique on the publicly available MIMIC-II database and show the effectiveness of
the feature selection. We also provide a detailed analysis of the best features identified by our
approach.
Machine literacy and artificial intelligence( AI) have become potent tools that are transforming several industries, including healthcare. The integration of AI and machine literacy in healthcare has opened up new possibilities, transubstantiating the way medical professionals diagnose, treat, and watch for cases. These technologies have the eventuality to enhance delicacy, effectiveness, and patient issues while also addressing difficulties in healthcare delivery. From individual and imaging analysis to prophetic analytics and substantiated treatment, AI and machine literacy offer promising advancements. Still, along with the openings come ethical debates, sequestration enterprises, and the need for official fabrics to insure responsible and transparent use of these technologies. In this composition, we will claw into the role of AI and machine literacy in healthcare, exploring their significance, benefits, and impact on colorful aspects of the assiduity.
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Explainable Artificial Intelligence for Patient Safety A Review of Application in Pharmacovigilance.docx
1. Base paper Title: Explainable Artificial Intelligence for Patient Safety: A Review of
Application in Pharmacovigilance
Modified Title: Understandable AI for Patient Safety: An Examination of Its Use in
Pharmacovigilance
Abstract
Explainable AI (XAI) is a methodology that complements the black box of artificial
intelligence, and its necessity has recently been highlighted in various fields. The purpose of
this research is to identify studies in the field of pharmacovigilance using XAI. Though there
have been many previous attempts to select papers, with a total of 781 papers being confirmed,
only 25 of them manually met the selection criteria. This study presents an intuitive review of
the potential of XAI technologies in the field of pharmacovigilance. In the included studies,
clinical data, registry data, and knowledge data were used to investigate drug treatment, side
effects, and interaction studies based on tree models, neural network models, and graph models.
Finally, key challenges for several research issues for the use of XAI in pharmacovigilance
were identified. Although artificial intelligence (AI) is actively used in drug surveillance and
patient safety, gathering adverse drug reaction information, extracting drug-drug interactions,
and predicting effects, XAI is not normally utilized. Therefore, the potential challenges
involved in its use alongside future prospects should be continuously discussed.
Existing System
The World Health Organization defines pharmacovigilance (PV) as the science and
activities related to the detection, assessment, understanding, and prevention of adverse effects
or other drug-related problems [1]. Recent artificial intelligence-based technologies can be an
efficient complement to traditional PV methods, which can be costly and time-consuming and
can result in adverse drug reactions (ADRs) that go unreported to healthcare professionals.
Artificial intelligence (AI) can improve PV, but its use in PV is still in the early stages of
research. Various machine learning (ML) techniques, together with natural language
processing and data mining, can be applied to electronic health records, claims databases and
social media data to improve the characterization of known drug side effects and reactions, and
to detect new signals [2], [3]. AI-based technologies have been criticized for their inexplicable
algorithms, despite their high predictive power. In critical decision areas such as healthcare,
2. the reasoning behind a decision is as important as the decision itself, which is why there is
growing interest in and research and development around Explainable Artificial Intelligence
(XAI). XAI was developed to improve the transparency of AI systems and generate
explanations for them, and seeks to increase trust and understanding by assessing the strengths
and limitations of existing models [4], [5], [6]. Approaches that extract information from a
model’s decision-making process, such as post-hoc explanations, can provide useful
information for practitioners and users interested in caseby-case explanations rather than the
internal workings of a model [7]. XAI increases the explainability and transparency of AI
algorithms by making it possible to interpret the variables that influence decisions, complex
internal features, and learned decision paths within a decision process [8], [9]. I.R. Ward et al.
successfully quantified the importance of features using an XAI algorithm, further
demonstrating the potential contribution of XAI to PV monitoring [10].
Drawback in Existing System
Interpretability vs. Performance Trade-off:
There is often a trade-off between model interpretability and performance.
Simplifying a model for better interpretability might lead to a decrease in predictive
accuracy. Striking the right balance between model complexity and interpretability is a
crucial challenge.
Limited Standardization:
Lack of standardized methods for explaining AI models in healthcare is a significant
drawback. The absence of uniform guidelines makes it difficult to compare different
XAI techniques and hinders widespread adoption.
Dynamic Nature of Healthcare Data:
Healthcare data is dynamic and evolves over time. XAI models may struggle to adapt
to changes in patient profiles, treatment protocols, and disease patterns. The ability to
update models in real-time is a challenge that needs to be addressed.
3. Resource Intensiveness:
Developing and maintaining XAI models can be resource-intensive. Many healthcare
organizations, especially smaller ones, may face challenges in terms of the expertise
and financial resources required for the implementation and upkeep of such systems.
Proposed System
Data Collection and Preprocessing:
Collect and preprocess relevant patient data, including medical histories, treatment
records, and adverse event reports. Ensure data quality, address missing values, and
normalize variables.
Feature Selection and Engineering:
Identify relevant features and potentially engineer new ones that contribute to the
predictive model. Feature engineering can enhance the model's ability to capture
important patterns in the data.
Integration of XAI Techniques:
Implement XAI techniques to provide explanations for model predictions. This could
involve methods such as LIME, SHAP values, or attention mechanisms in neural
networks. The chosen technique depends on the selected model and the interpretability
requirements.
Real-Time Monitoring and Alerts:
Implement a system for real-time monitoring of patient data and model predictions.
If adverse events or anomalies are detected, generate alerts for healthcare professionals
to intervene promptly.
4. Algorithm
Decision Trees:
Decision trees are a straightforward and interpretable algorithm used in XAI. They
represent a series of decisions in a tree-like structure, where each node represents a
decision based on a specific feature. The path from the root to a leaf node represents
the decision process, making it easy to interpret.
Logistic Regression:
Logistic Regression is a linear model commonly used for binary classification
problems. It estimates the probability of an event occurring based on input features. The
coefficients of the model can be interpreted to understand the impact of each feature on
the outcome.
Neural Networks with Attention Mechanisms:
In deep learning, attention mechanisms have been incorporated into neural networks
to highlight the importance of specific input features. This can help make neural
networks more interpretable by showing which parts of the input data are crucial for a
particular prediction.
Advantages
Enhanced Trust and Acceptance:
XAI provides clear and understandable explanations for its predictions or decisions,
fostering trust among healthcare professionals, regulatory bodies, and patients. The
transparency helps users better comprehend the AI system's reasoning, leading to
increased acceptance and confidence in its recommendations.
Identification of Biases and Errors:
XAI algorithms can help uncover biases and errors in the data or the model itself.
By understanding how the model arrives at its decisions, stakeholders can identify and
address any biases that might exist in the training data, reducing the risk of biased or
discriminatory outcomes in patient safety decisions.
5. Real-Time Monitoring and Adaptation:
XAI models can be designed to provide real-time explanations for their predictions.
This capability allows healthcare professionals to monitor changes in patient conditions
and treatment responses promptly. The adaptability of XAI systems can contribute to
more effective pharmacovigilance practices.
Facilitates Continuous Improvement:
XAI promotes a continuous improvement cycle by allowing stakeholders to analyze
and refine models based on the provided explanations. This iterative process helps
enhance the accuracy and reliability of the AI system over time, aligning it more closely
with the evolving needs of patient safety in pharmacovigilance.
Software Specification
Processor : I3 core processor
Ram : 4 GB
Hard disk : 500 GB
Software Specification
Operating System : Windows 10 /11
Frond End : Python
Back End : Mysql Server
IDE Tools : Pycharm