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Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_117_библиотеки_им_акад_М_И_Перельмана
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Patient Data Analytics Using XAI: Existing Tools andCase Studies
https://t.me/med1917
Fig. 1 Illustration of XAI and traditional AI
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improvise and help humans. Section 1.8 discusses the future scope of XAI.Finally,
the last section, Sect. 1.9, concludes the chapter.
1.2 Why Is XAI Important inPatient Data Analytics?
PDA [9] constitutes a comprehensive framework for the acquisition, arrangement,
scrutiny, and elucidation of healthcare data from diverse origins, with the aim of
attaining invaluable insights and enhancing patient care. This framework encompasses the utilization of sophisticated methodologies and tools to distill signicant
insights from extensive reservoirs of patient data, including electronic health records
(EHRs), medical imagery, genetic information, wearable devices, and similar
sources. Within this context, XAI plays a pivotal role in ensuring transparency,
accountability, interpretability, adherence to regulatory standards, and the augmentation of operational workows within PDA.The visual representation of this role
is aptly captured in Fig.3.
1.2.1 Transparency
The predictions of AI models can only be trusted if they can explain to the patient
as well as the doctor the logic behind making such a prediction. Therefore, XAI
helps in revealing the background on how AI models make their decisions.
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Fig. 2 Impact areas of XAI
Fig. 3 Roles of XAI in PDA
1.2.2 Accountability
XAI can be accountable to patients because they allow auditing and validation, error
detection and correction, ensure ethical and legal compatibility, and allow continuous improvement by taking feedback.
Patient Data Analytics Using XAI: Existing Tools andCase Studies
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1.2.3 Interpretable
XAI systems provide patient understandable explanations for the analytics they
make. They allow patients to gain insights into the analytics the AI does.
1.2.4 Law Compliant
As the decision-making process is transparent and auditable, XAI systems can be
made to follow regulations, ethics, and legal practices of a nation, which was not
possible in AI models.
1.2.5 Patient Data Analytics Workow
The XAI-based PDA workows empower doctors to make informed decisions
about patient condition and thereby improves patient care.
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1.3 Challenges ofXAI inPDA
As a new technology, XAI has to overcome novel challenges to be a successful
concept. Here are some of the prominent challenges that XAI has to deal with.
1.3.1 Black Box Models
XAI has to understand the complex black box PDA models and interpret them to the
doctors as well as patients. Sometimes it will be hard for the doctors or patients to
understand the AI model’s prediction. This may limit the usefulness of XAI in PDA.
1.3.2 Bias
There may be negative consequences for patient care arising from the biased explanations given by XAI to the doctors as the XAI may fail to understand the true
relationships between data points.
1.3.3 Complexity
XAI techniques can be complex and difcult to implement, which can limit their
adoption by healthcare organizations.
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1.3.4 User Needs
A variety of users pose a variety of needs. Every user will have his/her own way of
looking at the XAI model. Hence, developing an XAI system that will be satisfying
all user needs is a highly challenging task.
1.3.5 Standards
Due to the lack of standards, comparing XAI models is a challenge.
1.3.6 Costs
XAI models require a lot of computing power that may prove to be costly for many
users and therefore the cost of developing and maintaining an XAI system is
challenging.
J. Srinivas et al.
1.4 Methods ofXAI
In this section the various methods available for implementing XAI for PDA are
introduced based on Refs. [10, 11]. XAI methods can be broadly classied into
intrinsically interpretable methods, model-agnostic methods, and example-based
explanations. Figure4 depicts the various XAI methods for PDA available in the
literature.
1.4.1 Intrinsically Interpretable Methods (IIMs)
These methods are built in such a manner that they are easily understandable by the
user right from their inception. Models built using decision trees, rule-based systems, and linear regression models are examples of intrinsically interpretable methods. Due to their easy interpretability, the possible challenges and predictions of the
model can be easily explained to the patient or doctor while doing PDA.Due to the
transparent nature of the IIMs, the predictions made by such models in PDA are
explainable. IIMs are robust and hence they do not pose any threat to existing data
or systems. Generalized linear models (GLMs) and generalized additive models
(GAMs), naïve Bayes classier based on Bayes theorem, and k-nearest neighbors
can also be categorized as IIM-based XAI methods.
Patient Data Analytics Using XAI: Existing Tools andCase Studies
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Fig. 4 Categorizations of XAI methods
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1.4.2 Model-Agnostic Methods (MAMs)
MAMs are more complex XAI methods than IIM-based methods but are more
explainable when compared to IIM-based methods. Most popular MAMs include
individual conditional expectation, Shapley values, partial dependence plot, accumulation local effects plot, feature importance, global surrogate, feature interaction,
and local surrogate.
The major strength of any MAM is their ability to separate explainability from
the AI model, making them compatible to run with multiple AI models. Table1
gives a brief description of these methods. It also lists the pros and cons of each of
these methods.
1.4.3 Example-Based Explanations (EBE)
These methods draw explanations for the predictions made by the black box AI
models by providing examples of data points that are similar to the input data point.
This helps in interpreting the logic behind a specic prediction made by the AI
model. Later these explanations can be used to generate new predictions. Suppose
“a datapoint P is the same as Q and Q caused R, so we can assume that P will also
cause R.” Some of the popular EBE techniques include counterfactual method,
adversarial method, prototypes, inuential instances, and k-nearest neighbor model.
Table2 lists the various features of EBE methods.
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Table 1 List of model-agnostic methods
Method Description Pros Cons
Partial
dependence plot
(PDP) [12]
Individual
conditional
expectation
(ICE) [13]
Accumulated
local effects
(ALE) plot
Feature
interaction
Feature
importance
Global surrogate Creates a simple model
Local surrogate
(LIME) [14]
Shapley values
(SHAP) [15]
Visualizes the relationship
between a single feature
and the predictions of the
black box model
Visualizes the change in
the prediction of the black
box model as a function
of a single feature
Visualizes the
accumulated effect of all
features on the prediction
of the black box model
Measures the interaction
between two or more
features
Measures the importance
of each feature to the
model’s predictions
that approximates the
predictions of the black
box model
Creates a local,
interpretable model that
approximates the
predictions of the black
box model around a
specic input
Attribution method that
assigns a value to each
feature based on its
contribution to the
prediction of the black
box model
Easy to understand, can
be used to identify
feature importance and
interactions
Can be used to identify
feature importance and
interactions
Easy to understand, can
be used to identify
feature importance and
interactions
Can be used to identify
features that are most
important to the
model’s predictions
Can be used to identify
features that are most
important to the
model’s predictions
Can be used to explain
the predictions of the
black box model in a
simple way
Can be used to explain
the predictions of the
black box model in a
local way
Can be used to explain
the predictions of the
black box model in a
comprehensive way
Can be computationally
expensive, may not be
accurate for complex
relationships
Can be computationally
expensive, may not be
accurate for complex
relationships
Can be computationally
expensive, may not be
accurate for complex
relationships
Can be computationally
expensive, may not be
accurate for complex
relationships
Can be biased toward
features with more data
points
Can be inaccurate, may
not be able to capture
complex relationships
Can be inaccurate, may
not be able to capture
complex relationships
Can be computationally
expensive, may not be
accurate for complex
relationships
J. Srinivas et al.
1.5 A Generic Architecture oftheXAI Model forPDA
This section of the chapter describes the generic architecture of any XAI model for
PDA that was mentioned in Ref. [8]. The architecture is similar to any AI model
except that in XAI a new block is introduced by the developer called explainability
for making the black box model into a relatively transparent model. This new block
transforms the black box to explainable AI.Figure5 represents a generic architecture of an XAI model for patient data analytics.
Patient Data Analytics Using XAI: Existing Tools andCase Studies
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Table 2 Description of EBE methods
Technique Description Pros Cons
Counterfactual
method
Adversarial
method
Prototypes Finds prototypes, which are
Inuential
instances
K-nearest
neighbors
(KNN)
Finds instances in the training
dataset that are similar to the
input data point, but have
different labels. The
differences between the two
instances are then used to
explain the prediction of the
black box model
Creates adversarial examples
that are similar to the input
data point, but have different
labels. The adversarial
examples are then used to
explain why the black box
model made the wrong
prediction
data points that are
representative of the different
classes. The prototypes are
then used to explain the
predictions of the black box
model
Finds inuential instances,
which are data points that
have a large impact on the
predictions of the black box
model. The inuential
instances are then used to
explain the predictions of the
black box model
Finds the k-nearest neighbors
of the input data point in the
training dataset. The labels of
the nearest neighbors are then
used to explain the prediction
of the black box model
Easy to understand,
can be used to
identify features that
are most important to
the model’s
predictions
Can be used to
identify
vulnerabilities in
black box models
Easy to understand,
can be used to
identify features that
are most important to
the model’s
predictions
Can be used to
identify data points
that are important for
the model’s
predictions
Easy to understand,
can be used to
identify features that
are most important to
the model’s
predictions
Can be computationally
expensive, may not be
accurate for complex
relationships
Can be computationally
expensive, may not be
accurate for complex
relationships
Can be inaccurate, as
the prototypes may not
be representative of all
of the data points in the
training set
Can be computationally
expensive, may not be
accurate for complex
relationships
Can be inaccurate, as
the nearest neighbors
may not be
representative of all of
the data points in the
training set
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The generic architecture of an XAI model comprises distinct components that
collectively contribute to its functioning. These components include the following:
Input block: This block represents the initial data or information fed into the
XAI model for analysis. In the context of healthcare, it may encompass patient data
encompassing electronic health records, medical images, genetic information, and
other relevant data sources.
Patient data: This constitutes the specic dataset related to the patient’s health
condition and medical history. It serves as the foundation upon which the XAI model’s analysis and decision-making process are based.
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Fig. 5 Generic architecture of an XAI model
AI model: The AI model block refers to the core articial intelligence algorithm
or model that processes the patient data to make predictions, recommendations, or
classications based on patterns and correlations found within the data.
XAI block: This block is central to the concept of eXplainable AI.It encompasses multiple subcomponents aimed at elucidating the inner workings of the AI
model, making its decision-making process more comprehensible and interpretable.
The XAI block generally consists of the following:
XAI model: This refers to the specic model or approach implemented to provide
explanations for the AI Model’s decisions. It could involve techniques like rulebased explanations or generating saliency maps to highlight inuential features.
XAI module: The XAI module serves as the intermediary component responsible
for processing the output of the AI model and generating the corresponding explanations. It applies XAI techniques to extract interpretable insights from the AI model’s output.
XAI interface: The XAI interface acts as the point of interaction between the AI
model’s predictions and the human users. It presents the explanations generated by
the XAI module in a user-friendly manner, aiding users in understanding the rationale behind the AI model’s decisions.
∂
()
∂
FX
Xi
Patient Data Analytics Using XAI: Existing Tools andCase Studies
https://t.me/med1917
Output block: This block represents the nal outcomes or results produced by
the AI model. In healthcare, these outcomes could be diagnoses, treatment recommendations, risk assessments, or other relevant insights.
The development of the XAI block is undertaken by the model developers. This
involves designing, implementing, and ne-tuning the XAI model, XAI module,
and XAI interface to ensure that the explanations provided are accurate, understandable, and valuable to users, seeking insights from the AI model’s predictions.
1.5.1 Mathematical Justication ofXAI
XAI aims to enhance the transparency and interpretability of AI models’ decisionmaking processes, making them more comprehensible to human users. This transparency is achieved through various techniques that provide insights into how an AI
model arrives at its predictions or decisions. One fundamental aspect of XAI
involves the use of feature importance scores or attribution methods. Let’s consider
a simple example with a binary classication problem. Given an input feature vector
X and a trained AI model F(X) that predicts the class label Y (0 or 1). Let Xi represent
the importance of each feature Xi. It is possible to compute Xi. One commonly used
method for computing Xi is the Gradient-based method, which calculates the gradient of the model’s prediction with respect to each input feature. Mathematically, the
gradient of F(X) with respect to Xi can be denoted as
. A higher magnitude
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of this gradient indicates that a change in Xi has a signicant impact on the model’s
output. These gradients can be used to assign important scores to each feature
reecting their contributions to the model’s prediction.
1.6 Toolkits andFrameworks
Most of the current XAI models in PDA casually explain the predictions of the
model rather than providing a statistical representation of the decision-making process. These models just expound on the features and blocks of the existing versions
of the AI model. These AI methods just provide approximation approaches as
Addons to the existing AI models. Usually, humans look for the proof of choices
that the AI model has made rather than some causal explanations. Interpretability
and fairness are very important in PDA models. This section of the chapter tries to
throw light on such models.
Hasoon etal. [16] proposed an XAI model for classication of X-ray images of
corona patients. The diagnosis model is easily interpretable as well as transparent.
The workow used for every decision made is clearly visible at each phase. The
model informs every aspect of the decision taken to the user. The features that have
contributed to the decision-making process are easily accessible by the users. A
human understandable explanation for the decisions made by a deep learning model
was designed in Ref. [17] by merging TREPAN decision tree [18] with a cluster of
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network’s hidden layer [19]. The paper focused on bringing forth the information
stream in the deep neural network. The idea was to demystify the deep neural network using human friendly features. A credit card default prediction use case was
taken for demonstrating XAI.
One more XAI model was developed in Ref. [20] for classifying estrogen receptor capability in breast images. XAI techniques such as integrated gradients
approach and SmoothGrad noise algorithm were employed to visualize the learning
abilities of the deep-learning convolutional neural networks. This XAI-based PDA
approach motivates other researchers to transfer their black box-based AI PDA
models into more transparent XAI models.
AIX 360 [21], Alibi [22], Skater [23], H2O [24], InterpretML [25], ethical-MLXAI [26], DaleX [27], and iNNvestigate [28] are all different tools and frameworks
for explaining and understanding machine learning models. Table3 lists the various
features of these XAI frameworks. AIX 360 is a commercial tool from IBM that
provides a variety of explainability features, including counterfactual explanations,
local explanations, and global explanations.
It can be used to generate counterfactual explanations and to compare the performance of different models. Skater is an open-source tool that helps to understand
the behavior of machine learning models. It can be used to generate decision trees,
saliency maps, and other visualizations of model predictions. H2O is an opensource machine learning platform that includes a number of explainability features.
These features can be used to understand the importance of individual features in a
model, as well as the overall structure of the model. InterpretML is an open-source
tool that helps to explain machine learning models. It can be used to generate local
explanations, global explanations, and counterfactual explanations. Ethical-MLXAI is a collection of open-source tools for explaining and understanding machine
learning models. It includes tools for generating local explanations, global explanations, and counterfactual explanations. DaleX is an open-source tool that helps to
explain machine learning models. It can be used to generate local explanations,
global explanations, and counterfactual explanations. iNNvestigate is an opensource tool that helps to understand the behavior of machine learning models. It can
be used to generate decision trees, saliency maps, and other visualizations of model
predictions. On similar lines, a group of researchers proposed a “support vector
machine”-based method for identifying illness using image processing techniques
[29]. Enhanced hunt optimization-based deep learning [30] methodology was proposed recently by a group of investigators for arrhythmia classication. The research
also employed Internet of Things concepts to get a better accuracy rate. Deep learning was also used by a group of investigators for exploring despair eccentricities [31].
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