Patent Application
20190340505
This application claims priority to European Application No. 18170554.2, having a filing date of May 3, 2018, the entire contents of which are hereby incorporated by reference.
The following relates to a method and system of determining influence of attributes in Recurrent Neural Networks (RNN) trained on therapy prediction. Specifically, a method using Layer-wise Relevance Propagation (LRP) is disclosed which enables determining the specific influence of attributes of patients used as input to RNNs on the predicted or suggested therapy.
The increasing data volume and variety pose nowadays novel challenges for predictive data analysis. Especially in the task of processing data features of higher dimensionality and complexity, deep neural networks like RNNs have proven to be powerful approaches. They outperform more traditional methods that rely on hand-engineered representations of data on a wide range of problems varying from image classification over machine translation to playing video games. To a large extent, the success of deep neural networks is attributable to their capability to represent the raw data features in a new and latent space that facilitates the predictive task. Deep neural networks are also applicable in the field of healthcare informatics. Convolution neural networks (CNNs), for instance, can be applied for classification and segmentation of medical imaging data. RNNs are efficient in processing clinical events data. The predictive power of these RNNs can assist physicians in repetitive tasks such as annotating radiology images and reviewing health records. Thus, the physicians can concentrate on the more intellectually challenging and creative tasks.
However, healthcare remains a critical area where deep neural networks or machine learning models have to be applied with great caution. The fact that the internal functionality of or in other words the way results in form of suggestions are generated by (not necessarily deep) neural networks is not directly explainable limits application of (deep) neural networks in healthcare informatics. The General Data Protection Regulation (GDPR) of the European Union (EU) of May 2018 restricts automated decision making produced inter alia by algorithms. According to Article 13(2)(f) GDPR “Information to be provided where personal data are collected from the data subject” a data controller (e.g. clinics or physicians) should provide the data subject (e.g. patients) with information about “the existence of automated decision-making, including profiling, referred to in Article 22(1), (4) GDPR” and “meaningful information about the logic involved”. According to Article 22(1), (2)(c) GDPR “Automated individual decision-making, including profiling” the data subject/patient “shall have the right not to be subject to a decision based solely on automated processing”, unless, the data subject/patient is explicitly consent with it. Therefore, a data subject/patient has the right to demand an explanation not only of the predicted/suggested therapy, but also of the method which generates this prediction/suggestion. For clinics/physicians in the EU, the GDPR demands as a mandatory component in clinical services providing explanation where neural networks or machine learning or any other algorithmic logic is applied to generate decision prediction.
Depending on the (deep) neural network and specifically on its complexity or depth the (deep) neural network has a certain expressiveness or, in other words, power. The expressiveness of a (deep) neural network describes how many attributes e.g. of a patient can be used and how many relationships between said attributes can be recognized and considered in deriving the prediction/suggestion of a decision like a certain therapy.
For (deep) neural networks a linear and logistic regression, where normally there is a distribution assumption for regression coefficients and statistical tests to quantify whether a coefficient is significantly different from 0 are performed, cannot be used, because, there is no distribution assumption for the weight parameters (regression coefficients) of the (deep) neural network and therefore no statistical tests are applicable. One approach to describe (deep) neural networks is the Mimic Learning Paradigm (MLP) which aims to simplify the model or neural network, respectively. The MLP suggests training a simple (e.g. linear regression) model against a predicted value produced by a trained deep neural network until the simple model over-fits. MLP thus provides a simple and interpretable model. Overfitting is in general a simpler task in machine learning. However, finding a simple or shallow (linear regression) model for high dimensional and complex data is challenging. Further, due to simplification the expressiveness is possibly drastically reduced compared to the deep neural network. Hence, the predictions/suggestions made by such a simplified (deep) neural network could be falsified. Another approach for explaining (deep) neural networks, specifically RNNs and Convolutional Neural Networks (CNNs), is the Attention Mechanism (AM) which aims to still further complicate the (deep) neural network. Additional modules are included into the (deep) neural network. Said additional modules learn to assign an attention score on each time step or pixel groups. The AM provides interpretation of the relevance of the input features (e.g. attributes of a patient) and can sometimes increase prediction quality as well. One drawback is that by introducing additional modules the (deep) neural networks becomes more complex and thus require longer training time and more labelled data.
The input data features of a RNN trained on therapy prediction or suggestion, respectively, are attributes of patients. The attributes of patients can comprise inter alia personal data (age, weight, ethnicity, etc.), information about a primary tumour (type, size, location, etc.), laboratory values (coagulation markers (PT/INR), organ markers (liver enzyme count, liver function markers, kidney values, pancreatic markers (lipase, amylase), muscular markers, myocardial muscular markers, metabolism markers (bone markers (alkaline phosphatase, calcium, phosphate), fat metabolism markers (cholesterol, triglycerides, HDL cholesterol, LDL cholesterol), iron, diabetes marker (glucose)), immune defence/inflammation values (inflammation marker (CRP), immunoglobulin (IgG, IgA, IgM), proteins in serum, electrolytes)), genetic attributes or clinical image data (MRT/CT images). These attributes are provided as binary values in a high-dimensional and very spares matrix for each patient. The dimensionality of said matrix can be from tens to multiple thousands and the sparsity can be equal or higher than 90% [percent] or equal or higher than 93%. Said input data features (patient attributes) of a RNN trained on therapy prediction are different from input data of a CNN trained on classification and segmentation of clinical image data which is provided as non-sparse or dense and low-dimensional matrix of pixels. A nonsparse/dense matrix is a matrix where most entries have a value different from 0 e.g. pixel values from 0 to 256 in a matrix of image data. This difference in the input data features of the RNN trained on therapy prediction lead to significant differences in computation. In case of image data a strong spatial correlation among neighbouring pixels can be expected. This is definitely not the case with electronic healthcare records (EHR) included in input data features of a RNN trained on therapy prediction or suggestion. For such data sequential models such as RNNs are used. Embodiments of the invention consequently applies LRP on EHR data.
An aspect relates to explaining predictions of RNNs trained on therapy prediction based on attributes of patients (patient attributes) in form of binary values in a high-dimensional and very sparse matrix. A further aspect of embodiments of the present invention is to preserve as much as possible of the expressiveness of architectures of RNNs, while the complexity of training (time and amount of data for training) is not significantly increased.
These objectives are achieved by the method according to claim 1 and the system according to the further independent claim. Refinements of embodiments of the present invention are object of the dependent claims.
According to a first aspect of embodiments of the present invention a method of determining influence of attributes in Recurrent Neural Networks (RNN) having 1 layers, where l is 1 to L, and time steps t, where t is 1 to T, and trained on therapy prediction, comprising the following steps starting at time step T:
According to a second aspect of embodiments of the present invention a system configured to determine influence of attributes in Recurrent Neural Networks, RNN, having 1 layers, where 1 is 1 to L, and time steps t, where t is 1 to T, and trained on therapy prediction, comprises at least one memory. The layers l are stored in the at least one memory or in different memories of the system. The system further comprises an interface configured to receive the layers l of an input-to-hidden network of the RNN, an input vector xl of size M for the first layer l=1 comprising input features for the RNN and a first relevance score RkL of size M for each output neuron zk, where k is 1 to N, and configured to output a sequence of relevance scores Rjl|t of the respective first layer l=1 of all time steps t. The system also comprises a processing unit. The processing unit is configured to execute the following iterative steps for each layer l starting at layer L:
The processing unit is further configured to execute the following step:
The system according to embodiments of the present invention is configured to implement the method according to embodiments of the present invention.
In order to explain the RNN trained on therapy prediction the RNN is left as it is. The RNN is not simplified or complicated by introduction of further modules. Instead a Layer-wise Relevance Propagation (LRP) algorithm is used on the RNN. Weight parameters pk,j in the RNN are analysed in order to determine how much influence each input feature/patient attribute has on the final prediction/suggestion of a therapy. In contrast to a sensitivity analysis according to AM, which calculates a partial derivative of each input feature with respect to (w.r.t.) the target, according to embodiments of the present invention investigation of the p-values of regression coefficients, which test whether the regression coefficients are significantly zero, or the nodes in decision trees of the RNN is based on statements that a specific input feature/patient attribute is in general relevant for the prediction. The attention modules and the relevance propagation of AM, on the other hand, suggest how relevant each input feature is for a specific data point.
A basic idea in LRP is to decompose the predicted probability of a specific target like a suggested treatment into a set of relevance scores Rkl and redistribute them onto neurons of the previous layer of the RNN and finally onto the j input features/patient attributes xj of the first layer. The relevance scores Rkl are defined in terms of the strength of the connection between one input feature/patient attribute xjl of the first layer l=1 or (input) neuron xjl of a layer 1 and one (output) neuron zkl of the first or current layer l, respectively, which is represented by the weight pk,jl and the activation of the one (input) neuron xjl or of the (output) neuron zkl−1 in the previous layer l−1 or of the one input feature/patient attribute xjl. In each layer l of the RNN the relevance score Rkl can be seen as a kind of contribution that each (input) neuron xjl or (output) neuron zkl−1 of the previous layer l−1 of the RNN or input feature/patient attribute xjl gives to each (output) neuron zkl of the current or first layer l of the RNN. This approach is applied recurrently, in other words from an output layer l=L down to the input layer l=1 such that a relevance score Rk→jl for each input feature/patient attribute xjl is derived. This LRP is applied on real-world healthcare data in form of patient attributes which are binary values in a high-dimensional and very sparse matrix. A RNN trained to predict therapy decisions such that the prediction quality is close to that of a clinical expert. These decisions predicted/suggested by the RNN are explained using LRP. Thus it can be validated, that the derived predicted/suggested decisions regarding a therapy of a patient largely accord with the actual clinical knowledge and guidelines.
The RNN may have up to some hundred layers l. The maximal number of layers L is equal or larger than 20 and the maximal number of layers L is equal or higher than 30. The input vector x denotes input data features, here attributes of a patient, for the first layer of the RNN and activated output. M and N may be different for each layer l of the RNN. Thus for each layer l the specific values of M and of N have to be determined from the respective layer l. The size of the layer l, namely the values M and N vary a lot. The values M and N can have values between 1 and multiple thousands and between tens and thousands. The first relevance score RkL is equivalent to the predicted probability of the model. The last hidden state h|t refers to the hidden state of previous time step, namely h|t−1 that itself depends on the pre-previous hidden state h|t−2 and the previous input x|t−1.
In step a) the layers l of the RNN are received. Further the input vector xl for the first layer l=1 is received. The layers l are stored in the at least one memory of the system. The input vector xl comprises input features for the RNN like patient attributes. Also the first relevance score RkL for the last layer l=L is received. After receiving the input values for the method in step a) via the interface, namely the layers l, the input vector xl and the first relevance score RkL, the consecutive steps b), c) and d) are executed for each layer l of the RNN in the processing unit of the system, wherein the layer L is the first layer of the iteration and the layer l is the last layer of the iteration. Thereby, for each layer l the relevance score Rjl for the next step or layer l−1 is determined based on the relevance score Rkl of the present step/layer l. In each step l of the iteration over all layers l firstly for each output neuron k of the present layer l of the RNN proportions pk,jl are determined for each input vector xl. Each of the proportions pk,jl is based on a respective component xjl of the input vector xl. Further, each of the proportions pk,jl is based on a weight wk,jl for the respective component xjl, which weight wk,jl is known from the respective layer l. Finally, each of the proportions pk,jl is based on the respective output neuron zkl of the present layer l of the RNN.
In the successive step c) the relevance score Rkl is decomposed for each output neuron k of the present layer l. The relevance score Rkl for the present layer is derived from a relevance score Rjl of the previous step or layer. In the very first step L for layer L the first relevance score RkL is given as input from step a). The relevance score Rkl is decomposed into decomposed relevance scores Rk→jl for each component xjl of the input vector xl based on the proportions pk,jl from the respective preceding step b). Finally, in the successive step d) the decomposed relevance scores Rk→jl are combined to the relevance score Rjl f or the next step or layer l−1. After the iteration of steps b) to d) has been executed over all layers L of the RNN, the steps e) and f) are executed. According to step e), which is also executed on the processing unit, the steps a) and the iteration of steps b) to d) are executed for the next time step t−1, wherein this iteration begins with time step T. For step e) the layers l for the iteration of steps b) to d) are he layers l of a hidden-to-hidden network of the RNN for the next time step t−1. Further, the input vector xl is a last hidden state h|t, which is based on the output neuron z|t of the RNN of the previous time step t. Finally, the first relevance score RkL is a relevance score of the previous hidden state Rjl|t which is the last relevance score Rjl of the first layer l=1 of the previous time step t. After the iteration of steps b) to d) is finished for each layer l of the respective hidden-to-hidden network of the RNN the sequence of relevance scores Rjl|t of the respective first layer l=1 of all time steps t is output via the interface.
Thus, explaining predictions of RNNs trained on therapy prediction based on attributes of patients (patient attributes) in form of binary values in a high-dimensional and very sparse matrix is enabled. Further, as much as possible of the expressiveness of architectures of RNNs is preserved, while the complexity of training (time and amount of data for training) is not significantly increased.
According to a further aspect of embodiments of the present invention in step b) executed on the processing unit the respective output neuron k is determined by the input vector xl and a respective weight vector wkl.
Here, the RNN comprises fully connected layers l. Fully connected layers have relations between all input neurons j and all output neurons k. Thereby, each input neuron xjl influences each output neuron zkl of the respective layer l of the RNN. The fully connected layers l can be denoted as
z
l
=W
l
·x
l
+b
In this equation x1 either denotes the output neurons zkl−1 of a preceding layer l−1 or the input data features for the very first layer l=1 xjl as input neurons of the layer l. The matrix Wl contains all weights wk,jl for the respective layer l. Further, zl denotes the output neurons zkl of the respective layer l. Further, b is a constant value, the so-called bias or intercept, and can be disregarded.
According to a further aspect of embodiments of the present invention in step b) executed on the processing unit stabilizers are introduced to avoid numerical instability.
In Numerical calculations very high numbers can cause instabilities and lead to false or no data. Especially divisions through very small values can lead to said very high numbers. In order to avoid such instabilities, stabilizers of the form
ε·sign(zk)
can be introduced to the equation for calculation of the proportions pk,jl for each input vector xl:
ε can be in the range of e−2 to e−6.
By introducing said stabilizers false data in or abortion of the calculations for explaining predictions of RNNs trained on therapy prediction can be avoided.
According to a further aspect of embodiments of the present invention the RNN is a simple RNN or a Long Short-Term Memory, LSTM, network or a Gated Recurrent Unit, GRU, network.
LSTM and GRU are all RNNs that model time sequences. LSTM and GRU are specifically suitable for memorizing long temporal patterns (from a longer time ago).
Some of the embodiments will be described in detail, with references to the following Figures, wherein like designations denote like members, wherein:
In
z
l
=W
l
·x
l
+b
In this equation x1 either denotes the output neurons zkl−1 of a preceding layer l−1 or the input data features for the very first layer l=1 xjl as input neurons of the layer l. The matrix Wl contains all weights wk,jl for the respective layer l. Further, zl denotes the output neurons zkl of the respective layer l. Further, b is a constant value, the so-called bias or intercept, and can be disregarded. In a first step a) the layers l of an input-to-hidden network of the RNN are received. Further, an input vector xl for the first layer l=1 is received. The input vector xl comprises input features for the RNN like patient attributes. A first relevance score RkL for each output neuron zk, where k is 1 to N. Each relevance score Rkl for the respective layer l can represents a kind of contribution that each (input) neuron xjl−1 of the previous layer l−1 of the RNN or input feature/patient attribute xj0 gives to each (output) neuron zkl of the current or first layer l of the RNN. The following steps b), c) and d) are iteratively executed for each layer 1=L . . . 1 of the RNN starting with the last layer L. In step b) for each output neuron zkl proportions pk,jl for each input vector xl are determined. The proportions pk,jl can be calculated as:
The proportions pk,jl are thus each based on a respective component xjl of the input vector xl, a weight wk,jl for the respective component xjl and the respective output neuron zkl. The weight wk,jl is known from the respective layer l. Additionally, stabilizers can be introduced to avoid numerical instability. In order to avoid such instabilities, stabilizers of the form
ε·sign(zk)
can be introduced to the equation for calculation of the proportions pk,jl for each input vector xl:
ε can be in the range of e−2 to e−6. In step c) a relevance score Rkl is decomposed for each output neuron zkl into decomposed relevance scores Rk→jl for each component xjl. The decomposing is based on the proportions pk,jl from preceding step b).
R
k→j
l
=p
k,j
l
·R
k
l
The relevance score Rkl is known from a relevance score Rjl+1 of the previous step l+1 or the first relevance score RkL in step/layer l=L. The relevance score Rkl is the sum of the decomposed relevance scores Rk→*jl over all input neurons xjl.
In step d) all decomposed relevance scores Rk→jl of the present step or layer l are combined to the relevance score Rjl for the next step/level l−1. The relevance score Rjl is the sum of the decomposed relevance scores Rk→jl overall output neurons zkl
After all relevance scores Rjl for all layers l=L . . . 1 are calculated, the iteration is exited and step e) is executed.
In step e) the steps a) to d) are repeated for the different time steps t=1 . . . T of the RNN. Thus, step e) is a further iteration over the time steps t, starting with time step T. For the steps a) to d) of the iteration of step e) the layers l are the layers l of a hidden-to-hidden network of the RNN for the next time step t−1, the input vector xl is a last hidden state h|t, which is based on the output neuron z|t of the RNN of the previous time step t, and the first relevance score RkL is a relevance score of the previous hidden state Rjl|t which is the last relevance score Rjl of the first layer l=1 of the previous time step t. After the steps a) and the steps b to d) of the iteration over the layers l have been executed for each time step t of the iteration of step e) the step f) is executed, wherein a sequence of relevance scores Rjl|t of the respective first layer l=1 of all time steps t is output.
The method described above can be implemented on a system 10 as schematically depicted in
In
The method and system according to embodiments of the present invention were tested with data provided by the PRAEGNANT study network. The data was collected on recruited patients suffering from metastatic breast cancer. 1048 patients were selected for training of the RNN and 150 patients were selected for testing the method and system according to embodiments of the present invention, all of which meet the first line of medication therapy, and have positive hormone receptor and negative HER2. This criterion is of clinical relevance, in that only antihormone therapy or chemotherapy are possible, and even the physicians have to debate over some of these patient cases. On each patient 199 static features were retrieved that encode, 1) demographic information, 2) the primary tumour and 3) metastasis before being recruited in the study. These features form for each patient i a feature vector mi, i∈{0, 1}199. Further their time-stamped clinical event data were included as sequential features, such as 4) clinic visits, 5) diagnosed metastasis and 6) received therapies. For the ith patient these sequential features were encoded using an ordered set {xi[t]}t=1Ti where each xi[t]{0, 1}189 Ti denotes the number of clinical events observed on the patient i, i.e., the length of the sequence. Here Ti is between 0 and 15, and is on average 3.03.
Among the static features, there are originally four numerical values, including the age, the number of positive cells of oestrogen receptor, the number of positive cells of progesterone receptor and the Ki-67 value. This poses a novel challenge to the application of LRP algorithm, because the consistency of the relevance propagation is only guaranteed, if all input features are in the same space. To this end, two kinds of stratification are applied to transform the numerical features. For the feature of age all patients are stratified into three groups of almost identical size, using the 33.3% and 66.7% quantiles. On the other hand it is referred to clinical practices to handle the other three features. The number of positive cells of oestrogen receptor, for instance, is stratified in two groups using one threshold of 20%. Because a percent smaller than this threshold can be a hint for chemotherapy, if a number of other criteria are fulfilled as well. The same also applies to the Ki-67 value with a threshold of 30%.
The model which is applied to predict the therapy decision consists of a LSTM with embedding layer and a feed-forward network. Due to the sparsity and dimensionality of xi[t] first an embedding layer is deployed, denoted with function γ( ), which is expected to learn a latent representation si[t]. An LSTM λ(·) then consumes these sequential latent representations as input. It generates at the last time step Ti another representation vector, which is expected to encode all relevant information from the entire sequence. Recurrent neural networks, such as LSTM, are able to learn a fixed-size vector from sequences of variable sizes. From the static features mi, which is also sparse and high dimensional, a representation with a feed-forward network η(·) is learned. Both representations are concatenated to a vector hi, which represents all relevant information on patient I up to time step t. Finally, the vector hi serves as input to a logistic regression that predicts the probability that the patient should receive either antihormone (1) or chemotherapy (0).
The training set is split into 5 mutual exclusive sets to form 5-fold cross-validation pairs. For one of the pairs hyper-parameter tuning is performed and the model on is trained on the other 4 pairs as well. The model is applied with the best validation performance in term of accuracy on the test set. The performances are listed in Tab. 1.
With the same schema a strong baseline model is reported, which is a two-layered feed-forward network consuming the concatenation of mi, and the aggregated sequential features
The results are listed in Tab. 2.
Also weak baselines such as random prediction and the most-popular prediction are included in Tab. 3.
The latter one constantly predicts the more popular decision in the training set for all test cases. Furthermore, a clinician was asked to evaluate 69 of the 150 test cases, in that he should decide for each patient between antihormone and chemotherapy. 75.4% of the re-evaluations turn out to agree with the ground truth, while the present model achieves 81.2% accuracy. This clinical validation is based on a relative small patient set. However, it demonstrates that a seemingly simple decision task between antihormone and chemotherapy is not always trivial even for physicians, in that a physician may not agree with her/his colleague, or even with herself/himself at another time point, in one quarter of all cases. The method according to embodiments of the present invention achieves prediction performance that is comparable with human decisions. More importantly, while it is extremely expensive and demanding to for physicians to (re-)evaluate so many patient cases at once, a computer program can be utilized for the task anytime necessary. The computer program can be a computer program product, comprising a computer readable hardware storage device having compute readable program code stored therein, said program code executable by a processor of a computer system to implement a method.
In order to explain the prediction of the model the relevance score is calculated w.r.t. the correctly predicted class, respectively. Tab. 4 and 5 summarize the static features that are most frequently identified to have contributed to the prediction of antihormone and chemotherapy, respectively, in the test set.
Recalling that the patients are known to have positive hormone receptors, antihormone therapy seems to be the default decision. This fact is supported, for instance, by the features of “positive oestrogen receptor status” (2nd) and “positive cells of oestrogen receptor ≥20%” (5th) in Tab. 4. The 8th feature, the age group, suggests that the eldest patients should receive antihormone therapy.
This also agrees with clinical knowledge that chemotherapy often results in severe side-effect should be prescribed with caution to elder patients. However, it is much more interesting to study what the features that result in a chemotherapy decision are, because an antihormone therapy seems to be the default decision for such patient cohort.
In Tab. 5, features such as “primary tumour malignant invasive” (1st), “Ki-67 value IHC≥30%” (11th), that describe an invasive primary tumour that suggests chemotherapy are found. Features like “G3 grading” (7th) and the metastasis in lungs, liver and lymph nodes (3rd, 4th and 5th) depict a late stage of the metastasis. The patient features of “age group: young” and “postmenopausal” are also identified to have contributed to the prediction. All these factors agree with the clinical knowledge, as well as guidelines in handling metastatic breast cancer with chemotherapy.
Tab. 6 and Tab. 7 list the sequential features that are frequently marked as relevant for the respective prediction. The event feature that belongs to an type is denoted using a colon. For instance, “medication therapy: antihormone therapy” means a medication therapy that has a feature of antihormone type.
In Tab. 6 the features “curative radiotherapy” (1st) and surgeries (2nd, 4th and 5th) indicate an early stage of the cancer. Because the physicians have undergone therapies that aim at curing the primary tumour. The features of “no metastasis in liver” (7th) and “first lesion metastasis in lungs” (8th) suggest an early phase in the development of the metastasis, which also indicates an optimistic therapy situation.
In Tab. 7, however, features are observed that support a decision for chemotherapy. Specifically, “a complete remission of metastasis” (2nd) and “local recurrence in the breast” (3rd) are hints of a progressing cancer which, considering other patient features in Tab. 5, would lead to a decision for chemotherapy.
In Tab. 8 for each event type, such as local recurrence, radiotherapy, etc., all relevance scores for antihormone and chemotherapy, respectively are summarized.
The first row in the Tab. 8, for instance, can be interpreted such that, if the patients have experienced a local recurrence, she/he should receive chemotherapy instead of an antihormone therapy (0.772 vs. −0.193). Another dominating decision criterion is given by the metastasis (4th row): according to the LRP algorithm, the fact that metastasis is observed in the past also strongly suggests chemotherapy instead of an antihormone therapy (3.657 vs. −1.192), which again agrees with clinical guidelines. It is, however, not always appropriate to interpret each feature independently. A clinical therapy decision might be an extremely complicated one. The interactions between the features could result in a decision that is totally different from the one that only takes into account a single feature.
A patient case A, confer Tab. 9, received an antihormone therapy, which the model correctly predicts with a probability of 0.754.
One observes 4 events before this decision was due. The LRP algorithm assigns high relevance scores to the fact that she had a bone metastasis before being recruited in the study. Bone metastasis is seen as an optimistic metastasis, because there exist a variety of bone specific medications that effectively treat this kind of metastasis. Also the event of curative radiotherapy, which is assigned with a high relevance score, hints a good outcome of the therapy. Considering the patient is in the 3rd age group as well, it is often recommended in such cases to prescribe antihormone therapy. For this specific patient, the LRP algorithm turns out to have identified relevant features that accord with clinical guidelines.
A patient B, see Tab. 10, was prescribed chemotherapy, which the model predicted with a probability of 0.916.
Seven events have been observed before this therapy decision was due. The static features that have been identified as relevant for the chemotherapy show a strong pattern of metastasis, including brain, lung and other locations. The identified sequential features include invasive local recurrences in the breast and axilla. Based on general clinical knowledge and guideline, for such a young patient with quite malignant tumour, chemotherapy seems indeed appropriate. Furthermore, it is also interesting to see that the feature of being postmenopausal has a negative relevance for the decision antihormone therapy in case A, while a positive one for the chemotherapy in case B. In other words, being postmenopausal always supports the decision of chemotherapy, which agrees with clinical knowledge and guidelines.
Although the present invention has been disclosed in the form of preferred embodiments and variations thereon, it will be understood that numerous additional modifications and variations could be made thereto without departing from the scope of the invention.
For the sake of clarity, it is to be understood that the use of ‘a’ or ‘an’ throughout this application does not exclude a plurality, and ‘comprising’ does not exclude other steps or elements.
| Number | Date | Country | Kind |
|---|---|---|---|
| 18170554.2 | May 2018 | EP | regional |
