=Paper=
{{Paper
|id=Vol-3740/paper-123
|storemode=property
|title=ALSFRS-R Score Prediction for Amyotrophic Lateral Sclerosis
|pdfUrl=https://ceur-ws.org/Vol-3740/paper-123.pdf
|volume=Vol-3740
|authors=Guido Barducci,Flavio Sartori,Giovanni Birolo,Tiziana Sanavia,Piero Fariselli
|dblpUrl=https://dblp.org/rec/conf/clef/BarducciSBSF24
}}
==ALSFRS-R Score Prediction for Amyotrophic Lateral Sclerosis==
https://ceur-ws.org/Vol-3740/paper-123.pdf
ALSFRS-R Score Prediction for Amyotrophic Lateral
Sclerosis
Notebook for the iDPP Lab on Intelligent Disease Progression Prediction at CLEF 2024
Guido Barducci1 , Flavio Sartori1 , Giovanni Birolo1 , Tiziana Sanavia1 and Piero Fariselli1
1
Computational Biomedicine Unit, Dept. of Medical Sciences, University of Turin, Turin, Italy
Abstract
Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disorder that results in the gradual
deterioration of motor abilities, leading to challenges in breathing, speaking, swallowing, and ultimately death,
typically occurring within a few years. The symptoms of ALS can vary significantly from one individual to
another, affecting various bodily functions and areas. To assess this wide range of symptoms, the Amyotrophic
Lateral Sclerosis Functional Rating Scale - Revised (ALSFRS-R) is utilized. Predicting the ALSFRS-R score is
clinically relevant for personalizing patient monitoring. To address this need, the Intelligent Disease Progression
Prediction challenge was organized, tasking participants with developing novel methods to predict these scores
using non-invasive sensor data that monitor some individual characteristics. The competition included two
tasks that differed only in the way the ALSFRS-R questionnaires were completed: either by medical staff (task
1) or by the patient (task 2). Given the limited number of patients on the training set, it was decided to use a
relatively simple model, Random Forest, and to preselect sensor features by retaining those most correlated with
the outcome to be predicted. We selected the model with the lowest MAE estimated by cross-validation on the
challenge training set. The competition results demonstrate that our method attained on the test set an average
Mean Absolute Error (MAE) of 0.234 and 0.311, along with a Root Mean Square Error (RMSE) of 0.519 and 0.601
for tasks 1 and 2, respectively. Although the error may appear very low, this is because questionnaire values tend
to remain constant from one visit to another, thus facilitating prediction.
Keywords
Machine Learning, ALS, ALSFRS-R
1. Introduction
Amyotrophic lateral sclerosis (ALS), also known as neuropathy, is a rapidly progressive and ultimately
fatal neurological disease that affects the neurons controlling voluntary muscles in the arms, legs, and
face. The yearly incidence of ALS is around 1 to 2.6 cases per 100, 000 individuals, while the prevalence
is approximately 6 cases per 100, 000. ALS belongs to a group of motor neuron disorders and typically
results in death. Previous studies report approximately 48% and 24% survival rates at 3 and 5 years
respectively, with around 4% surviving beyond 10 years. However, population-based studies show
lower 5-year survival rates, ranging from 4% to 30% [1] [2].
The symptoms of this disease can vary greatly from case to case and can affect different functions
and areas of the body. To describe this wide range of symptoms, the Amyotrophic Lateral Sclerosis
Functional Rating Scale - Revised (ALSFRS-R) is employed. It consists of a 12-item inventory, with each
item rated on a 0–4 scale by patients and/or caregivers, resulting in a maximum score of 48 points.
ALSFRS-R assesses patients’ levels of self-sufficiency in areas including feeding, grooming, ambulation,
and communication [1] [3] .
Given the variability of this disease, monitoring checks should vary depending on its characteristics,
such as the progression rate. Currently, there is no system capable of predicting the course of the
disease, making it very challenging to personalize patient visits based on disease progression. The goal
CLEF 2024: Conference and Labs of the Evaluation Forum, September 09–12, 2024, Grenoble, France
$ guido.barducci@unito.it (G. Barducci); flavio.sartori@unito.it (F. Sartori); giovanni.birolo@unito.it (G. Birolo);
tiziana.sanavia@unito.it (T. Sanavia); piero.fariselli@unito.it (P. Fariselli)
0009-0005-1052-8495 (G. Barducci); 0009-0004-3833-6551 (F. Sartori); 0000-0003-0160-9312 (G. Birolo);
0000-0003-3288-0631 (T. Sanavia); 0000-0003-1811-4762 (P. Fariselli)
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
of this paper is to utilize information collected through the sensors of a commercial fitness smartwatch,
past ALSFRS-R scores, and static features (such as age, sex, etc.) to predict future ALSFRS-R scores. The
questionnaire data available has two different sources: they can be filled out by a doctor or by the patient
through the use of a dedicated smartphone application. Therefore, the challenge has been divided into
two tasks with the same goal but using data from different sources characterized by different frequencies
of intervals between one questionnaire and the next, as well as differing medical or personal opinions,
which may lead to different scoring choices despite similar symptoms. To solve these tasks, classical
machine learning models were used instead of deep learning given the small number of patients in the
training dataset. For more details we refer the reader to the challenge overview papers [4, 5].
The paper is divided into the following sections: 2 Related Work, which reports some papers address-
ing topics similar to this work; 3 Methodology, where the entire procedure that led to the predictions
for the two tasks is outlined; 4 Experimental Setup, which details the procedures used; 5 Results, where
the obtained results along with performance metrics are presented; and 6 Conclusions and Future Work,
which reviews the essential steps of the paper and proposes alternative methodologies that could be
useful for improving predictions.
2. Related Work
The quest to identify prognostic factors and build predictive models for amyotrophic lateral sclerosis
(ALS) progression has been a longstanding challenge, but one of paramount importance. ALS exhibits
significant variability in its progression and outcomes, posing obstacles to making accurate predictions.
Many methodologies have undergone rigorous testing using data from the PRO-ACT database. While
this repository may not perfectly capture the full spectrum of ALS patients in the population, it stands
as the largest publicly accessible dataset amalgamating ALS clinical trials [6] [7] [8].
Wearable devices have been effectively used to study individuals with ALS, demonstrating a link
between ALS progression and behavior and function patterns in people with amyotrophic lateral
sclerosis, as measured by digital wearables [9] [10] [11] [12]. These measurements include total activity
volume, active versus sedentary time, and time spent at home. Additionally, wearable devices are
increasingly utilized to investigate physical activity in populations with cardiovascular disease, multiple
sclerosis, arthritis, and other conditions. Although studies have been conducted to predict characteristics
related to the ALSFRS score, such as its score and slope, to date, there are no predictors leveraging data
from smartwatches to predict the ALSFRS score [13] [8]. Hence, it is imperative to investigate such
data types extensively to ascertain if they can enhance diagnostic predictions.
3. Methodology
Three different types of data were used to predict ALSFRS-R scores: sensor data from Garmin VivoActive
4 smartwatches, static feature data, and ALSFRS-R questionnaire data. Regarding sensor data, these
consist of 90 different features per day and are characterized by a large number of missing values (in
many days, no features were recorded, rendering the sensor vector absent for those days) due to both
the data collection device and patient behavior. The static data are baseline characteristics recorded at a
specific time, they include: sex, diagnostic delay, age at diagnosis, forced vital capacity (FVC), weight,
and body mass index (BMI). Finally, the ALSFRS-R data are of the same type as those to be predicted
but collected at a previous time.
To leverage these challenging data, two different approaches have been explored: the Mono Window
approach and the Double Window approach. The Mono Window approach is the simpler of the two:
for each prediction, only the sensors recorded within 7 days prior to the questionnaire to be predicted
are used (these can be utilized in various ways, such as averaging or taking the median). The second
approach involves considering two sensor data windows instead of one: the first window adjacent
to the questionnaire to be predicted, and the second adjacent to the previous available questionnaire.
The idea behind this second method is to provide the model with more information about the changes
Frequencies of Residues Values in the Training ALSFRS-R Data Frequencies of Residues Values in the Training ALSFRS-R Data
4.0 3.0 2.0 1.0 0.0 -1.0 -2.0 -3.0 -4.0
0.8
-3.0
0.8
-2.0 0.7 0.7
0.6 0.6
0.5
0.0 -1.0
0.5
Value
Value
0.4 0.4
0.3 0.3
1.0
0.2 0.2
0.1 0.1
3.0
0.0 0.0
Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Q10 Q11 Q12 Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Q10 Q11 Q12
Score name Score name
(a) Task 1 (b) Task 2
Figure 1: Frequencies of residue values in the training ALSFRS-R data for tasks 1 and 2. As you can see, the
most frequent value is 0 for each task. Consequently, the majority of questionnaire values remain unchanged
compared to the previous ones.
in recorded parameters over time. Despite the large amount of sensor data (13.946 feature vectors)
and the fact that the second method seems more natural for handling this type of data, it is heavily
penalized by the irregularity of the sensors. Indeed, two 7-day windows with at least 3 days of sensor
data were not available in 20 out of 54 patients. For this reason, the choice to use the first approach
was mandatory. In this process, for each patient, all sensor data outside the temporal window of the 7
days preceding the ALSFRS-R values to be predicted was disregarded. The sensor data within each time
window were averaged along the time to obtain one feature vector of length 90 for each window that is
less affected by daily variability. Once the feature vectors representing the sensor data were obtained,
they were concatenated with the vectors of static features and with the vectors of previous ALSFRS-R
data before the questionnaires to be predicted; by doing this the the final feature vectors were obtained.
Regarding the outcomes to predict, these are the values of ALSFRS-R questionnaires after the last
ones available for the training. To solve this task, it has been observed that the questionnaire values
tend to remain constant between visits (see Figure 1); therefore, it was decided to use the previous
time’s questionnaire score as the prediction baseline and to fit the model on the residuals. To obtain
the final prediction value, it was sufficient to add the predicted residual to the value of the previous
questionnaire relative to the one to be predicted1 . The Random Forest Classifier was chosen as the
model; unlike deep learning models, it does not require large datasets for training, making it suitable
for this task. Before being used to fit the model, the data were preprocessed by scaling and performing
feature selection, as explained in the section 4.
4. Experimental Setup
The training dataset for this challenge comprises data from 52 different patients for both tasks. These
data can be categorized into three types: static data, ALSFRS-R data, and data from a smartwatch sensor.
By analyzing the correlation matrices, two important facts can be observed: as for the ALSFRS-R data, it
can be observed that they can be divided into several correlated groups depending on the area affected by
the disease (see Figure 2). Meanwhile, regarding the sensor data, there are strongly correlated features,
as shown in the figure 3. As shown in the image, the correlated features are grouped into distinct blocks.
For the cardiac features, two main blocks can be identified: the first, smaller block pertains to Heart Rate
Variability (HRV), while the second block encompasses other cardiac characteristics related to the RR
interval. For the respiratory features, two blocks are associated with respiration and blood oxygenation.
Lastly, another block of correlated features pertains to the patient’s steps. The grouping of features
1
The samples in the training set corresponding to a residual value occurring fewer than 8 times have been discarded; indeed,
they are too few to be recognized by the model.
Qi correlation matrix in the Training set Qi correlation matrix in the Training set
Q1 1.0 Q1 1.0
Q2 Q2
Q3 0.8 Q3 0.8
Q4 Q4
Q5 0.6 Q5 0.6
Q6 Q6
Q7 0.4 Q7 0.4
Q8 Q8
Q9 0.2 Q9 0.2
Q10 Q10
Q11 0.0 Q11 0.0
Q12 Q12
Q1
Q2
Q3
Q4
Q5
Q6
Q7
Q8
Q9
Q10
Q11
Q12
Q1
Q2
Q3
Q4
Q5
Q6
Q7
Q8
Q9
Q10
Q11
Q12
(a) Task 1 (b) Task 2
Figure 2: Correlation matrix of ALSFRS-R values in the training data for both task 1 and task 2. They reveal that
the scores can be clustered into distinct groups. This indicates significant correlations among certain ALSFRS-R
items, suggesting underlying patterns or relationships within the data.
into these blocks is expected, as they represent different statistics describing the same physiological
processes.
The two tasks differ only in terms of the subject and the frequency with which the questionnaires
are filled out. This makes the two tasks slightly different primarily for two reasons: the data from the
first task should reasonably be more objective, as it is a clinician who fills out the questionnaires rather
than the individual patient. Additionally, the data from the second task are compiled through an app
and have therefore a higher and more irregular frequency, as depicted in Figure 4.
Regarding data preprocessing, the features were initially scaled using Min-Max Scaler and imputed
with the mean or mode depending on their continuous or categorical nature. Concerning the sensor data,
an additional process was added: the correlation between each of these features and the questionnaire
to be predicted was calculated, and only those features with a correlation above a certain threshold
were retained. This approach was chosen due to the low number of samples available to train the
model compared to the total number of features. After the data preprocessing, they were used to train a
Random Forest Classifier. To determine the optimal hyperparameters of the model2 , along with the
correlation threshold utilized to select the sensor features3 , the following cross-validation strategy was
employed. The training set of the challenge was divided into an inner training set and a validation set
(80-20%). Hyperparameter optimization was conducted via cross-validation on the inner training set
using a grid search method4 , and the chosen hyperparameters for each the tasks are displayed in Table
1 and Table 2.
5. Results
The challenge was divided into two tasks, Task 1 and Task 2, each involving the construction of a
model to predict the values of ALSFRS-R questionnaires completed by either a clinician or the patient
using a dedicated smartphone application. Despite testing two different macro types of methods: Mono
Window and Double Window, the low number of patients in the training set resulted in significantly
lower performance for the second type. Therefore, we only submitted results from the first type. These
2
The Random Forest hyperparameters tested were maxing deep and max features; they have been tested respectively in ranges
[2, 9] and [sqrt, ’log2’, None]. The number of trees was fixed at 300.
3
The thresholds tested has been [0, 0.05, 0.1, 0.15, 0.2, 0.25].
4
During the fold creation process, care was taken to obtain stratified folds for outcomes to ensure partitions with similar
percentages for each outcome category.
Sensors correlation matrix in the Training set
measure_days_from_diagnosis 1.00
basal_calories
beat_to_beat_C2a
beat_to_beat_Ca
beat_to_beat_GI
beat_to_beat_IALS 0.75
beat_to_beat_PI
beat_to_beat_PSS
beat_to_beat_SD1d
beat_to_beat_SDNNd
beat_to_beat_csi
beat_to_beat_cvi 0.50
beat_to_beat_hcvNN
beat_to_beat_madNN
beat_to_beat_medianNN
beat_to_beat_pNN50
beat_to_beat_sd1
beat_to_beat_sd2 0.25
beat_to_beat_sdNNI1
beat_to_beat_sdNNI5
beat_to_beat_sdaNN2
beat_to_beat_sdsd
heart_rate_linear_trend 0.00
heart_rate_maximum
heart_rate_min_time
heart_rate_quadratic_trend
heart_rate_std
respiration_DFA_alpha1
respiration_RMSSD 0.25
respiration_SD2
respiration_SDBB
respiration_SampEn
respiration_alpha1_DimRange
respiration_alpha1_ExpRange
respiration_alpha2_DimRange 0.50
respiration_alpha2_ExpRange
spo2_AODmax
spo2_CA
spo2_DI
spo2_MED
spo2_ODI 0.75
spo2_POD
spo2_SD
steps_12_am-6_am
steps_6_am-12_pm
total_steps
respiration_alpha1_ExpRange
1.00
respiration_alpha2_ExpRange
heart_rate_quadratic_trend
measure_days_from_diagnosis
respiration_RMSSD
respiration_alpha1_DimRange
respiration_alpha2_DimRange
respiration_SD2
respiration_SDBB
respiration_SampEn
basal_calories
beat_to_beat_C2a
beat_to_beat_Ca
beat_to_beat_GI
beat_to_beat_IALS
beat_to_beat_PI
beat_to_beat_PSS
beat_to_beat_SD1d
beat_to_beat_SDNNd
beat_to_beat_csi
beat_to_beat_cvi
beat_to_beat_hcvNN
beat_to_beat_madNN
beat_to_beat_pNN50
beat_to_beat_sd1
beat_to_beat_sd2
beat_to_beat_medianNN
beat_to_beat_sdNNI1
beat_to_beat_sdNNI5
beat_to_beat_sdaNN2
beat_to_beat_sdsd
heart_rate_linear_trend
heart_rate_std
respiration_DFA_alpha1
spo2_MED
spo2_CA
spo2_DI
spo2_ODI
spo2_POD
spo2_SD
heart_rate_maximum
heart_rate_min_time
steps_12_am-6_am
steps_6_am-12_pm
total_steps
spo2_AODmax
Figure 3: Sensors Correlation Matrix.
were obtained using a Random Forest Classifier, which was trained after determining the optimal
hyperparameters through five-fold cross-validation; the average metrics for each ALSFRS-R question
over the 5 validation folds are reported in Table 3 for the first task and in Table 4 for the second one.
6. Conclusions and Future Work
The models attempted to predict the ALSFRS-R questionnaire values were constrained by the small
size of the training dataset. Despite experimenting with models utilizing various time intervals, the
only ones proving useful for prediction were those solely relying on a window of sensor data adjacent
to the questionnaire to be predicted, without leveraging information from more distant times. This
limitation stems from the challenging nature of the data, which contains a large number of missing
values. Among the models tested, the one demonstrating the best performance and subsequently used
for submission was based on Random Forest, preceded by a feature selection step to reduce the number
of sensor features.
80
60
Count
40
20
0
0 50 100 150 200 250 300
Days
(a) Task 1
700
600
500
Count
400
300
200
100
0
0 50 100 150 200
Days
(b) Task 2
Figure 4: Here’s the histogram showing the difference in days between consecutive questionnaires for task
1 and task 2. As you can see, for task 2, the questionnaires are filled out at more irregular intervals and with
greater frequency compared to task 1
The performance obtained shows significant variability depending on the questionnaire number;
The Mean Absolute Error (MAE) calculated on the test set is 0.23 and 0.31 respectively for Task 1 and
Task 2, while the Root Mean Squared Error (RMSE) is 0.52 and 0.60 respectively. The lower error is
observed in the first task, which could be attributed to the fact that the questionnaire compilation
by clinical staff tends to be more reliable and objective compared to the subjective opinion from the
patient. These seemingly promising results are unfortunately attributed to the ALSFRS-R questionnaires
mostly remaining constant from one visit to another, making it very easy to achieve high prediction
performance.
To address this issue, one potential approach to improve is using data augmentation to increase the
number of questionnaires in the training set. To improve predictions, methods of deep learning could be
tested, leveraging much longer sequences of sensor data (such as recurrent neural networks). However,
Table 1
This table pertains to Task 1. It presents the hyperparameters associated with Random Forest along with the
correlation threshold used for feature selection. This process involved removing sensor features with a correlation
to the outcome lower than the specified threshold.
Q depth max features correlation threshold
Q1 3 sqrt 0.2
Q2 2 sqrt 0.0
Q3 3 log2 0.0
Q4 8 sqrt 025
Q5 7 log2 0.15
Q6 6 sqrt 0.2
Q7 8 log2 0.1
Q8 9 sqrt 0.15
Q9 6 sqrt 0.15
Q10 2 log2 0.0
Q11 2 log2 0.0
Q12 3 log2 0.25
Table 2
This table pertains to Task 2. It presents the hyperparameters associated with Random Forest along with the
correlation threshold used for feature selection. This process involved removing sensor features with a correlation
to the outcome lower than the specified threshold.
Q depth max features correlation threshold
Q1 4 sqrt 0.25
Q2 5 log2 0.0
Q3 3 sqrt 0.0
Q4 4 log2 0.0
Q5 7 sqrt 0.05
Q6 9 log2 0.0
Q7 7 sqrt 0.0
Q8 9 log2 0.25
Q9 8 sqrt 0.25
Q10 8 log2 0.25
Q11 3 sqrt 0.0
Q12 4 sqrt 0.15
these models require significantly more data for training, which represent the biggest obstacle for this
task.
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Q MAE (std) RMSE (std)
Q1 0.155 (0.080) 0.385 (0.0971)
Q2 0.091 (0.019) 0.298 (0.0317)
Q3 0.159 (0.028) 0.397 (0.034)
Q4 0.266 (0.106) 0.508 (0.107)
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Q10 0.250 (0.029) 0.577 (0.049)
Q11 0.105 (0.035) 0.319 (0.051)
Q12 0.059 (0.032) 0.237 (0.063)
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