Tools / Methods

See all our scientific publications focusing on tools and methods.


 

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DYNAMITE: Integrating Archetypal Analysis and Process Mining for Interpretable Disease Progression Modelling

#Open access #Journal

Isotta Trescato, Erica Tavazzi, Martina Vettoretti, Roberto Gatta, Rosario Vasta, Adriano Chio, Barbara Di Camillo

DYNAMITE, an acronym for DYNamic Archetypal analysis for MIning disease TrajEctories, is a new methodology developed specifically to model disease progression by exploiting information available in longitudinal clinical datasets. First, archetypal analysis is applied to data organised in matrix form, with the aim of finding extreme and representative disease states (archetypes) linked to the original data through convex coefficients. Then, each original observation is associated with a single archetype based on their similarity; finally, an event log is created encoding the progression of disease states for each patient in terms of archetype states. In the last stage of the procedure, archetypal analysis is coupled with process mining, which allows the event log archetypes to be visualised graphically as sequences of disease states, allowing the clinical trajectories of patients to be extracted and examined.

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iDPP@CLEF 2024 - Participants' repositories for the Intelligent Disease Prediction Progression Challenge

#Workshop

Birolo, Giovanni, Bosoni, Pietro, Faggioli, Guglielmo, Aidos, Helena, Bergamaschi, Roberto, Cavalla, Paola, Chiò, Adriano, Dagliati, arianna, de Carvalho, Mamede, Di Nunzio, Giorgio Maria, Fariselli, Pietro, Garcìa Dominguez, Jose Manuel, Gromicho, Marta, Guazzo, Alessandro, Longato, Enrico, Madeira, Sara C., Manera, Umberto, Marchesin, Stefano, Menotti, Laura, Silvello, Gianmaria, Tavazzi, Eleonora, Tavazzi, Erica, Trescato, Isotta, Vettoretti, Martina, Di Camillo, Barbara, Ferro, Nicola

iDPP@CLEF 2024 (Intelligent Disease Progression Prediction at CLEF) is a challenge organized by the BRAINTEASER Horizon 2020 project and co-located with CLEF 2024 (Conference and Labs of the Evaluation Forum).

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Deep Temporal Consensus Clustering for Patient Stratification in Amyotrophic Lateral Sclerosis

#Workshop

Miguel Pego Roque, Andreia S Martins, Marta Gromicho, Mamede de Carvalho, Sara C Madeira, Pedro Tomás, Helena Aidos

Amyotrophic Lateral Sclerosis (ALS) is a fast-acting neurodegenerative disease, characterized by loss of muscle movement and heterogeneity in disease evolution. This poses a challenge in predicting the best time for therapy administration. Here, we propose Deep Temporal Consensus Clustering (DTCC), a stratification method to uncover patient groups with similar disease progression. Using only the initial 6-month follow-up period, DTCC uncovered five clusters that were evaluated in terms of disease evolution and time-to-event. For three critical events (non-invasive ventilation, gastrostomy and death) the attained groups show distinct 10- year progressions, validating the approach.

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Parallel intersection counting on shared-memory multiprocessors and GPUs

#Open access #Journal

Moreno Marzolla, Giovanni Birolo, Gabriele D’Angelo, Piero Fariselli

Computing intersections among sets of one-dimensional intervals is an ubiquitous problem in computational geometry with important applications in bioinformatics, where the size of typical inputs is large and it is therefore important to use efficient algorithms. In this paper we propose a parallel algorithm for the 1D intersection-counting problem, that is, the problem of counting the number of intersections between each interval in a given set A and every interval in a set B. Our algorithm is suitable for shared-memory architectures (e.g., multicore CPUs) and GPUs. The algorithm is work-efficient because it performs the same amount of work as the best serial algorithm for this kind of problem. Our algorithm has been implemented in C++ using the Thrust parallel algorithms library, enabling the generation of optimized programs for multicore CPUs and GPUs from the same source code. The performance of our algorithm is evaluated on synthetic and real datasets, showing good scalability on different generations of hardware.

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Temporal stratification of amyotrophic lateral sclerosis patients using disease progression patterns

#Open access # Peer-reviewed #Journal

Daniela M. Amaral, Diogo F Soares, Marta Gromicho, Mamede de Carvalho, Sara C Madeira, Pedro Tomás, Helena Aidos

Understanding how different groups of patients experience disease progression is essential for improving care and guiding treatment decisions. This study introduces a new data-driven approach, ClusTric, which uses advanced clustering methods to uncover complex patterns in how diseases like amyotrophic lateral sclerosis (ALS) progress over time.

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BRAINTEASER ALS and MS Datasets

#Workshop

Faggioli, Guglielmo, Marchesin, Stefano, Menotti, Laura, Aidos, Helena, Bergamaschi, Roberto, Birolo, Giovanni, Bosoni, Pietro, Cavalla, Paola, Chiò, Adriano, Dagliati, Arianna, de Carvalho, Mamede, Di Nunzio, Giorgio Maria, Fariselli, Piero, García Dominguez, Jose Manuel, Gromicho, Marta, Guazzo, Alessandro, Longato, Enrico, Madeira, Sara C., Manera, Umberto, Silvello, Gianmaria, Tavazzi, Eleonora, Tavazzi, Erica, Trescato, Isotta, Vettoretti, Martina, Di Camillo, Barbara, Ferro, Nicola

BRAINTEASER (Bringing Artificial Intelligence home for a better care of amyotrophic lateral sclerosis and multiple sclerosis) is a data science project that seeks to exploit the value of big data, including those related to health, lifestyle habits, and environment, to support patients with Amyotrophic Lateral Sclerosis (ALS) and Multiple Sclerosis (MS) and their clinicians. Taking advantage of cost-efficient sensors and apps, BRAINTEASER will integrate large, clinical datasets that host both patient-generated and environmental data.

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The BrainTeaser Ontology for ALS and MS Clinical Data

#Workshop

Guglielmo Faggioli, Stefano Marchesin, Laura Menotti, Giorgio Maria Di Nunzio, Gianmaria Silvello, Nicola Ferro

This webpage describes the design and development of the BrainTeaser Ontology (BTO) whose purpose is to jointly model both Amyotrophic Lateral Sclerosis (ALS) and Multiple Sclerosis (MS) Clinical Data.

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iDPP@CLEF 2024: The Intelligent Disease Progression Prediction Challenge

#Workshop

Aidos, H., Bergamaschi, S., Cavalla, P., Chiò, A., Dagliati, A., Di Camillo, B., de Carvalho, M., Ferro, N., Fariselli, P., García Dominguez, J. M., Madeira, S. C., and Tavazzi, E. (2024).

iDPP@CLEF 2024 focused on prospective patient data for ALS collected via a dedicated app developed by the BRAINTEASER project and sensor data in the context of clinical trials in Turin, Pavia, Lisbon, and Madrid. For MS, iDPP@CLEF 2024 will rely on retrospective patient data complemented with environmental and pollution data from clinical institutions in Pavia and Turin.

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Intelligent Disease Progression Prediction: Overview of iDPP@CLEF 2024

#Workshop

Birolo, G., Bosoni, P., Faggioli, G., Aidos, H., Bergamaschi, R., Cavalla, P., Chiò, A., Dagliati, A., de Carvalho, M., Di Nunzio, G. M., Fariselli, P., Garcia Dominguez, J. M., Gromicho, M., Guazzo, A., Longato, E., Madeira, S., Manera, U., Marchesin, S., Menotti, L., Silvello, G., Tavazzi, E., Tavazzi, E., Trescato, I., Vettoretti, M., Di Camillo, B., and Ferro, N. (2024).

In this edition, we extended the MS dataset of iDPP@CLEF 2023 with environmental data. Furthermore, we introduced two new ALS tasks, focused on predicting the progression of the disease using data obtained from wearable devices, making it the first iDPP edition that uses prospective data collected directly from the patients involved in the BRAINTEASER project.

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Overview of iDPP@CLEF 2024: The Intelligent Disease Progression Prediction Challenge

#Workshop

Birolo, G., Bosoni, P., Faggioli, G., Aidos, H., Bergamaschi, R., Cavalla, P., Chiò, A., Dagliati, A., de Carvalho, M., Di Nunzio, G. M., Fariselli, P., Garcia Dominguez, J. M., Gromicho, M., Guazzo, A., Longato, E., Madeira, S., Manera, U., Marchesin, S., Menotti, L., Silvello, G., Tavazzi, E., Tavazzi, E., Trescato, I., Vettoretti, M., Di Camillo, B., and Ferro, N. (2024).

iDPP@CLEF 2024 continues the work of the previous editions, iDPP@CLEF 2022 and 2023. The 2022 edition focused on predicting ALS progression and utilizing explainable AI. The 2023 edition expanded on this by including environmental data and introduced a new task for predicting MS progression. This edition extends the MS dataset with environmental data and introduces two new ALS tasks aimed at predicting disease progression using data from wearable devices. This marks the first iDPP edition to utilize prospective data directly collected from patients involved in the BRAINTEASER project.

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Predicting the functional rating scale and self-assessment status of ALS patients with sensor data

#Workshop

Andreia S Martins, Daniela M Amaral, Eduardo N Castanho, Diogo F Soares, Ruben Branco, Sara C Madeira, Helena Aidos

iDPP @ CLEF 2024 aimed to develop novel methodologies for predicting ALS disease progression, enabling the community to combine efforts and improve current prognostic methods. This report discusses evaluation of the impact of sensor data on improving the prediction of ALSFRS-R scores.

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Using Wearable and Environmental Data to Improve the Prediction of Amyotrophic Lateral Sclerosis and Multiple Sclerosis Progression: an Explorative Study

#Workshop

Elena Marinello, Alessandro Guazzo, Enrico Longato, Erica Tavazzi, Isotta Trescato, Martina Vettoretti, and Barbara Di Camillo.

Amyotrophic Lateral Sclerosis (ALS) and Multiple Sclerosis (MS) are chronic diseases with a severe impact on patients’ lives. Both diseases create significant psychological and economic burdens due to alternating acute phases requiring hospital and home care. One possible solution could be the employment of sensor data to develop predictive models that can assist clinicians in making treatment and therapeutic decisions. In the context of the iDPP@CLEF 2024 challenge, this work aimed to develop and compare different machine-learning approaches for predicting the Amyotrophic Lateral Sclerosis Functional Rating Scale-Revised (ALSFRS-R) scores in ALS patients, and relapses in MS patients, using wearable and environmental data, respectively.

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Assessing disease progression in ALS: prognostic subgroups and outliers

#Open access #Peer-reviewed #Journal

Inês Alves, Marta Gromicho, Miguel Oliveira Santos, Susana Pinto, Mamede de Carvalho

The rate of disease progression, measured by the decline of ALS Functional Rating Scale-Revised (ALSFRS-R) from symptom onset to diagnosis (ΔFS) is a well-established prognostic biomarker for predicting survival. Objectives: This study aims to categorize a large patient cohort based on the initial ΔFS and subsequently investigate survival deviations from the expected prognosis defined by ΔFS. Our study reaffirms ΔFS as a prognostic biomarker for ALS. We disclosed outliers defying anticipated patterns. The observed shift in progression categories underscores the non-linear nature of disease progression. Genetic and unknown biological reasons may explain these deviations. Further research is needed to fully understand modulation of ALS survival.

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Automated Pipeline for Denoising, Missing Data Processing, and Feature Extraction for Signals acquired via Wearable Devices in Multiple Sclerosis and Amyotrophic Lateral Sclerosis Applications

#Open access #Peer-reviewed #Journal

Cossu L, Cappon G, Facchinetti A

The incorporation of health-related sensors in wearable devices has increased their use as essential monitoring tools for a wide range of clinical applications. However, the signals obtained from these devices often present challenges such as artifacts, spikes, high-frequency noise, and data gaps, which impede their direct exploitation. Additionally, clinically relevant features are not always readily available. This problem is particularly critical within the H2020 BRAINTEASER project, funded by the European Community, which aims at developing models for the progression of Multiple Sclerosis (MS) and Amyotrophic Lateral Sclerosis (ALS) using data from wearable devices. The performance and effectiveness of the proposed automated pipeline have been evaluated through pivotal beta testing sessions, which demonstrated the ability of the pipeline to improve the data quality and extract features from the data. Further clinical validation of the extracted features will be performed in the upcoming steps of the BRAINTEASER project.

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Adaptive and self-learning Bayesian filtering algorithm to statistically characterize and improve signal-to-noise ratio of heart-rate data in wearable devices

#Open access #Peer-reviewed #Journal

Cossu L, Cappon G, Facchinetti A

The use of wearable sensors to monitor vital signs is increasingly important in assessing individual health. However, their accuracy often falls short of that of dedicated medical devices, limiting their usefulness in a clinical setting. This study introduces a new Bayesian filtering (BF) algorithm that is designed to learn the statistical characteristics of signal and noise, allowing for optimal smoothing. The results show that BF accurately captures SNR variability, reducing the root mean square error from 2.84 bpm to 1.21 bpm and the mean absolute relative error from 3.46% to 1.36%. These findings highlight the potential of BF as a preprocessing tool to enhance signal quality from wearable sensors, particularly in HR data, thereby expanding their applications in clinical and research settings.

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An extensible and unifying approach to retrospective clinical data modeling: the BrainTeaser Ontology

#Open access #Peer-reviewed #Journal

Faggioli, G., Menotti, L., Marchesin, S., Chiò, A., Dagliati, A., de Carvalho, M., Gromicho, M., Manera, U., Tavazzi, E., Di Nunzio, G. M., Silvello, G., and Ferro, N.

Automatic disease progression prediction models require large amounts of training data, which are seldom available, especially when it comes to rare diseases. A possible solution is to integrate data from different medical centres. Nevertheless, various centres often follow diverse data collection procedures and assign different semantics to collected data. Ontologies, used as schemas for interoperable knowledge bases, represent a state-of-the-art solution to homologate the semantics and foster data integration from various sources. This work presents the BrainTeaser Ontology (BTO), an ontology that models the clinical data associated with two brain-related rare diseases (ALS and MS) in a comprehensive and modular manner.

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SYNDSURV: A simple framework for survival analysis with data distributed across multiple institutions

#Open access #Peer-reviewed #Journal

Cesare Rollo, Corrado Pancotti, Giovanni Birolo, Ivan Rossi, Tiziana Sanavia, Piero Fariselli

Data sharing among different institutions represents one of the major challenges in developing distributed machine learning approaches. Federated learning is a possible solution, but requires fast communications and flawless security. Here, we propose SYNDSURV (SYNthetic Distributed SURVival), an alternative approach that simplifies the current state-of-the-art paradigm by allowing different centres to generate local simulated instances from real data and then gather them into a centralised hub, where an Artificial Intelligence (AI) model can learn in a standard way. The main advantage of this procedure is that it is model-agnostic, therefore prediction models can be directly applied in distributed applications without requiring particular adaptations as the current federated approaches do.

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Predicting Clinical Outcomes of amyotrophic lateral sclerosis Progression using Logistic Regression and Deep-Learning Multilayer Perceptron Approaches

#Conference

Guazzo A, Atzeni M, Idi E, Trescato I, Tavazzi E, Longato E, Manera U, Chiò A, Gromicho M, Alves I, de Carvalho M, Vettoretti M, Di Camillo B

Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease that results in death within a short time span (3-5 years). One of the major challenges in treating ALS is its highly heterogeneous disease progression and the lack of effective prognostic tools to forecast it. The main aim of this study was, then, to test the feasibility of predicting relevant clinical outcomes that characterize the progression of ALS with a two-year prediction horizon via artificial intelligence techniques using routine visits data.

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Predicting Clinical Outcomes of amyotrophic lateral sclerosis Progression using Logistic Regression and Deep-Learning Multilayer Perceptron Approaches

#Workshop

Mahin Vazifehdan, Pietro Bosoni, Daniele Pala, Eleonora Tavazzi, Roberto Bergamaschi, Riccardo Bellazzi, Arianna Dagliati

Multiple Sclerosis (MS) is a chronic disease characterized by progressive or alternate impairment of neurological functions (motor, sensory, visual, and cognitive). Predicting disease progression with a probabilistic and time-dependent approach might help in suggesting interventions that can delay the progression of the disease. This study aimed at i) exploring different methodologies for imputing missing FS sub-scores, and ii) predicting the EDSS score using complete clinical data. Results show that Exponential Weighted Moving Average achieved the lowest error rate in the missing data imputation task; furthermore, the combination of Classification and Regression Trees for the imputation and SVM for the prediction task obtained the best accuracy.

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Baseline Machine Learning Approaches To Predict Multiple Sclerosis Disease Progression

#Workshop

Guazzo A, Trescato I, Longato E, Tavazzi E, Vettoretti M, Di Camillo B

Developed in the context of the iDPP@CLEF 2023 challenge, this work aims at developing different machine-learning approaches to predict a worsening in patient disability caused by MS using a shared dataset provided by the challenge organisers.

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Intelligent Disease Progression Prediction: Overview of iDPP@CLEF 2023

#Open access #Peer-reviewed #Journal

Erica Tavazzi, Enrico Longato, Martina Vettoretti, Helena Aidos, Isotta Trescato, Chiara Roversi, Andreia S Martins, Eduardo N Castanho, Ruben Branco, Diogo F Soares, Alessandro Guazzo, Giovanni Birolo, Daniele Pala, Pietro Bosoni, Adriano Chiò, Umberto Manera, Mamede de Carvalho, Bruno Miranda, Marta Gromicho, Inês Alves, Riccardo Bellazzi, Arianna Dagliati, Piero Fariselli, Sara C Madeira, Barbara Di Camillo

iDPP@CLEF aims at developing an evaluation infrastructure for AI algorithms to describe ALS and MS mechanisms, stratify patients based on their phenotype, and predict disease progression in a probabilistic, time-dependent manner.

iDPP@CLEF 2022 ran as a pilot lab in CLEF 2022, with tasks related to predicting ALS progression and explainable AI algorithms for prediction. iDPP@CLEF 2023 continued in CLEF 2023, with a focus on predicting MS progression and exploring whether pollution and environmental data can improve the prediction of ALS progression.

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Artificial intelligence and statistical methods for stratification and prediction of progression in amyotrophic lateral sclerosis: A systematic review

#Open access #Peer-reviewed #Journal

Erica Tavazzi, Enrico Longato, Martina Vettoretti, Helena Aidos, Isotta Trescato, Chiara Roversi, Andreia S Martins, Eduardo N Castanho, Ruben Branco, Diogo F Soares, Alessandro Guazzo, Giovanni Birolo, Daniele Pala, Pietro Bosoni, Adriano Chiò, Umberto Manera, Mamede de Carvalho, Bruno Miranda, Marta Gromicho, Inês Alves, Riccardo Bellazzi, Arianna Dagliati, Piero Fariselli, Sara C Madeira, Barbara Di Camillo

This systematic review examines artificial intelligence’s methodological landscape in ALS, focusing on patient stratification and disease progression prediction.
Out of 1604 reports, we identified 15 studies on patient stratification, 28 on ALS progression prediction, and 6 on both. We highlighted a general agreement in terms of input variable selection for both stratification and prediction of ALS progression, and in terms of prediction targets. A striking lack of validated models emerged, as well as a general difficulty in reproducing many published studies, mainly due to the absence of the corresponding parameter lists. While deep learning seems promising for prediction applications, its superiority with respect to traditional methods has not been established; there is, instead, ample room for its application in the subfield of patient stratification. Finally, an open question remains on the role of new environmental and behavioural variables collected via novel, real-time sensors.

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BRAINTEASER Architecture for Integration of AI Models and Interactive Tools for Amyotrophic Lateral Sclerosis (ALS) and Multiple Sclerosis (MS) Progression Prediction and Management

#Conference

Vladimir Urošević, Nikola Vojičić, Aleksandar Jovanović, Borko Kostić, Sergio Gonzalez-Martinez, María Fernanda Cabrera-Umpiérrez, Manuel Ottaviano, Luca Cossu, Andrea Facchinetti & Giacomo Cappon

The presented platform architecture and deployed implementation in real-life clinical and home care settings on four Amyotrophic Lateral Sclerosis (ALS) and Multiple Sclerosis (MS) study sites, integrates the novel working tools for improved disease management with the initial releases of the AI models for disease monitoring. The described robust industry-standard scalable platform is to be a referent example of the integration approach based on loose coupling APIs and industry open standard human-readable and language-independent interface specifications, and its successful baseline implementation for further upcoming releases of additional and more advanced AI models and supporting pipelines (such as for ALS and MS progression prediction, patient stratification, and ambiental exposure modelling) in the following development.

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Triclustering-based classification of longitudinal data for prognostic prediction: targeting relevant clinical endpoints in amyotrophic lateral sclerosis

#Open access #Peer-reviewed #Journal

Diogo F. Soares, Rui Henriques, Marta Gromicho, Mamede de Carvalho, Sara C. Madeira

This research introduces a new type of understandable models for predicting how diseases progress over time, focusing on a specific group of patients with certain characteristics. We developed a new method called TCtriCluster, which looks for meaningful patterns in data collected over time from these patients, helping us understand the progression of the disease better. By using these patterns, we improved the accuracy of predicting important events in ALS, such as when patients might need breathing assistance or other types of support. This method performed better than existing ones, with high accuracy in predicting when certain interventions might be needed. This approach was tested on a large group of ALS patients in Portugal, providing valuable insights for healthcare professionals in managing the disease and its various stages.

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iDPP@CLEF 2023: The Intelligent Disease Progression Prediction Challenge

#Workshop

Helena Aidos, Roberto Bergamaschi, Paola Cavalla, Adriano Chiò, Arianna Dagliati, Barbara Di Camillo, Mamede Alves de Carvalho, Nicola Ferro, Piero Fariselli, Jose Manuel García Dominguez, Sara C. Madeira & Eleonora Tavazzi

Amyotrophic Lateral Sclerosis (ALS) and Multiple Sclerosis (MS) are chronic diseases characterized by progressive or alternate impairment of neurological functions (motor, sensory, visual, cognitive). The goal of iDPP@CLEF Open Evaluation Challenge is to design and develop an evaluation infrastructure for AI algorithms able to: better describe disease mechanisms; stratify patients according to their phenotype assessed all over the disease evolution; predict disease progression in a probabilistic, time dependent fashion. iDPP@CLEF will continue in CLEF 2023, focusing on the prediction of MS progression and exploring whether pollution and environmental data can improve the prediction of ALS progression.

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Learning prognostic models using a mixture of biclustering and triclustering: Predicting the need for non-invasive ventilation in Amyotrophic Lateral Sclerosis

#Open access #Peer-reviewed #Journal

Diogo F. Soares, Rui Henriques, Marta Gromicho, Mamede de Carvalho, Sara C. Madeira

This paper wants to improve the best features in methods used for predicting outcomes and deciding when patients with Amyotrophic Lateral Sclerosis (ALS) need non-invasive breathing assistance. It achieves this by studying clear patterns of how the disease progresses. The discoveries made can also be useful for understanding other aspects of ALS and similar diseases.

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Baseline Machine Learning Approaches To Predict Amyotrophic Lateral Sclerosis Disease Progression

#Workshop

Trescato I., Guazzo A., Longato E., Hazizaj E., Roversi C., Tavazzi E., Vettoretti M., Di Camillo B.

The main goal of this study was to compare different methods for predicting the occurrence and timing of specific events in ALS, including the need for non-invasive ventilation, percutaneous endoscopic gastrostomy, and death. The study took place during the CLEF Challenge 2022, and the organizers provided two versions of datasets: the first comprised information collected only at the first visit after ALS diagnosis, while the second included all information collected during a 6-month follow-up starting from the first visit. Notably, regardless of the outcome predicted, the models including dynamic features improved model performance, highlighting the significance of the first 6 months of data for accurate predictions in fast-progressing diseases like ALS.

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Deep learning methods to predict amyotrophic lateral sclerosis disease progression

#Open access #Journal

Corrado Pancotti, Giovanni Birolo, Cesare Rollo, Tiziana Sanavia, Barbara Di Camillo, Umberto Manera, Adriano Chiò & Piero Fariselli

Amyotrophic lateral sclerosis (ALS) is a complex disease that weakens muscles by attacking nerve cells. Because ALS patients face shortened lifespans, understanding the disease’s progression quickly is crucial for improving treatments. Scientists are turning to computer models to forecast how ALS develops over time.
One major data source aiding this research is the PRO-ACT repository. In 2015, a competition challenged developers to create programs predicting ALS progression using this data.
Our study dives into this challenge, applying advanced Deep Learning techniques.
Our findings suggest that Deep Learning could offer a fresh approach to forecasting ALS progression, offering hope for more effective treatment strategies.

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