Semi-Supervised Multimodal Relevance Vector Regression Improves Cognitive Performance Estimation from Imaging and Biological Biomarkers

Accurate estimation of cognitive scores for patients can help track the progress of neurological diseases. In this paper, we present a novel semi-supervised multimodal relevance vector regression (SM-RVR) method for predicting clinical scores of neurological diseases from multimodal imaging and biological biomarker, to help evaluate pathological stage and predict progression of diseases, e.g., Alzheimer’s diseases (AD). Unlike most existing methods, we predict clinical scores from multimodal (imaging and biological) biomarkers, including MRI, FDG-PET, and CSF. Considering that the clinical scores of mild cognitive impairment (MCI) subjects are often less stable compared to those of AD and normal control (NC) subjects due to the heterogeneity of MCI, we use only the multimodal data of MCI subjects, but no corresponding clinical scores, to train a semi-supervised model for enhancing the estimation of clinical scores for AD and NC subjects. We also develop a new strategy for selecting the most informative MCI subjects. We evaluate the performance of our approach on 202 subjects with all three modalities of data (MRI, FDG-PET and CSF) from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. The experimental results show that our SM-RVR method achieves a root-mean-square error (RMSE) of 1.91 and a correlation coefficient (CORR) of 0.80 for estimating the MMSE scores, and also a RMSE of 4.45 and a CORR of 0.78 for estimating the ADAS-Cog scores, demonstrating very promising performances in AD studies

Machine learning for modeling the progression of Alzheimer disease dementia using clinical data: A systematic literature review

OBJECTIVE: Alzheimer disease (AD) is the most common cause of dementia, a syndrome characterized by cognitive impairment severe enough to interfere with activities of daily life. We aimed to conduct a systematic literature review (SLR) of studies that applied machine learning (ML) methods to clinical data derived from electronic health records in order to model risk for progression of AD dementia. MATERIALS AND METHODS: We searched for articles published between January 1, 2010, and May 31, 2020, in PubMed, Scopus, ScienceDirect, IEEE Explore Digital Library, Association for Computing Machinery Digital Library, and arXiv. We used predefined criteria to select relevant articles and summarized them according to key components of ML analysis such as data characteristics, computational algorithms, and research focus. RESULTS: There has been a considerable rise over the past 5 years in the number of research papers using ML-based analysis for AD dementia modeling. We reviewed 64 relevant articles in our SLR. The results suggest that majority of existing research has focused on predicting progression of AD dementia using publicly available datasets containing both neuroimaging and clinical data (neurobehavioral status exam scores, patient demographics, neuroimaging data, and laboratory test values). DISCUSSION: Identifying individuals at risk for progression of AD dementia could potentially help to personalize disease management to plan future care. Clinical data consisting of both structured data tables and clinical notes can be effectively used in ML-based approaches to model risk for AD dementia progression. Data sharing and reproducibility of results can enhance the impact, adaptation, and generalizability of this research

Clinical microbiology with multi-view deep probabilistic models

Clinical microbiology is one of the critical topics of this century. Identification and discrimination of microorganisms is considered a global public health threat by the main international health organisations, such as World Health Organisation (WHO) or the European Centre for Disease Prevention and Control (ECDC). Rapid spread, high morbidity and mortality, as well as the economic burden associated with their treatment and control are the main causes of their impact. Discrimination of microorganisms is crucial for clinical applications, for instance, Clostridium difficile (C. diff ) increases the mortality and morbidity of healthcare-related infections. Furthermore, in the past two decades, other bacteria, including Klebsiella pneumoniae (K. pneumonia), have demonstrated a significant propensity to acquire antibiotic resistance mechanisms. Consequently, the use of an ineffective antibiotic may result in mortality. Machine Learning (ML) has the potential to be applied in the clinical microbiology field to automatise current methodologies and provide more efficient guided personalised treatments. However, microbiological data are challenging to exploit owing to the presence of a heterogeneous mix of data types, such as real-valued high-dimensional data, categorical indicators, multilabel epidemiological data, binary targets, or even time-series data representations. This problem, which in the field of ML is known as multi-view or multi-modal representation learning, has been studied in other application fields such as mental health monitoring or haematology. Multi-view learning combines different modalities or views representing the same data to extract richer insights and improve understanding. Each modality or view corresponds to a distinct encoding mechanism for the data, and this dissertation specifically addresses the issue of heterogeneity across multiple views. In the probabilistic ML field, the exploitation of multi-view learning is also known as Bayesian Factor Analysis (FA). Current solutions face limitations when handling high-dimensional data and non-linear associations. Recent research proposes deep probabilistic methods to learn hierarchical representations of the data, which can capture intricate non-linear relationships between features. However, some Deep Learning (DL) techniques rely on complicated representations, which can hinder the interpretation of the outcomes. In addition, some inference methods used in DL approaches can be computationally burdensome, which can hinder their practical application in real-world situations. Therefore, there is a demand for more interpretable, explainable, and computationally efficient techniques for highdimensional data. By combining multiple views representing the same information, such as genomic, proteomic, and epidemiologic data, multi-modal representation learning could provide a better understanding of the microbial world. Hence, in this dissertation, the development of two deep probabilistic models, that can handle current limitations in state-of-the-art of clinical microbiology, are proposed. Moreover, both models are also tested in two real scenarios regarding antibiotic resistance prediction in K. pneumoniae and automatic ribotyping of C. diff in collaboration with the Instituto de Investigación Sanitaria Gregorio Marañón (IISGM) and the Instituto Ramón y Cajal de Investigación Sanitaria (IRyCIS). The first presented algorithm is the Kernelised Sparse Semi-supervised Heterogeneous Interbattery Bayesian Analysis (SSHIBA). This algorithm uses a kernelised formulation to handle non-linear data relationships while providing compact representations through the automatic selection of relevant vectors. Additionally, it uses an Automatic Relevance Determination (ARD) over the kernel to determine the input feature relevance functionality. Then, it is tailored and applied to the microbiological laboratories of the IISGM and IRyCIS to predict antibiotic resistance in K. pneumoniae. To do so, specific kernels that handle Matrix-Assisted Laser Desorption Ionization (MALDI)-Time-Of-Flight (TOF) mass spectrometry of bacteria are used. Moreover, by exploiting the multi-modal learning between the spectra and epidemiological information, it outperforms other state-of-the-art algorithms. Presented results demonstrate the importance of heterogeneous models that can analyse epidemiological information and can automatically be adjusted for different data distributions. The implementation of this method in microbiological laboratories could significantly reduce the time required to obtain resistance results in 24-72 hours and, moreover, improve patient outcomes. The second algorithm is a hierarchical Variational AutoEncoder (VAE) for heterogeneous data using an explainable FA latent space, called FA-VAE. The FA-VAE model is built on the foundation of the successful KSSHIBA approach for dealing with semi-supervised heterogeneous multi-view problems. This approach further expands the range of data domains it can handle. With the ability to work with a wide range of data types, including multilabel, continuous, binary, categorical, and even image data, the FA-VAE model offers a versatile and powerful solution for real-world data sets, depending on the VAE architecture. Additionally, this model is adapted and used in the microbiological laboratory of IISGM, resulting in an innovative technique for automatic ribotyping of C. diff, using MALDI-TOF data. To the best of our knowledge, this is the first demonstration of using any kind of ML for C. diff ribotyping. Experiments have been conducted on strains of Hospital General Universitario Gregorio Marañón (HGUGM) to evaluate the viability of the proposed approach. The results have demonstrated high accuracy rates where KSSHIBA even achieved perfect accuracy in the first data collection. These models have also been tested in a real-life outbreak scenario at the HGUGM, where successful classification of all outbreak samples has been achieved by FAVAE. The presented results have not only shown high accuracy in predicting each strain’s ribotype but also revealed an explainable latent space. Furthermore, traditional ribotyping methods, which rely on PCR, required 7 days while FA-VAE has predicted equal results on the same day. This improvement has significantly reduced the time response by helping in the decision-making of isolating patients with hyper-virulent ribotypes of C. diff on the same day of infection. The promising results, obtained in a real outbreak, have provided a solid foundation for further advancements in the field. This study has been a crucial stepping stone towards realising the full potential of MALDI-TOF for bacterial ribotyping and advancing our ability to tackle bacterial outbreaks. In conclusion, this doctoral thesis has significantly contributed to the field of Bayesian FA by addressing its drawbacks in handling various data types through the creation of novel models, namely KSSHIBA and FA-VAE. Additionally, a comprehensive analysis of the limitations of automating laboratory procedures in the microbiology field has been carried out. The shown effectiveness of the newly developed models has been demonstrated through their successful implementation in critical problems, such as predicting antibiotic resistance and automating ribotyping. As a result, KSSHIBA and FA-VAE, both in terms of their technical and practical contributions, signify noteworthy progress both in the clinical and the Bayesian statistics fields. This dissertation opens up possibilities for future advancements in automating microbiological laboratories.La microbiología clínica es uno de los temas críticos de este siglo. La identificación y discriminación de microorganismos se considera una amenaza mundial para la salud pública por parte de las principales organizaciones internacionales de salud, como la Organización Mundial de la Salud (OMS) o el Centro Europeo para la Prevención y Control de Enfermedades (ECDC). La rápida propagación, alta morbilidad y mortalidad, así como la carga económica asociada con su tratamiento y control, son las principales causas de su impacto. La discriminación de microorganismos es crucial para aplicaciones clínicas, como el caso de Clostridium difficile (C. diff ), el cual aumenta la mortalidad y morbilidad de las infecciones relacionadas con la atención médica. Además, en las últimas dos décadas, otros tipos de bacterias, incluyendo Klebsiella pneumoniae (K. pneumonia), han demostrado una propensión significativa a adquirir mecanismos de resistencia a los antibióticos. En consecuencia, el uso de un antibiótico ineficaz puede resultar en un aumento de la mortalidad. El aprendizaje automático (ML) tiene el potencial de ser aplicado en el campo de la microbiología clínica para automatizar las metodologías actuales y proporcionar tratamientos personalizados más eficientes y guiados. Sin embargo, los datos microbiológicos son difíciles de explotar debido a la presencia de una mezcla heterogénea de tipos de datos, tales como datos reales de alta dimensionalidad, indicadores categóricos, datos epidemiológicos multietiqueta, objetivos binarios o incluso series temporales. Este problema, conocido en el campo del aprendizaje automático (ML) como aprendizaje multimodal o multivista, ha sido estudiado en otras áreas de aplicación, como en el monitoreo de la salud mental o la hematología. El aprendizaje multivista combina diferentes modalidades o vistas que representan los mismos datos para extraer conocimientos más ricos y mejorar la comprensión. Cada vista corresponde a un mecanismo de codificación distinto para los datos, y esta tesis aborda particularmente el problema de la heterogeneidad multivista. En el campo del aprendizaje automático probabilístico, la explotación del aprendizaje multivista también se conoce como Análisis de Factores (FA) Bayesianos. Las soluciones actuales enfrentan limitaciones al manejar datos de alta dimensionalidad y correlaciones no lineales. Investigaciones recientes proponen métodos probabilísticos profundos para aprender representaciones jerárquicas de los datos, que pueden capturar relaciones no lineales intrincadas entre características. Sin embargo, algunas técnicas de aprendizaje profundo (DL) se basan en representaciones complejas, dificultando así la interpretación de los resultados. Además, algunos métodos de inferencia utilizados en DL pueden ser computacionalmente costosos, obstaculizando su aplicación práctica. Por lo tanto, existe una demanda de técnicas más interpretables, explicables y computacionalmente eficientes para datos de alta dimensionalidad. Al combinar múltiples vistas que representan la misma información, como datos genómicos, proteómicos y epidemiológicos, el aprendizaje multimodal podría proporcionar una mejor comprensión del mundo microbiano. Dicho lo cual, en esta tesis se proponen el desarrollo de dos modelos probabilísticos profundos que pueden manejar las limitaciones actuales en el estado del arte de la microbiología clínica. Además, ambos modelos también se someten a prueba en dos escenarios reales relacionados con la predicción de resistencia a los antibióticos en K. pneumoniae y el ribotipado automático de C. diff en colaboración con el IISGM y el IRyCIS. El primer algoritmo presentado es Kernelised Sparse Semi-supervised Heterogeneous Interbattery Bayesian Analysis (SSHIBA). Este algoritmo utiliza una formulación kernelizada para manejar correlaciones no lineales proporcionando representaciones compactas a través de la selección automática de vectores relevantes. Además, utiliza un Automatic Relevance Determination (ARD) sobre el kernel para determinar la relevancia de las características de entrada. Luego, se adapta y aplica a los laboratorios microbiológicos del IISGM y IRyCIS para predecir la resistencia a antibióticos en K. pneumoniae. Para ello, se utilizan kernels específicos que manejan la espectrometría de masas Matrix-Assisted Laser Desorption Ionization (MALDI)-Time-Of-Flight (TOF) de bacterias. Además, al aprovechar el aprendizaje multimodal entre los espectros y la información epidemiológica, supera a otros algoritmos de última generación. Los resultados presentados demuestran la importancia de los modelos heterogéneos ya que pueden analizar la información epidemiológica y ajustarse automáticamente para diferentes distribuciones de datos. La implementación de este método en laboratorios microbiológicos podría reducir significativamente el tiempo requerido para obtener resultados de resistencia en 24-72 horas y, además, mejorar los resultados para los pacientes. El segundo algoritmo es un modelo jerárquico de Variational AutoEncoder (VAE) para datos heterogéneos que utiliza un espacio latente con un FA explicativo, llamado FA-VAE. El modelo FA-VAE se construye sobre la base del enfoque de KSSHIBA para tratar problemas semi-supervisados multivista. Esta propuesta amplía aún más el rango de dominios que puede manejar incluyendo multietiqueta, continuos, binarios, categóricos e incluso imágenes. De esta forma, el modelo FA-VAE ofrece una solución versátil y potente para conjuntos de datos realistas, dependiendo de la arquitectura del VAE. Además, este modelo es adaptado y utilizado en el laboratorio microbiológico del IISGM, lo que resulta en una técnica innovadora para el ribotipado automático de C. diff utilizando datos MALDI-TOF. Hasta donde sabemos, esta es la primera demostración del uso de cualquier tipo de ML para el ribotipado de C. diff. Se han realizado experimentos en cepas del Hospital General Universitario Gregorio Marañón (HGUGM) para evaluar la viabilidad de la técnica propuesta. Los resultados han demostrado altas tasas de precisión donde KSSHIBA incluso logró una clasificación perfecta en la primera colección de datos. Estos modelos también se han probado en un brote real en el HGUGM, donde FA-VAE logró clasificar con éxito todas las muestras del mismo. Los resultados presentados no solo han demostrado una alta precisión en la predicción del ribotipo de cada cepa, sino que también han revelado un espacio latente explicativo. Además, los métodos tradicionales de ribotipado, que dependen de PCR, requieren 7 días para obtener resultados mientras que FA-VAE ha predicho resultados correctos el mismo día del brote. Esta mejora ha reducido significativamente el tiempo de respuesta ayudando así en la toma de decisiones para aislar a los pacientes con ribotipos hipervirulentos de C. diff el mismo día de la infección. Los resultados prometedores, obtenidos en un brote real, han sentado las bases para nuevos avances en el campo. Este estudio ha sido un paso crucial hacia el despliegue del pleno potencial de MALDI-TOF para el ribotipado bacteriana avanzado así nuestra capacidad para abordar brotes bacterianos. En conclusión, esta tesis doctoral ha contribuido significativamente al campo del FA Bayesiano al abordar sus limitaciones en el manejo de tipos de datos heterogéneos a través de la creación de modelos noveles, concretamente, KSSHIBA y FA-VAE. Además, se ha llevado a cabo un análisis exhaustivo de las limitaciones de la automatización de procedimientos de laboratorio en el campo de la microbiología. La efectividad de los nuevos modelos, en este campo, se ha demostrado a través de su implementación exitosa en problemas críticos, como la predicción de resistencia a los antibióticos y la automatización del ribotipado. Como resultado, KSSHIBA y FAVAE, tanto en términos de sus contribuciones técnicas como prácticas, representan un progreso notable tanto en los campos clínicos como en la estadística Bayesiana. Esta disertación abre posibilidades para futuros avances en la automatización de laboratorios microbiológicos.Programa de Doctorado en Multimedia y Comunicaciones por la Universidad Carlos III de Madrid y la Universidad Rey Juan CarlosPresidente: Juan José Murillo Fuentes.- Secretario: Jerónimo Arenas García.- Vocal: María de las Mercedes Marín Arriaz

Artificial intelligence for predictive biomarker discovery in immuno-oncology: a systematic review

Background: The widespread use of immune checkpoint inhibitors (ICIs) has revolutionised treatment of multiple cancer types. However, selecting patients who may benefit from ICI remains challenging. Artificial intelligence (AI) approaches allow exploitation of high-dimension oncological data in research and development of precision immuno-oncology. Materials and methods: We conducted a systematic literature review of peer-reviewed original articles studying the ICI efficacy prediction in cancer patients across five data modalities: genomics (including genomics, transcriptomics, and epigenomics), radiomics, digital pathology (pathomics), and real-world and multimodality data. Results: A total of 90 studies were included in this systematic review, with 80% published in 2021-2022. Among them, 37 studies included genomic, 20 radiomic, 8 pathomic, 20 real-world, and 5 multimodal data. Standard machine learning (ML) methods were used in 72% of studies, deep learning (DL) methods in 22%, and both in 6%. The most frequently studied cancer type was non-small-cell lung cancer (36%), followed by melanoma (16%), while 25% included pan-cancer studies. No prospective study design incorporated AI-based methodologies from the outset; rather, all implemented AI as a post hoc analysis. Novel biomarkers for ICI in radiomics and pathomics were identified using AI approaches, and molecular biomarkers have expanded past genomics into transcriptomics and epigenomics. Finally, complex algorithms and new types of AI-based markers, such as meta-biomarkers, are emerging by integrating multimodal/multi-omics data. Conclusion: AI-based methods have expanded the horizon for biomarker discovery, demonstrating the power of integrating multimodal data from existing datasets to discover new meta-biomarkers. While most of the included studies showed promise for AI-based prediction of benefit from immunotherapy, none provided high-level evidence for immediate practice change. A priori planned prospective trial designs are needed to cover all lifecycle steps of these software biomarkers, from development and validation to integration into clinical practice

Personalized Automatic Estimation of Self-reported Pain Intensity from Facial Expressions

Pain is a personal, subjective experience that is commonly evaluated through visual analog scales (VAS). While this is often convenient and useful, automatic pain detection systems can reduce pain score acquisition efforts in large-scale studies by estimating it directly from the participants' facial expressions. In this paper, we propose a novel two-stage learning approach for VAS estimation: first, our algorithm employs Recurrent Neural Networks (RNNs) to automatically estimate Prkachin and Solomon Pain Intensity (PSPI) levels from face images. The estimated scores are then fed into the personalized Hidden Conditional Random Fields (HCRFs), used to estimate the VAS, provided by each person. Personalization of the model is performed using a newly introduced facial expressiveness score, unique for each person. To the best of our knowledge, this is the first approach to automatically estimate VAS from face images. We show the benefits of the proposed personalized over traditional non-personalized approach on a benchmark dataset for pain analysis from face images.Comment: Computer Vision and Pattern Recognition Conference, The 1st International Workshop on Deep Affective Learning and Context Modelin

Multi-Domain Transfer Learning for Early Diagnosis of Alzheimer’s Disease

Recently, transfer learning has been successfully applied in early diagnosis of Alzheimer’s Disease (AD) based on multi-domain data. However, most of existing methods only use data from a single auxiliary domain, and thus cannot utilize the intrinsic useful correlation information from multiple domains. Accordingly, in this paper, we consider the joint learning of tasks in multi-auxiliary domains and the target domain, and propose a novel Multi-Domain Transfer Learning (MDTL) framework for early diagnosis of AD. Specifically, the proposed MDTL framework consists of two key components: 1) a multi-domain transfer feature selection (MDTFS) model that selects the most informative feature subset from multi-domain data, and 2) a multidomain transfer classification (MDTC) model that can identify disease status for early AD detection. We evaluate our method on 807 subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database using baseline magnetic resonance imaging (MRI) data. The experimental results show that the proposed MDTL method can effectively utilize multi-auxiliary domain data for improving the learning performance in the target domain, compared with several state-of-the-art methods