Data harmonization for the analysis of personalized treatment of psychosis with metacognitive training

Personalized medicine is a data-driven approach that aims to adapt patients' diagnostics and therapies to their characteristics and needs. The availability of patients' data is therefore paramount for the personalization of treatments on the basis of predictive models, and even more so in machine learning-based analyses. Data harmonization is an essential part of the process of data curation. This study presents research on data harmonization for the development of a harmonized retrospective database of patients in Metacognitive Training (MCT) treatment for psychotic disorders. This work is part of the European ERAPERMED 2022-292 research project entitled 'Towards a Personalized Medicine Approach to Psychological Treatment of Psychosis' (PERMEPSY), which focuses on the development of a personalized medicine platform for the treatment of psychosis. The study integrates information from 22 studies into a common format to enable a data analytical approach for personalized treatment. The harmonized database comprises information about 698 patients who underwent MCT and includes a wide range of sociodemographic variables and psychological indicators used to assess a patient's mental health state. The characteristics of patients participating in the study are analyzed using descriptive statistics and exploratory data analysis.

Deep learning-based Alzheimer's disease detection: reproducibility and the effect of modeling choices

Introduction

Machine Learning (ML) has emerged as a promising approach in healthcare, outperforming traditional statistical techniques. However, to establish ML as a reliable tool in clinical practice, adherence to best practices in data handling, and modeling design and assessment is crucial. In this work, we summarize and strictly adhere to such practices to ensure reproducible and reliable ML. Specifically, we focus on Alzheimer's Disease (AD) detection, a challenging problem in healthcare. Additionally, we investigate the impact of modeling choices, including different data augmentation techniques and model complexity, on overall performance.

Methods

We utilize Magnetic Resonance Imaging (MRI) data from the ADNI corpus to address a binary classification problem using 3D Convolutional Neural Networks (CNNs). Data processing and modeling are specifically tailored to address data scarcity and minimize computational overhead. Within this framework, we train 15 predictive models, considering three different data augmentation strategies and five distinct 3D CNN architectures with varying convolutional layers counts. The augmentation strategies involve affine transformations, such as zoom, shift, and rotation, applied either concurrently or separately.

Results

The combined effect of data augmentation and model complexity results in up to 10% variation in prediction accuracy. Notably, when affine transformation are applied separately, the model achieves higher accuracy, regardless the chosen architecture. Across all strategies, the model accuracy exhibits a concave behavior as the number of convolutional layers increases, peaking at an intermediate value. The best model reaches excellent performance both on the internal and additional external testing set.

Discussions

Our work underscores the critical importance of adhering to rigorous experimental practices in the field of ML applied to healthcare. The results clearly demonstrate how data augmentation and model depth-often overlooked factors- can dramatically impact final performance if not thoroughly investigated. This highlights both the necessity of exploring neglected modeling aspects and the need to comprehensively report all modeling choices to ensure reproducibility and facilitate meaningful comparisons across studies.

The ethical aspects of exposome research: a systematic review

In recent years, exposome research has been put forward as the next frontier for the study of human health and disease. Exposome research entails the analysis of the totality of environmental exposures and their corresponding biological responses within the human body. Increasingly, this is operationalized by big-data approaches to map the effects of internal as well as external exposures using smart sensors and multiomics technologies. However, the ethical implications of exposome research are still only rarely discussed in the literature. Therefore, we conducted a systematic review of the academic literature regarding both the exposome and underlying research fields and approaches, to map the ethical aspects that are relevant to exposome research. We identify five ethical themes that are prominent in ethics discussions: the goals of exposome research, its standards, its tools, how it relates to study participants, and the consequences of its products. Furthermore, we provide a number of general principles for how future ethics research can best make use of our comprehensive overview of the ethical aspects of exposome research. Lastly, we highlight three aspects of exposome research that are most in need of ethical reflection: the actionability of its findings, the epidemiological or clinical norms applicable to exposome research, and the meaning and action-implications of bias.

Methods for Stratification and Validation Cohorts: A Scoping Review

Personalized medicine requires large cohorts for patient stratification and validation of patient clustering. However, standards and harmonized practices on the methods and tools to be used for the design and management of cohorts in personalized medicine remain to be defined. This study aims to describe the current state-of-the-art in this area. A scoping review was conducted searching in PubMed, EMBASE, Web of Science, Psycinfo and Cochrane Library for reviews about tools and methods related to cohorts used in personalized medicine. The search focused on cancer, stroke and Alzheimer's disease and was limited to reports in English, French, German, Italian and Spanish published from 2005 to April 2020. The screening process was reported through a PRISMA flowchart. Fifty reviews were included, mostly including information about how data were generated (25/50) and about tools used for data management and analysis (24/50). No direct information was found about the quality of data and the requirements to monitor associated clinical data. A scarcity of information and standards was found in specific areas such as sample size calculation. With this information, comprehensive guidelines could be developed in the future to improve the reproducibility and robustness in the design and management of cohorts in personalized medicine studies.

Multiple additive regression trees with hybrid loss for classification tasks across heterogeneous clinical data in distributed environments: a case study

Multiple additive regression trees (MART) have been widely used in the literature for various classification tasks. However, the overfitting effects of MART across heterogeneous and highly imbalanced big data structures within distributed environments has not yet been investigated. In this work, we utilize distributed MART with hybrid loss to resolve overfitting effects during the training of disease classification models in a case study with 10 heterogeneous and distributed clinical datasets. Lexical and semantic analysis methods were utilized to match heterogeneous terminologies with 80% overlap. Data augmentation was used to resolve class imbalance yielding virtual data with goodness of fit 0.01 and correlation difference 0.02. Our results highlight the favorable performance of the proposed distributed MART on the augmented data with an average increase by 7.3% in the accuracy, 6.8% in sensitivity, 10.4% in specificity, for a specific loss function topology.

Shaping a data-driven era in dementia care pathway through computational neurology approaches

BACKGROUND

Dementia is caused by a variety of neurodegenerative diseases and is associated with a decline in memory and other cognitive abilities, while inflicting an enormous socioeconomic burden. The complexity of dementia and its associated comorbidities presents immense challenges for dementia research and care, particularly in clinical decision-making.

MAIN BODY

Despite the lack of disease-modifying therapies, there is an increasing and urgent need to make timely and accurate clinical decisions in dementia diagnosis and prognosis to allow appropriate care and treatment. However, the dementia care pathway is currently suboptimal. We propose that through computational approaches, understanding of dementia aetiology could be improved, and dementia assessments could be more standardised, objective and efficient. In particular, we suggest that these will involve appropriate data infrastructure, the use of data-driven computational neurology approaches and the development of practical clinical decision support systems. We also discuss the technical, structural, economic, political and policy-making challenges that accompany such implementations.

CONCLUSION

The data-driven era for dementia research has arrived with the potential to transform the healthcare system, creating a more efficient, transparent and personalised service for dementia.

Overcoming the Barriers That Obscure the Interlinking and Analysis of Clinical Data Through Harmonization and Incremental Learning

Goal: To present a framework for data sharing, curation, harmonization and federated data analytics to solve open issues in healthcare, such as, the development of robust disease prediction models. Methods: Data curation is applied to remove data inconsistencies. Lexical and semantic matching methods are used to align the structure of the heterogeneous, curated cohort data along with incremental learning algorithms including class imbalance handling and hyperparameter optimization to enable the development of disease prediction models. Results: The applicability of the framework is demonstrated in a case study of primary Sjögren's Syndrome, yielding harmonized data with increased quality and more than 85% agreement, along with lymphoma prediction models with more than 80% sensitivity and specificity. Conclusions: The framework provides data quality, harmonization and analytics workflows that can enhance the statistical power of heterogeneous clinical data and enables the development of robust models for disease prediction.

Predicting lymphoma development in patients with Sjögren's syndrome

Introduction: The issue of predicting lymphoma in primary Sjögren's syndrome (pSS) starts from its clinical and biologic essence, i.e., an autoimmune exocrinopathy with sicca syndrome, inflammation and lymphoproliferation of MALT (mucosa-associated lymphoid tissue) in exocrine glands. Areas covered: The two major predictors to be firstly focused are persistent salivary gland (SG) swelling and cryoglobulinemic vasculitis with related features as purpura and low C4, or the sole serum cryoglobulinemia repeatedly detected. They are pathogenetically linked and reflect a heavier MALT involvement by histopathology, with the expansion of peculiar rheumatoid factor (RF)-positive clones/idiotypes. Other predictors include lymphadenopathy, splenomegaly, neutropenia, lymphopenia, serum beta2-microglobulin, monoclonal immunoglobulins, light chains, and RF. Composite indexes/scores may also predict lymphoma. Expert opinion: Prediction at baseline needs amelioration, and must be repeated in the follow-up. Careful clinical characterization, with harmonization and stratification of large cohorts, is a relevant preliminary step. Validated and new biomarkers are needed in biologic fluids and tissues. SG echography with automatic scoring could represent a future imaging biomarker, still lacking. Scoring MALT involvement in pSS, as an additional tool to evaluate disease activity and possibly to predict lymphoma, is welcomed. All these efforts are now ongoing within the HarmonicSS project and in other research initiatives in pSS.

Medical data quality assessment: On the development of an automated framework for medical data curation

Data quality assessment has gained attention in the recent years since more and more companies and medical centers are highlighting the importance of an automated framework to effectively manage the quality of their big data. Data cleaning, also known as data curation, lies in the heart of the data quality assessment and is a key aspect prior to the development of any data analytics services. In this work, we present the objectives, functionalities and methodological advances of an automated framework for data curation from a medical perspective. The steps towards the development of a system for data quality assessment are first described along with multidisciplinary data quality measures. A three-layer architecture which realizes these steps is then presented. Emphasis is given on the detection and tracking of inconsistencies, missing values, outliers, and similarities, as well as, on data standardization to finally enable data harmonization. A case study is conducted in order to demonstrate the applicability and reliability of the proposed framework on two well-established cohorts with clinical data related to the primary Sjögren's Syndrome (pSS). Our results confirm the validity of the proposed framework towards the automated and fast identification of outliers, inconsistencies, and highly-correlated and duplicated terms, as well as, the successful matching of more than 85% of the pSS-related medical terms in both cohorts, yielding more accurate, relevant, and consistent clinical data.