An interpretable deep learning framework identifies proteomic drivers of Alzheimer's disease.

Alzheimer's disease (AD) is the leading neurodegenerative pathology in aged individuals, but many questions remain on its pathogenesis, and a cure is still not available. Recent research efforts have generated measurements of multiple omics in individuals that were healthy or diagnosed with AD. Although machine learning approaches are well-suited to handle the complexity of omics data, the models typically lack interpretability. Additionally, while the genetic landscape of AD is somewhat more established, the proteomic landscape of the diseased brain is less well-understood. Here, we establish a deep learning method that takes advantage of an ensemble of autoencoders (AEs) - EnsembleOmicsAE-to reduce the complexity of proteomics data into a reduced space containing a small number of latent features. We combine brain proteomic data from 559 individuals across three AD cohorts and demonstrate that the ensemble autoencoder models generate stable latent features which are well-suited for downstream biological interpretation. We present an algorithm to calculate feature importance scores based on the iterative scrambling of individual input features (i.e., proteins) and show that the algorithm identifies signaling modules (AE signaling modules) that are significantly enriched in protein-protein interactions. The molecular drivers of AD identified within the AE signaling modules derived with EnsembleOmicsAE were missed by linear methods, including integrin signaling and cell adhesion. Finally, we characterize the relationship between the AE signaling modules and the age of death of the patients and identify a differential regulation of vimentin and MAPK signaling in younger compared with older AD patients.

Unlocking the Potential of Secondary Data for Public Health Research: Retrospective Study With a Novel Clinical Platform.

BACKGROUND: Clinical routine data derived from university hospitals hold immense value for health-related research on large cohorts. However, using secondary data for hypothesis testing necessitates adherence to scientific, legal (such as the General Data Protection Regulation, federal and state protection legislations), technical, and administrative requirements. This process is intricate, time-consuming, and susceptible to errors. OBJECTIVE: This study aims to develop a platform that enables clinicians to use current real-world data for testing research and evaluate advantages and limitations at a large university medical center (542,944 patients in 2022). METHODS: We identified requirements from clinical practitioners, conceptualized and implemented a platform based on the existing components, and assessed its applicability in clinical reality quantitatively and qualitatively. RESULTS: The proposed platform was established at the University Medical Center Hamburg-Eppendorf and made 639 forms encompassing 10,629 data elements accessible to all resident scientists and clinicians. Every day, the number of patients rises, and parts of their electronic health records are made accessible through the platform. Qualitatively, we were able to conduct a retrospective analysis of Parkinson disease over 777 patients, where we provide additional evidence for a significantly higher proportion of action tremors in patients with rest tremors (340/777, 43.8%) compared with those without rest tremors (255/777, 32.8%), as determined by a chi-square test (P<.001). Quantitatively, our findings demonstrate increased user engagement within the last 90 days, underscoring clinicians' increasing adoption of the platform in their regular research activities. Notably, the platform facilitated the retrieval of clinical data from 600,000 patients, emphasizing its substantial added value. CONCLUSIONS: This study demonstrates the feasibility of simplifying the use of clinical data to enhance exploration and sustainability in scientific research. The proposed platform emerges as a potential technological and legal framework for other medical centers, providing them with the means to unlock untapped potential within their routine data.

"Goldmine" or "big mess"? An interview study on the challenges of designing, operating, and ensuring the durability of Clinical Data Warehouses in France and Belgium.

OBJECTIVES: Clinical Data Warehouses (CDW) are the designated infrastructures to enable access and analysis of large quantities of electronic health record data. Building and managing such systems implies extensive "data work" and coordination between multiple stakeholders. Our study focuses on the challenges these stakeholders face when designing, operating, and ensuring the durability of CDWs for research. MATERIALS AND METHODS: We conducted semistructured interviews with 21 professionals working with CDWs from France and Belgium. All interviews were recorded, transcribed verbatim, and coded inductively. RESULTS: Prompted by the AI boom, healthcare institutions launched initiatives to repurpose data they were generating for care without a clear vision of how to generate value. Difficulties in operating CDWs arose quickly, strengthened by the multiplicity and diversity of stakeholders involved and grand discourses on the possibilities of CDWs, disjointed from their actual capabilities. Without proper management of the information flows, stakeholders struggled to build a shared vision. This was evident in our interviewees' contrasting appreciations of what mattered most to ensure data quality. Participants explained they struggled to manage knowledge inside and across institutions, generating knowledge loss, repeated mistakes, and impeding progress locally and nationally. DISCUSSION AND CONCLUSION: Management issues strongly affect the deployment and operation of CDWs. This may stem from a simplistic linear vision of how this type of infrastructure operates. CDWs remain promising for research, and their design, implementation, and operation require careful management if they are to be successful. Building on innovation management, complex systems, and organizational learning knowledge will help.

Proxy endpoints - bridging clinical trials and real world data.

OBJECTIVE: Disease severity scores, or endpoints, are routinely measured during Randomized Controlled Trials (RCTs) to closely monitor the effect of treatment. In real-world clinical practice, although a larger set of patients is observed, the specific RCT endpoints are often not captured, which makes it hard to utilize real-world data (RWD) to evaluate drug efficacy in larger populations. METHODS: To overcome this challenge, we developed an ensemble technique which learns proxy models of disease endpoints in RWD. Using a multi-stage learning framework applied to RCT data, we first identify features considered significant drivers of disease available within RWD. To create endpoint proxy models, we use Explainable Boosting Machines (EBMs) which allow for both end-user interpretability and modeling of non-linear relationships. RESULTS: We demonstrate our approach on two diseases, rheumatoid arthritis (RA) and atopic dermatitis (AD). As we show, our combined feature selection and prediction method achieves good results for both disease areas, improving upon prior methods proposed for predictive disease severity scoring. CONCLUSION: Having disease severity over time for a patient is important to further disease understanding and management. Our results open the door to more use cases in the space of RA and AD such as treatment effect estimates or prognostic scoring on RWD. Our framework may be extended beyond RA and AD to other diseases where the severity score is not well measured in electronic health records.

Differences Among Elderly Patients Having Asymmetric Sensorineural Hearing Loss With and Without Cerebellopontine Angle Tumors.

Objective: Asymmetric sensorineural hearing loss (ASNHL) exhibits a higher prevalence among the elderly compared to younger individuals, yet optimal management remains subject to ongoing debate. We aimed to elucidate the clinical disparities among elderly patients with ASNHL, distinguishing between those with and without cerebellopontine angle (CPA) tumors. Methods: A retrospective analysis was conducted on elderly patients (aged ≥65 years) diagnosed with ASNHL who underwent magnetic resonance imaging (MRI) between January 2012 and December 2022 at our tertiary referral center. Results: A total of 119 patients were enrolled, with a median age of 71 years (range: 65-89 years). Among them, 11 patients (9.2%) exhibited abnormal MRI findings. In the CPA tumors group, vestibular schwannoma was the most prevalent abnormality (63.6%), with a mean growth rate of 0.53 mm/year (range: 0-1.33 mm/year). The prevalence of CPA tumors in patients with diabetes mellitus (DM) and ASNHL was significantly lower than in those without DM (P = .021). Vertigo emerged as a significant associated symptom in cases with CPA tumors (P = .011). However, there were no significant differences in mean hearing thresholds or asymmetry of hearing loss at individual frequencies between the 2 groups. Conclusions: Elderly patients with ASNHL and vertigo should undergo radiological assessment. Patients with DM exhibit a lower prevalence of CPA tumors than those without DM, warranting careful observation and follow-up due to the limited diagnostic yield of MRI. No discernible differences in audiometric patterns were detected between patients with and without CPA tumors.

Expression level of myocardial enzymes in patients with schizophrenia: Predictive value in the occurrence of violence.

BACKGROUND: Schizophrenic patients are prone to violence, frequent recurrence, and difficult to predict. Emotional and behavioral abnormalities during the onset of the disease, resulting in active myocardial enzyme spectrum. AIM: To explored the expression level of myocardial enzymes in patients with schizophrenia and its predictive value in the occurrence of violence. METHODS: A total of 288 patients with schizophrenia in our hospital from February 2023 to January 2024 were selected as the research object, and 100 healthy people were selected as the control group. Participants' information, clinical data, and laboratory examination data were collected. According to Modified Overt Aggression Scale score, patients were further divided into the violent (123 cases) and non-violent group (165 cases). RESULTS: The comparative analysis revealed significant differences in serum myocardial enzyme levels between patients with schizophrenia and healthy individuals. In the schizophrenia group, the violent and non-violent groups also exhibited different levels of serum myocardial enzymes. The levels of myocardial enzymes in the non-violent group were lower than those in the violent group, and the patients in the latter also displayed aggressive behavior in the past. CONCLUSION: Previous aggressive behavior and the level of myocardial enzymes are of great significance for the diagnosis and prognosis analysis of violent behavior in patients with schizophrenia. By detecting changes in these indicators, we can gain a more comprehensive understanding of a patient's condition and treatment.

Paediatric Drug Development in Japan: Current Status and Future Challenges.

INTRODUCTION: Until around 2000, the number of medicinal products labelled for paediatric use was limited worldwide. Regulatory measures to promote paediatric drug development in the US and Europe and the establishment of an international guideline (ICH-E11) have led to an increase in the number of paediatric labels. In Japan, efforts have been made to promote the development of paediatric drugs. This study was aimed to examine whether these supportive efforts are successful in Japan. METHODS: This study examined the number of new drugs approved for paediatric indications in Japan from 2006 to 2023, as well as the clinical data package, that is, characteristics of the approved paediatric drugs and paediatric clinical trials, and the percentage of extrapolation of adult data, in the most recent 9-year period. RESULTS: The number of paediatric drug approvals showed an increasing trend between 2006 and 2023 with some fluctuations. The proportion of drugs indicated for paediatric patients to the total number of approved drugs was about 30% until 2022, but increased to 48% in 2023. During the period from 2015 to 2023, simultaneous development in adults and children accounted for 59% (159/269) of paediatric development, but the complete extrapolation of adult data to paediatric populations has not been widely utilized (11.2%, 30/269). CONCLUSIONS: The number of paediatric drug approvals has shown an upward trend, suggesting that measures to promote the development of paediatric drugs may have been exerting a favourable effect in Japan. However, there is still a limited number of drugs that have additional indications for paediatric use. Appropriate development strategies, such as the extrapolation of adult data to paediatric populations, should be considered if scientifically justified.

Applications of Artificial Intelligence-Based Systems in the Management of Esophageal Varices.

BACKGROUND: Esophageal varices, dilated submucosal veins in the lower esophagus, are commonly associated with portal hypertension, particularly due to liver cirrhosis. The high morbidity and mortality linked to variceal hemorrhage underscore the need for accurate diagnosis and effective management. The traditional method of assessing esophageal varices is esophagogastroduodenoscopy (EGD), which, despite its diagnostic and therapeutic capabilities, presents limitations such as interobserver variability and invasiveness. This review aims to explore the role of artificial intelligence (AI) in enhancing the management of esophageal varices, focusing on its applications in diagnosis, risk stratification, and treatment optimization. METHODS: This systematic review focuses on the capabilities of AI algorithms to analyze clinical scores, laboratory data, endoscopic images, and imaging modalities like CT scans. RESULTS: AI-based systems, particularly machine learning (ML) and deep learning (DL) algorithms, have demonstrated the ability to improve risk stratification and diagnosis of esophageal varices, analyzing vast amounts of data, identifying patterns, and providing individualized recommendations. However, despite these advancements, clinical scores based on laboratory data still show low specificity for esophageal varices, often requiring confirmatory endoscopic or imaging studies. CONCLUSIONS: AI integration in managing esophageal varices offers significant potential for advancing diagnosis, risk assessment, and treatment strategies. While promising, AI systems should complement rather than replace traditional methods, ensuring comprehensive patient evaluation. Further research is needed to refine these technologies and validate their efficacy in clinical practice.

Large language models outperform traditional natural language processing methods in extracting patient-reported outcomes in IBD.

Background and Aims: Patient-reported outcomes (PROs) are vital in assessing disease activity and treatment outcomes in inflammatory bowel disease (IBD). However, manual extraction of these PROs from the free-text of clinical notes is burdensome. We aimed to improve data curation from free-text information in the electronic health record, making it more available for research and quality improvement. This study aimed to compare traditional natural language processing (tNLP) and large language models (LLMs) in extracting three IBD PROs (abdominal pain, diarrhea, fecal blood) from clinical notes across two institutions. Methods: Clinic notes were annotated for each PRO using preset protocols. Models were developed and internally tested at the University of California San Francisco (UCSF), and then externally validated at Stanford University. We compared tNLP and LLM-based models on accuracy, sensitivity, specificity, positive and negative predictive value. Additionally, we conducted fairness and error assessments. Results: Inter-rater reliability between annotators was >90%. On the UCSF test set (n=50), the top-performing tNLP models showcased accuracies of 92% (abdominal pain), 82% (diarrhea) and 80% (fecal blood), comparable to GPT-4, which was 96%, 88%, and 90% accurate, respectively. On external validation at Stanford (n=250), tNLP models failed to generalize (61-62% accuracy) while GPT-4 maintained accuracies >90%. PaLM-2 and GPT-4 showed similar performance. No biases were detected based on demographics or diagnosis. Conclusions: LLMs are accurate and generalizable methods for extracting PROs. They maintain excellent accuracy across institutions, despite heterogeneity in note templates and authors. Widespread adoption of such tools has the potential to enhance IBD research and patient care.

Promoting Collaborative Scholarship During the COVID-19 Pandemic Through an Innovative COVID-19 Data Explorer and Repository at Yale School of Medicine: Development and Usability Study.

BACKGROUND: The COVID-19 pandemic sparked a surge of research publications spanning epidemiology, basic science, and clinical science. Thanks to the digital revolution, large data sets are now accessible, which also enables real-time epidemic tracking. However, despite this, academic faculty and their trainees have been struggling to access comprehensive clinical data. To tackle this issue, we have devised a clinical data repository that streamlines research processes and promotes interdisciplinary collaboration. OBJECTIVE: This study aimed to present an easily accessible up-to-date database that promotes access to local COVID-19 clinical data, thereby increasing efficiency, streamlining, and democratizing the research enterprise. By providing a robust database, a broad range of researchers (faculty and trainees) and clinicians from different areas of medicine are encouraged to explore and collaborate on novel clinically relevant research questions. METHODS: A research platform, called the Yale Department of Medicine COVID-19 Explorer and Repository (DOM-CovX), was constructed to house cleaned, highly granular, deidentified, and continually updated data from over 18,000 patients hospitalized with COVID-19 from January 2020 to January 2023, across the Yale New Haven Health System. Data across several key domains were extracted including demographics, past medical history, laboratory values during hospitalization, vital signs, medications, imaging, procedures, and outcomes. Given the time-varying nature of several data domains, summary statistics were constructed to limit the computational size of the database and provide a reasonable data file that the broader research community could use for basic statistical analyses. The initiative also included a front-end user interface, the DOM-CovX Explorer, for simple data visualization of aggregate data. The detailed clinical data sets were made available for researchers after a review board process. RESULTS: As of January 2023, the DOM-CovX Explorer has received 38 requests from different groups of scientists at Yale and the repository has expanded research capability to a diverse group of stakeholders including clinical and research-based faculty and trainees within 15 different surgical and nonsurgical specialties. A dedicated DOM-CovX team guides access and use of the database, which has enhanced interdepartmental collaborations, resulting in the publication of 16 peer-reviewed papers, 2 projects available in preprint servers, and 8 presentations in scientific conferences. Currently, the DOM-CovX Explorer continues to expand and improve its interface. The repository includes up to 3997 variables across 7 different clinical domains, with continued growth in response to researchers' requests and data availability. CONCLUSIONS: The DOM-CovX Data Explorer and Repository is a user-friendly tool for analyzing data and accessing a consistently updated, standardized, and large-scale database. Its innovative approach fosters collaboration, diversity of scholarly pursuits, and expands medical education. In addition, it can be applied to other diseases beyond COVID-19.

The application of predictive value of diabetes autoantibody profile combined with clinical data and routine laboratory indexes in the classification of diabetes mellitus.

Objective: Currently, distinct use of clinical data, routine laboratory indicators or the detection of diabetic autoantibodies in the diagnosis and management of diabetes mellitus is limited. Hence, this study was aimed to screen the indicators, and to establish and validate a multifactorial logistic regression model nomogram for the non-invasive differential prediction of type 1 diabetes mellitus. Methods: Clinical data, routine laboratory indicators, and diabetes autoantibody profiles of diabetic patients admitted between September 2018 and December 2022 were retrospectively analyzed. Logistic regression was used to select the independent influencing factors, and a prediction nomogram based on the multiple logistic regression model was constructed using these independent factors. Moreover, the predictive accuracy and clinical application value of the nomogram were evaluated using Receiver Operating Characteristic (ROC) curves, calibration curves, decision curve analysis (DCA), and clinical impact curves (CIC). Results: A total of 522 diabetic patients were included in this study. These patients were randomized into training and validation sets in a 7:3 ratio. The predictors screened included age, prealbumin (PA), high-density lipoprotein cholesterol (HDL-C), islet cells autoantibodies (ICA), islets antigen 2 autoantibodies (IA-2A), glutamic acid decarboxylase antibody (GADA), and C-peptide levels. Based on these factors, a multivariate model nomogram was constructed, which had an Area Under Curve (AUC) of 0.966 and 0.961 for the training set and validation set, respectively. Subsequently, the calibration curves demonstrated a strong accuracy of the graph; the DCA and CIC results indicated that the graph could be used as a non-invasive valid predictive tool for the differential diagnosis of type 1 diabetes mellitus, clinically. Conclusion: The established prediction model combining patient's age, PA, HDL-C, ICA, IA-2A, GADA, and C-peptide can assist in differential diagnosis of type 1 diabetes mellitus and type 2 diabetes mellitus and provides a basis for the clinical as well as therapeutic management of the disease.

IN Esketamine and IV Ketamine: Results of a multi-site observational study assessing the effectiveness and tolerability of two novel therapies for treatment-resistant depression.

Intravenous (IV) ketamine and intranasal (IN) esketamine are novel therapies to manage treatment resistant depression within major depressive disorder (MDD-TRD). This is a multi-site observational study aiming to assess the real-world effectiveness and tolerability of these novel therapies in the management of MDD-TRD. 53 patients were referred to receive IV ketamine (n = 26, 69.23 % female, 52.81 ± 14.33 years old) or IN esketamine (n = 27, 51.85 % female, 43.93 ± 13.57 years old). Treatment effectiveness was assessed using the Montgomery and Åsberg Depression Rating Scale (MADRS) for depression severity and item 10 of the MADRS for suicidal ideation (SI). Tolerability was assessed by systematically tracking side effects and depersonalization using the 6-item Clinician administered dissociative symptom scale (CADSS-6). The data was analyzed using descriptive statistics, risk ratio and effect size. Both IV ketamine and IN esketamine significantly reduced depressive symptoms and suicidal ideation by treatment endpoint. Patients receiving IN esketamine, and patients receiving IV ketamine had a similar risk of developing side effects. All side effects reported were mild and transient. These results suggested that both IV ketamine and IN esketamine are effective in the management of depressive symptoms and were well tolerated. Therefore, the results of this study could serve to inform clinical practice.

A conditional multi-label model to improve prediction of a rare outcome: An illustration predicting autism diagnosis.

OBJECTIVE: This study aimed to develop a novel approach using routinely collected electronic health records (EHRs) data to improve the prediction of a rare event. We illustrated this using an example of improving early prediction of an autism diagnosis, given its low prevalence, by leveraging correlations between autism and other neurodevelopmental conditions (NDCs). METHODS: To achieve this, we introduced a conditional multi-label model by merging conditional learning and multi-label methodologies. The conditional learning approach breaks a hard task into more manageable pieces in each stage, and the multi-label approach utilizes information from related neurodevelopmental conditions to learn predictive latent features. The study involved forecasting autism diagnosis by age 5.5 years, utilizing data from the first 18 months of life, and the analysis of feature importance correlations to explore the alignment within the feature space across different conditions. RESULTS: Upon analysis of health records from 18,156 children, we are able to generate a model that predicts a future autism diagnosis with moderate performance (AUROC=0.76). The proposed conditional multi-label method significantly improves predictive performance with an AUROC of 0.80 (p < 0.001). Further examination shows that both the conditional and multi-label approach alone provided marginal lift to the model performance compared to a one-stage one-label approach. We also demonstrated the generalizability and applicability of this method using simulated data with high correlation between feature vectors for different labels. CONCLUSION: Our findings underscore the effectiveness of the developed conditional multi-label model for early prediction of an autism diagnosis. The study introduces a versatile strategy applicable to prediction tasks involving limited target populations but sharing underlying features or etiology among related groups.

Clinical data on treatment regimen and use of medication among patients with hemophilia B in Korea.

BACKGROUND: To investigate the clinical treatment status, such as treatment regimen, bleeding events, and drug dose, in patients with hemophilia B in South Korea. METHODS: In this retrospective chart review, data of patients with hemophilia B from eight university hospitals were collected. Demographic and clinical data, treatment data, such as regimen and number of injections, dose of factor IX concentrate, and bleeding data were reviewed. Descriptive analyses were performed with annual data for 2019, 2020, and 2021, as well as the three years consecutively. RESULTS: The medical records of 150 patients with hemophilia B between January 1, 2019, and December 31, 2021, were collected. Among these, 72 (48.0%) were severe, 47 (31.3%) were moderate, and 28 (18.7%) were mild. The results showed approximately two times more patients receiving prophylaxis as those receiving on-demand therapy, with 66.1% of patients receiving prophylaxis in 2019, 64.9% in 2020, and 72.1% in 2021. Annualized bleeding rates were 2.2% (± 3.1) in 2019, 1.8% (± 3.0) in 2020, and 1.8% (± 2.9) in 2021 among patients receiving prophylaxis. For the doses of factor IX concentrate, patients receiving prophylaxis received an average of 41.6 (± 11.9) IU/Kg/Injection in 2019, 45.7 (± 12.9) IU/Kg/Injection in 2020, and 60.1 (± 24.0) IU/Kg/Injection in 2021. CONCLUSIONS: Clinically, prophylaxis is more prevalent than reported. Based on insights gained from current clinical evidence, it is expected that the unmet medical needs of patients can be identified, and physicians can evaluate the status of patients and actively manage hemophilia B using more effective treatment strategies.

Electronic data capture in resource-limited settings using the lightweight clinical data acquisition and recording system.

Our prototype system designed for clinical data acquisition and recording of studies is a novel electronic data capture (EDC) software for simple and lightweight data capture in clinical research. Existing software tools are either costly or suffer from very limited features. To overcome these shortcomings, we designed an EDC software together with a mobile client. We aimed at making it easy to set-up, modifiable, scalable and thereby facilitating research. We wrote the software in R using a modular approach and implemented existing data standards along with a meta data driven interface and database structure. The prototype is an adaptable open-source software, which can be installed locally or in the cloud without advanced IT-knowledge. A mobile web interface and progressive web app for mobile use and desktop computers is added. We show the software's capability, by demonstrating four clinical studies with over 1600 participants and 679 variables per participant. We delineate a simple deployment approach for a server-installation and indicate further use-cases. The software is available under the MIT open-source license. Conclusively the software is versatile, easily deployable, highly modifiable, and extremely scalable for clinical studies. As an open-source R-software it is accessible, open to community-driven development and improvement in the future.

A Comparative Study on Imputation Techniques: Introducing a Transformer Model for Robust and Efficient Handling of Missing EEG Amplitude Data.

In clinical datasets, missing data often occur due to various reasons including non-response, data corruption, and errors in data collection or processing. Such missing values can lead to biased statistical analyses, reduced statistical power, and potentially misleading findings, making effective imputation critical. Traditional imputation methods, such as Zero Imputation, Mean Imputation, and k-Nearest Neighbors (KNN) Imputation, attempt to address these gaps. However, these methods often fall short of accurately capturing the underlying data complexity, leading to oversimplified assumptions and errors in prediction. This study introduces a novel Imputation model employing transformer-based architectures to address these challenges. Notably, the model distinguishes between complete EEG signal amplitude data and incomplete data in two datasets: PhysioNet and CHB-MIT. By training exclusively on complete amplitude data, the TabTransformer accurately learns and predicts missing values, capturing intricate patterns and relationships inherent in EEG amplitude data. Evaluation using various error metrics and R2 score demonstrates significant enhancements over traditional methods such as Zero, Mean, and KNN imputation. The Proposed Model achieves impressive R2 scores of 0.993 for PhysioNet and 0.97 for CHB-MIT, highlighting its efficacy in handling complex clinical data patterns and improving dataset integrity. This underscores the transformative potential of transformer models in advancing the utility and reliability of clinical datasets.

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

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. BTO assists in organizing and standardizing the data collected during patient follow-up. It was created by harmonizing schemas currently used by multiple medical centers into a common ontology, following a bottom-up approach. As a result, BTO effectively addresses the practical data collection needs of various real-world situations and promotes data portability and interoperability. BTO captures various clinical occurrences, such as disease onset, symptoms, diagnostic and therapeutic procedures, and relapses, using an event-based approach. Developed in collaboration with medical partners and domain experts, BTO offers a holistic view of ALS and MS for supporting the representation of retrospective and prospective data. Furthermore, BTO adheres to Open Science and FAIR (Findable, Accessible, Interoperable, and Reusable) principles, making it a reliable framework for developing predictive tools to aid in medical decision-making and patient care. Although BTO is designed for ALS and MS, its modular structure makes it easily extendable to other brain-related diseases, showcasing its potential for broader applicability.Database URL  https://zenodo.org/records/7886998 .