Medical Domain Research Articles

Federated unsupervised random forest for privacy-preserving patient stratification.

In the realm of precision medicine, effective patient stratification and disease subtyping demand innovative methodologies tailored for multi-omics data. Clustering techniques applied to multi-omics data have become instrumental in identifying distinct subgroups of patients, enabling a finer-grained understanding of disease variability. Meanwhile, clinical datasets are often small and must be aggregated from multiple hospitals. Online data sharing, however, is seen as a significant challenge due to privacy concerns, potentially impeding big data's role in medical advancements using machine learning. This work establishes a powerful framework for advancing precision medicine through unsupervised random forest-based clustering in combination with federated computing. We introduce a novel multi-omics clustering approach utilizing unsupervised random forests. The unsupervised nature of the random forest enables the determination of cluster-specific feature importance, unraveling key molecular contributors to distinct patient groups. Our methodology is designed for federated execution, a crucial aspect in the medical domain where privacy concerns are paramount. We have validated our approach on machine learning benchmark datasets as well as on cancer data from The Cancer Genome Atlas. Our method is competitive with the state-of-the-art in terms of disease subtyping, but at the same time substantially improves the cluster interpretability. Experiments indicate that local clustering performance can be improved through federated computing. The proposed methods are available as an R-package (https://github.com/pievos101/uRF).

Recent Advances in the Development and Utilization of Nanoparticles for the Management of Malignant Solid Tumors.

The purpose of nanotechnology-based drug delivery systems or novel drug delivery systems is to improve the effectiveness of therapy, and their promising properties have led to their increasing significance in the management of cancer. The researchers have primarily focused on designing novel nanocarriers, like nanoparticles (NPs), that can effectively deliver drugs to target cells and respond specifically to conditions particular to cancer. Whether passive or active targeting, these nanocarriers can deliver therapeutic cargoes to the tumor site to release the drug from the drug delivery systems. The purpose of this study is to provide recent scientific literature and key findings to researchers as well as the scientific community from the medical and pharmaceutical domains by reporting current advancements in the development of NPs for the treatment of different malignant solid tumors, such as colorectal, pancreatic, prostate, and cervical cancer.

Comparing the predictive utility of patient-reported medical, psychosocial, and eating-specific domains in bariatric presurgical evaluation

Comparing the predictive utility of patient-reported medical, psychosocial, and eating-specific domains in bariatric presurgical evaluation

Ethics on academic procurement of cadavers.

Anatomy is a vital discipline in the realm of Medicine, and its primary means of study is through the use of cadavers. Experts in the medical, legal, and ethical domains have discussed the use of cadavers or their anatomical parts for educational purposes, which are considered a severe drawback. The advantages of using cadavers include their contribution to medical education, research, the investigation of innovative surgical procedures or techniques, the detection of anatomical differences at an individual or population level, the enhancement of surgical skills, and the support of other anatomical investigations. This review highlights the issues like consent, respect for the deceased and cultural beliefs on cadaver procurement practices and also it emphasizes the need for better body donation initiatives and public awareness campaigns to ensure sustainable cadaver procurement practices, ensuring consent, respect for the deceased and cultural beliefs.

A Novel Evaluation Framework for Medical LLMs: Combining Fuzzy Logic and MCDM for Medical Relation and Clinical Concept Extraction.

Artificial intelligence (AI) has become a crucial element of modern technology, especially in the healthcare sector, which is apparent given the continuous development of large language models (LLMs), which are utilized in various domains, including medical beings. However, when it comes to using these LLMs for the medical domain, there's a need for an evaluation platform to determine their suitability and drive future development efforts. Towards that end, this study aims to address this concern by developing a comprehensive Multi-Criteria Decision Making (MCDM) approach that is specifically designed to evaluate medical LLMs. The success of AI, particularly LLMs, in the healthcare domain, depends on their efficacy, safety, and ethical compliance. Therefore, it is essential to have a robust evaluation framework for their integration into medical contexts. This study proposes using the Fuzzy-Weighted Zero-InConsistency (FWZIC) method extended to p, q-quasirung orthopair fuzzy set (p, q-QROFS) for weighing evaluation criteria. This extension enables the handling of uncertainties inherent in medical decision-making processes. The approach accommodates the imprecise and multifaceted nature of real-world medical data and criteria by incorporating fuzzy logic principles. The MultiAtributive Ideal-Real Comparative Analysis (MAIRCA) method is employed for the assessment of medical LLMs utilized in the case study of this research. The results of this research revealed that "Medical Relation Extraction" criteria with its sub-levels had more importance with (0.504) than "Clinical Concept Extraction" with (0.495). For the LLMs evaluated, out of 6 alternatives, ( ) "GatorTron S 10B" had the 1st rank as compared to ( ) "GatorTron 90B" had the 6th rank. The implications of this study extend beyond academic discourse, directly impacting healthcare practices and patient outcomes. The proposed framework can help healthcare professionals make more informed decisions regarding the adoption and utilization of LLMs in medical settings.

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.

Deep Interactive Segmentation of Medical Images: A Systematic Review and Taxonomy.

Interactive segmentation is a crucial research area in medical image analysis aiming to boost the efficiency of costly annotations by incorporating human feedback. This feedback takes the form of clicks, scribbles, or masks and allows for iterative refinement of the model output so as to efficiently guide the system towards the desired behavior. In recent years, deep learning-based approaches have propelled results to a new level causing a rapid growth in the field with 121 methods proposed in the medical imaging domain alone. In this review, we provide a structured overview of this emerging field featuring a comprehensive taxonomy, a systematic review of existing methods, and an in-depth analysis of current practices. Based on these contributions, we discuss the challenges and opportunities in the field. For instance, we find that there is a severe lack of comparison across methods which needs to be tackled by standardized baselines and benchmarks.

Comparative Evaluation of K-Means, Hierarchical Clustering, and DBSCAN in Blood Donor Segmentation

Clustering techniques are pivotal in the fields of data analysis and pattern recognition, offering significant insights by grouping data points with similar characteristics. This study aims to perform a comprehensive comparison of three widely used clustering algorithms—K-Means, Hierarchical Clustering, and DBSCAN—on a dataset of blood donors. The objective is to determine which algorithm achieves the most precise and effective clustering of the data, taking into account factors such as donor location, blood type, and donation frequency. The study presents a novel approach by integrating a web-based platform that allows blood donors to register online. This platform not only facilitates the real-time updating of the dataset but also enhances the overall relevance and applicability of the clustering model by continuously incorporating new data entries. By leveraging such a dynamic dataset, the clustering algorithms can adapt to evolving patterns and trends, ensuring more accurate and meaningful insights over time.To rigorously evaluate the performance of each clustering method, several well-established metrics are employed, including the Silhouette Score, which assesses how similar each data point is to its own cluster compared to other clusters; the Davies-Bouldin Index, which evaluates the average similarity ratio of each cluster with its most similar cluster; and the Calinski-Harabasz Index, which measures the ratio of the sum of between-clusters dispersion and of within-cluster dispersion for all clusters. The results of this study indicate that the K-Means algorithm consistently outperforms both Hierarchical Clustering and DBSCAN in terms of accuracy and the clarity of cluster definitions. The findings underscore the robustness of K-Means for applications involving blood donor data, where capturing precise donor groupings can have substantial implications for healthcare logistics and resource allocation. These insights pave the way for further research into the optimization of clustering techniques in dynamic datasets and their practical applications in medical and other domains.

Combinatory electric-field-guided deposition for spatial microparticles patterning

Spatial deposition and patterning of microparticles are crucial in chemistry, medicine, and biology. Existing technologies like electric force manipulation, despite precise trajectory control, struggle with complex and personalized patterns. Key challenges include adjusting the quantity of particles deposited in different areas and accurately depositing particles in non-continuous patterns. Here, we present a rational process termed combinatory electric-field-guided deposition (CED) for achieving spatially regulated microparticle deposition on insulative substrates. This process involves coating the substrates with insulating materials like PVP and positioning it on a relief-patterned negative electrode. The negative electric field generated by the electrode attracts microparticles, while the positive surface charges on the substrates repel microparticles, resulting in the formation of a potential well over the electrode area. Consequently, this configuration enables precise control over microparticle deposition without the need for direct contact with the substrate's surface, simplifying the process of switching masks to meet varying microparticle deposition requirements. Furthermore, we demonstrate the customization of patterned microparticles on superhydrophobic coatings to regulate cell distribution, as well as the successful loading of drug-laden microparticles onto antibacterial bandages to match the areas of skin lesions. These applications underscore the versatility of CED across chemical, medical, and bioengineering domains.

Assessing the ChatGPT aptitude: A competent and effective Dermatology doctor?

BackgroundThe efficacy and adeptness of ChatGPT 3.5 and ChatGPT 4.0 in the precise diagnosis and management of conditions like atopic dermatitis and Autoimmune blistering skin diseases (AIBD) remain to be elucidated. So this study examined the accuracy and effectiveness of the ChatGPT responses related to understanding, therapies, and specific cases of these two conditions. MethodFirstly, the responses provided by ChatGPTs to a set of 50 questionnaires underwent evaluation by five distinct dermatologists, with complete adjudication of the third-party reviewer. The comparative analysis included the evaluative efficacy of both ChatGPT3.5 and ChatGPT4.0 against the diagnostic abilities exhibited by three distinct cohorts of qualified clinical professionals. And then, an examination was conducted to assess the diagnostic proficiency of ChatGPT3.5 and ChatGPT4.0 in the context of diagnosing specific instances of skin blistering autoimmune diseases. ResultsIn assessing the proficiency of ChatGPTs in generating responses related to fundamental knowledge about AD it is noteworthy that both versions of ChatGPTs, despite their lack of specialized training on medical databases, exhibited a commendable capacity to yield solutions that exhibited a substantial degree of concurrence with evidence-based medical information. Accordingly we observed that the performance of ChatGPT-4.0 beyond that of the ChatGPT-3.5. However, it it crucial to emphasize that ChatGPT-4.0 did not show the ability to offer answers surpassing those provided by associate senior, and senior medical professionals. In the assessment designed to determine the proficiency of ChatGPTs in recognizing particular type of AIBD, it is evident that both ChatGPT-4 and ChatGPT-3.5 demonstrated inadequacy in providing responses that are both precise and accurate for each individual occurrence of this skin condition. ConclusionBoth ChatGPT-3.5 and ChatGPT-4.0 satisfactory for addressing fundamental inquiries related to atopic dermatitis, however they prove insufficient for diagnosing AIBD. The progress of ChatGPT in achieving utility within the professional medical domain remains a considerable journey ahead.

Circulating miRNAs modulating systemic low-grade inflammation and affecting neurodegeneration

Objective and design: Inflammatory processes are an important part of the etiology of many chronic diseases across various medical domains, including neurodegeneration. Understanding their regulation on the molecular level represents a major challenge. Regulatory microRNAs (miRNAs), have been recognized for their role in post-transcriptionally modulating immune-related pathways serving as biomarkers for numerous diseases. Subjects and Methods: This study aims to investigate the association between 176 plasma-circulating miRNAs and the blood-based immune markers C-reactive protein and fibrinogen within the general population-based SHIP-TREND-0 cohort (N = 801) and assess their impact on neurodegeneration in linear regression and moderation analyses. Results: We provide strong evidence for miRNA-mediated regulation, particularly in relation to fibrinogen, identifying 48 significant miRNAs with a pronounced over-representation in chronic inflammatory and neurological diseases. Additional moderation analyses explored the influence of the APOE ε4 genotype and brain white matter neurodegeneration on the association between miRNAs and inflammation. Again, significant associations were observed for fibrinogen with special emphasize on hsa-miR-148a-3p, known to impact on neuroinflammation. Conclusions: Our study suggests the involvement of several plasma-circulating miRNAs in regulating immunological markers while also being linked to neurodegeneration. The strong interplay between miRNAs and inflammation holds promising potential for clinical application in many immune-related neurodegenerative diseases.

ProcessGAN: Generating Privacy-Preserving Time-Aware Process Data with Conditional Generative Adversarial Nets

Process data constructed from event logs provides valuable insights into procedural dynamics over time. The confidential information in process data, together with the data's intricate nature, makes the datasets not sharable and challenging to collect. Consequently, research is limited using process data and analytics in the process mining domain. In this study, we introduced a synthetic process data generation task to address the limitation of sharable process data. We introduced a generative adversarial network, called ProcessGAN, to generate process data with activity sequences and corresponding timestamps. ProcessGAN consists of a transformer-based network as the generator, and a time-aware self-attention network as the discriminator. It can generate privacy-preserving process data from random noise. ProcessGAN considers the duration of the process and time intervals between activities to generate realistic activity sequences with timestamps. We evaluated ProcessGAN on five real-world datasets, two that are public and three collected in medical domains that are private. To evaluate the synthetic data, in addition to statistical metrics, we trained a supervised model to score the synthetic processes. We also used process mining to discover workflows for synthetic medical processes and had domain experts evaluate the clinical applicability of the synthetic workflows. ProcessGAN outperformed the existing generative models in generating complex processes with valid parallel pathways. The synthetic process data generated by ProcessGAN better represented the long-range dependencies between activities, a feature relevant to complicated medical and other processes. The timestamps generated by the ProcessGAN model showed similar distributions with the authentic timestamps. In addition, we trained a transformer-based network to generate synthetic contexts (e.g., patient demographics) that were associated with the synthetic processes. The synthetic contexts generated by our model outperformed the baseline models, with the distributions similar to the authentic contexts. We conclude that ProcessGAN can generate sharable synthetic process data indistinguishable from authentic data. Our source code is available in https://github.com/raaachli/ProcessGAN .

An Unsupervised Domain-Adaptive Framework for Chinese Spelling Checking

Chinese Spelling Check (CSC) is a meaningful task in the area of Natural Language Processing (NLP), which aims at detecting spelling errors in Chinese texts and then correcting these errors. Current typical Chinese Spelling Check models have shown impressive performance in general datasets with the help of pretrained language models such as BERT, but suffer great perform loss in downstream tasks with domain-specific terms because they are primarily trained on general corpora. To verify the cross-domain adaptation ability of these models, we build three new datasets with abundant domain-specific terms on financial, medical, and legal domains and conduct empirical investigations on them in the corresponding domain-specific test datasets to verify the cross-domain adaptation ability. In response to the poor performance of the existing models, we propose a framework named uChecker which utilizes unsupervised method in spelling error detection and correction. Experiment results prove that uChecker can perform well in domain-specific test datasets while not losing its performance in the general domain.

ORASIS-MAE Harnesses the Potential of Self-Learning from Partially Annotated Clinical Eye Movement Records

Self-supervised learning (SSL) has gained significant attention in the past decade for its capacity to utilize non-annotated datasets to learn meaningful data representations. In the medical domain, the challenge of constructing large annotated datasets presents a significant limitation, rendering SSL an ideal approach to address this constraint. In this study, we introduce a novel pretext task tailored to stimulus-driven eye movement data, along with a denoising task to improve the robustness against simulated eye tracking failures. Our proposed task aims to capture both the characteristics of the pilot (brain) and the motor (eye) by learning to reconstruct the eye movement position signal using up to 12.5% of the unmasked eye movement signal patches, along with the entire REMOBI target signal. Thus, the encoder learns a high-dimensional representation using a multivariate time series of length 8192 points, corresponding to approximately 40 s. We evaluate the learned representation on screening eight distinct groups of pathologies, including dyslexia, reading disorder, and attention deficit disorder, across four datasets of varying complexity and size. Furthermore, we explore various head architecture designs along with different transfer learning methods, demonstrating promising results with improvements of up to approximately 15%, leading to an overall macro F1 score of 61% and 61.5% on the Saccade and the Vergence datasets, respectively. Notably, our method achieves macro F1 scores of 64.7%, 66.1%, and 61.1% for screening dyslexia, reading disorder, and attention deficit disorder, respectively, on clinical data. These findings underscore the potential of self-learning algorithms in pathology screening, particularly in domains involving complex data such as stimulus-driven eye movement analysis.

Distributed Harmonization: Federated Clustered Batch Effect Adjustment and Generalization.

Independent and identically distributed (i.i.d.) data is essential to many data analysis and modeling techniques. In the medical domain, collecting data from multiple sites or institutions is a common strategy that guarantees sufficient clinical diversity, determined by the decentralized nature of medical data. However, data from various sites are easily biased by the local environment or facilities, thereby violating the i.i.d. rule. A common strategy is to harmonize the site bias while retaining important biological information. The COMBAT is among the most popular harmonization approaches and has recently been extended to handle distributed sites. However, when faced with situations involving newly joined sites in training or evaluating data from unknown/unseen sites, COMBAT lacks compatibility and requires retraining with data from all the sites. The retraining leads to significant computational and logistic overhead that is usually prohibitive. In this work, we develop a novel Cluster ComBat harmonization algorithm, which leverages cluster patterns of the data in different sites and greatly advances the usability of COMBAT harmonization. We use extensive simulation and real medical imaging data from ADNI to demonstrate the superiority of the proposed approach. Our codes are provided in https://github.com/illidanlab/distributed-cluster-harmonization.

Generative Adversarial Networks in Business and Social Science

Generative adversarial networks (GANs) have become a recent and rapidly developing research topic in machine learning. Since their inception in 2014, a significant number of variants have been proposed to address various topics across many fields, and they have particularly excelled not only in image and language processing but also in the medical and data science domains. In this paper, we aim to highlight the significance of and advancements that these GAN models can introduce in the field of Business Economics, where they have yet to be fully developed. To this end, a review of the literature of GANs is presented in general together with a more specific review in the field of Business Economics, for which only a few papers can be found. Furthermore, the most relevant papers are analysed in order to provide approaches for the opportunity to research GANs in the field of Business Economics.

Semantic Mapping of Named-Entities in openEHR Templates and Ad-hoc Generation of Compositions.

Integration of free texts from reports written by physicians to an interoperable standard is important for improving patient-centric care and research in the medical domain. In the context of unstructured clinical data, NLP Information Extraction serves in finding information in unstructured text. To our best knowledge, there is no efficient solution, in which extracted Named-Entities of an NLP pipeline can be ad-hoc inserted in openEHR compositions. We therefore developed a software solution that solves this data integration problem by mapping Named-Entities of an NLP pipeline to the fields of an openEHR template. The mapping can be accomplished by any user without any programming intervention and allows the ad-hoc creation of a composition based on the mappings.

Automation of Trainable Datasets Generation for Medical-Specific Language Model: Using MIMIC-IV Discharge Notes.

This study introduces a novel approach for generating machine-generated instruction datasets for fine-tuning medical-specialized language models using MIMIC-IV discharge records. The study created a large-scale text dataset comprising instructions, cropped discharge notes as inputs, and outputs in JSONL format. The dataset was generated through three main stages, generating instruction and output using seed tasks provided by medical experts, followed by invalid data filtering. The generated dataset consisted of 51,385 sets, with mean ROUGE between seed tasks of 0.185. Evaluation of the generated dataset were promising, with high validity rates determined by both GPT-3.5 and a human annotator (88.0% and 88.5% respectively). The study highlights the potential of automating dataset creation for NLP tasks in the medical domain.

Cohort Builder: A Software Pipeline for Generating Patient Cohorts with Predetermined Baseline Characteristics from Medical Records and Raw Ophthalmic Imaging Data.

In clinical research, the analysis of patient cohorts is a widely employed method for investigating relevant healthcare questions. The ability to automatically extract large-scale patient cohorts from hospital systems is vital in order to unlock the potential of real-world clinical data, and answer pivotal medical questions through retrospective research studies. However, existing medical data is often dispersed across various systems and databases, preventing a systematic approach to access and interoperability. Even when the data are readily accessible, clinical researchers need to sift through Electronic Medical Records, confirm ethical approval, verify status of patient consent, check the availability of imaging data, and filter the data based on disease-specific image biomarkers. We present Cohort Builder, a software pipeline designed to facilitate the creation of patient cohorts with predefined baseline characteristics from real-world ophthalmic imaging data and electronic medical records. The applicability of our approach extends beyond ophthalmology to other medical domains with similar requirements such as neurology, cardiology and orthopedics.

Exploring Explainable AI Techniques for Text Classification in Healthcare: A Scoping Review.

Text classification plays an essential role in the medical domain by organizing and categorizing vast amounts of textual data through machine learning (ML) and deep learning (DL). The adoption of Artificial Intelligence (AI) technologies in healthcare has raised concerns about the interpretability of AI models, often perceived as "black boxes." Explainable AI (XAI) techniques aim to mitigate this issue by elucidating AI model decision-making process. In this paper, we present a scoping review exploring the application of different XAI techniques in medical text classification, identifying two main types: model-specific and model-agnostic methods. Despite some positive feedback from developers, formal evaluations with medical end users of these techniques remain limited. The review highlights the necessity for further research in XAI to enhance trust and transparency in AI-driven decision-making processes in healthcare.