This study explores efficient algorithms to enhance user matching in Unrare.me, a novel social networking platform designed to connect individuals affected by rare diseases. Our primary objective is to develop a recommender system that identifies and suggests users with similar medical conditions, facilitating meaningful connections within these unique communities. Utilizing textual user profile data, we train sentence embedder models to generate similar embeddings for users that have rated each other high. We investigate various fine-tuning strategies, as well as a hybrid approach between a dense embedder and sparse SPLADE embeddings. Furthermore, we investigate the efficacy of various clustering algorithms, such as TopicBERT for thematic analysis, K-Means for centroid-based grouping, and Latent Dirichlet Allocation (LDA) for probabilistic topic modeling, to reduce the matching complexity and enable better scalability of the platform.

We present RepLLaMA, a neural ranking model for optimizing patient matching in rare disease communities. Using data from Unrare.me consisting of over two thousand profiles and over ten thousand ratings, our bi-encoder architecture maps profiles to 4096-dimensional vectors, enabling efficient similarity computations. The system processes unstructured symptom descriptions and structured responses, incorporating expert-guided LLM enhancements. Results show Top-10 Recall of 49.36%$(\pm 2.03)$, surpassing baselines while maintaining generalization. The implementation provides a scalable solution for rare disease patient matching, addressing computational complexity challenges.

With over 7,000 known rare diseases and a prevalence of less than one in a thousand, rare diseases pose substantial challenges to advanced medical support networks. This study investigates the efficacy of Unrare.me, a novel social networking platform designed for individuals affected by rare diseases, including patients, their family members, and medical professionals, addressing data sparsity and combinatorial complexities in user matching. We demonstrate that simple matching heuristics already serve as a decent basis for collecting user feedback on match quality. Leveraging over 10,000 user matching feedback scores from more than 2,000 active users, we evaluate algorithms including collaborative filtering and user embedding similarity with state-of-the-art Large Language Models (LLMs). With a top-10 and top-5 hit-rate of 55% and 37%, respectively, we show that a combination of medical data augmentation and embeddings significantly enhances performance beyond the initial heuristic baseline.

Table structure recognition (TSR), the task of inferring the layout of tables, including the row, column, and cell structure, is a surprisingly complex task. With the growing amount and importance of digital documents, it has become an increasingly relevant problem, which nonetheless has not yet been solved adequately and still presents a very active area of research. In recent years, a growing number of deep-learning-based approaches to table parsing have been proposed.This paper presents a novel deep-learning-based table structure recognition method that can predict row, column, and cell bounds for table images with a high degree of accuracy. To achieve this goal, a multi-stage pipeline incorporating a Vision-Transformer-based Autoencoder model was devised. This model was trained to predict cell regions for table images, from which accurate cell bounds can be inferred, including spanning cells which cover multiple rows or columns. The goal was to obtain a model that generalizes well and can return accurate predictions on various tables of differing complexity, even if they contain little initial structural information.An additional modification to the model architecture presented in the Masked Autoencoder (MAE) approach was also evaluated.

One-vs-Rest (OVR) classification aims to distinguish a single class of interest from other classes. The concept of novelty detection and robustness to dataset shift becomes crucial in OVR when the scope of the rest class extends from the classes observed during training to unseen and possibly unrelated classes. In this work, we propose a novel architecture, namely Decoupling Autoencoder (DAE) to tackle the common issue of robustness w.r.t. out-of-distribution samples which is prevalent in classifiers such as multi-layer perceptrons (MLP) and ensemble architectures. Experiments on plain classification, outlier detection, and dataset shift tasks show DAE to achieve robust performance across these tasks compared to the baselines, which tend to fail completely, when exposed to dataset shift. While DAE and the baselines yield rather uncalibrated predictions on the outlier detection and dataset shift task, we found that DAE calibration is more stable across all tasks. Therefore, calibration measures applied to the classification task could also improve the calibration of the outlier detection and dataset shift scenarios for DAE.

We present the Automated List Inspection (ALI) tool that utilizes methods from machine learning, natural language processing, combined with domain expert knowledge to automate financial statement auditing. ALI is a content based context-aware recommender system, that matches relevant text passages from the notes to the financial statement to specific law regulations. In this paper, we present the architecture of the recommender tool which includes text mining, language modeling, unsupervised and supervised methods that range from binary classification models to deep recurrent neural networks. Next to our main findings, we present quantitative and qualitative comparisons of the algorithms as well as concepts for how to further extend the functionality of the tool.