Unsupervised SapBERT-based bi-encoders for medical concept annotation of clinical narratives with SNOMED CT.

Objective: Clinical narratives provide comprehensive patient information. Achieving interoperability involves mapping relevant details to standardized medical vocabularies. Typically, natural language processing divides this task into named entity recognition (NER) and medical concept normalization (MCN).

Prospectively investigating the impact of AI onshared decision-making in post kidney transplant care (PRIMA-AI): protocol for a longitudinal qualitative study among patients, their support persons and treating physicians at a tertiary care centre.

Introduction: As healthcare is shifting from a paternalistic to a patient-centred approach, medical decision making becomes more collaborative involving patients, their support persons (SPs) and physicians. Implementing shared decision-making (SDM) into clinical practice can be challenging and becomes even more complex with the introduction of artificial intelligence (AI) as a potential actant in the communicative network. Although there is more empirical research on patients' and physicians' perceptions of AI, little is known about the impact of AI on SDM.

Investigating the Impact of AI on Shared Decision-Making in Post-Kidney Transplant Care (PRIMA-AI): Protocol for a Randomized Controlled Trial.

Background: Patients after kidney transplantation eventually face the risk of graft loss with the concomitant need for dialysis or retransplantation. Choosing the right kidney replacement therapy after graft loss is an important preference-sensitive decision for kidney transplant recipients. However, the rate of conversations about treatment options after kidney graft loss has been shown to be as low as 13% in previous studies.

Concordance of a decision algorithm and multidisciplinary team meetings for patients with liver cancer-a study protocol for a randomized controlled trial.

Introduction: Multidisciplinary team meetings (MDMs), also known as tumor conferences, are a cornerstone of cancer treatments. However, barriers such as incomplete patient information or logistical challenges can postpone tumor board decisions and delay patient treatment, potentially affecting clinical outcomes. Therapeutic Assistance and Decision algorithms for hepatobiliary tumor Boards (ADBoard) aims to reduce this delay by providing automated data extraction and high-quality, evidence-based treatment recommendations.

SapBERT-Based Medical Concept Normalization Using SNOMED CT.

Word vector representations, known as embeddings, are commonly used for natural language processing. Particularly, contextualized representations have been very successful recently. In this work, we analyze the impact of contextualized and non-contextualized embeddings for medical concept normalization, mapping clinical terms via a k-NN approach to SNOMED CT.

When performance is not enough-A multidisciplinary view on clinical decision support.

Scientific publications about the application of machine learning models in healthcare often focus on improving performance metrics. However, beyond often short-lived improvements, many additional aspects need to be taken into consideration to make sustainable progress. What does it take to implement a clinical decision support system, what makes it usable for the domain experts, and what brings it eventually into practical usage? So far, there has been little research to answer these questions.

Automatic Extraction of Medication Mentions from Tweets-Overview of the BioCreative VII Shared Task 3 Competition.

This study presents the outcomes of the shared task competition BioCreative VII (Task 3) focusing on the extraction of medication names from a Twitter user's publicly available tweets (the user's 'timeline'). In general, detecting health-related tweets is notoriously challenging for natural language processing tools. The main challenge, aside from the informality of the language used, is that people tweet about any and all topics, and most of their tweets are not related to health.

"Nothing works without the doctor:" Physicians' perception of clinical decision-making and artificial intelligence.

Introduction: Artificial intelligence-driven decision support systems (AI-DSS) have the potential to help physicians analyze data and facilitate the search for a correct diagnosis or suitable intervention. The potential of such systems is often emphasized. However, implementation in clinical practice deserves continuous attention.

Evaluation of a clinical decision support system for detection of patients at risk after kidney transplantation.

Patient care after kidney transplantation requires integration of complex information to make informed decisions on risk constellations. Many machine learning models have been developed for detecting patient outcomes in the past years. However, performance metrics alone do not determine practical utility.

EffiCare: Better Prognostic Models via Resource-Efficient Health Embeddings.

Recent medical prognostic models adapted from high data-resource fields like language processing have quickly grown in complexity and size. However, since medical data typically constitute low data-resource settings, performances on tasks like clinical prediction did not improve expectedly. Instead of following this trend of using complex neural models in combination with small, pre-selected feature sets, we propose EffiCare, which focuses on minimizing hospital resource requirements for assistive clinical prediction models.

eHealth in transplantation.

eHealth ("electronic" Health) is a new field in medicine that has the potential to change medical care, increase efficiency, and reduce costs. In this review, we analyzed the current status of eHealth in transplantation by performing a PubMed search over the last 5 years with a focus on clinical studies for post-transplant care. We retrieved 463 manuscripts, of which 52 clinical reports and eight randomized controlled trials were identified.

[Digital Nephrology].

Clinical Nephrology faces considerable structural challenges. Digitization can be a critical catalyst to better address the needs of patients and their healthcare providers with new system solutions. Improved communication is the key driver.

SIA: a scalable interoperable annotation server for biomedical named entities.

Recent years showed a strong increase in biomedical sciences and an inherent increase in publication volume. Extraction of specific information from these sources requires highly sophisticated text mining and information extraction tools. However, the integration of freely available tools into customized workflows is often cumbersome and difficult.