Processing of Short-Form Content in Clinical Narratives: Systematic Scoping Review.

Background: Clinical narratives are essential components of electronic health records. The adoption of electronic health records has increased documentation time for hospital staff, leading to the use of abbreviations and acronyms more frequently. This brevity can potentially hinder comprehension for both professionals and patients.

De-Identifying GRASCCO - A Pilot Study for the De-Identification of the German Medical Text Project (GeMTeX) Corpus.

Introduction: The German Medical Text Project (GeMTeX) is one of the largest infrastructure efforts targeting German-language clinical documents. We here introduce the architecture of the de-identification pipeline of GeMTeX.

Methods: This pipeline comprises the export of raw clinical documents from the local hospital information system, the import into the annotation platform INCEpTION, fully automatic pre-tagging with protected health information (PHI) items by the Averbis Health Discovery pipeline, a manual curation step of these pre-annotated data, and, finally, the automatic replacement of PHI items with type-conformant substitutes.

Influence of Context in Transformer-Based Medication Relation Extraction.

The extraction of medication information from unstructured clinical documents has been a major application of clinical NLP in the past decade as evidenced by the conduct of two shared tasks under the I2B2 and N2C2 umbrella. We here propose a new methodological approach which has already shown a tremendous potential for increasing system performance for general NLP tasks, but has so far not been applied to medication extraction from EHR data, namely deep learning based on transformer models. We ran experiments on established clinical data sets for English (exploiting I2B2 and N2C2 corpora) and German (based on the 3000PA corpus, a German reference data set).

Final Report on the German Clinical Reference Corpus 3000PA.

We here report on one of the outcomes of a large-scale German research program, the Medical Informatics Initiative (MII), aiming at the development of a solid data and software infrastructure for German-language clinical natural language processing. Within this framework, we have developed 3000PA, a national clinical reference corpus composed of patient records from three clinical university sites and annotated with a multitude of semantic annotation layers (including medical named entities, semantic and temporal relations between entities, as well as certainty and negation information related to entities and relations). This non-sharable corpus has been complemented by three sharable ones (JSYNCC, GGPONC, and GRASCCO).

Announcement of the German Medical Text Corpus Project (GeMTeX).

The largest publicly funded project to generate a German-language medical text corpus will start in mid-2023. GeMTeX comprises clinical texts from information systems of six university hospitals, which will be made accessible for NLP by annotation of entities and relations, which will be enhanced with additional meta-information. A strong governance provides a stable legal framework for the use of the corpus.

GRASCCO - The First Publicly Shareable, Multiply-Alienated German Clinical Text Corpus.

We describe the creation of GRASCCO, a novel German-language corpus composed of some 60 clinical documents with more than.43,000 tokens. GRASCCO is a synthetic corpus resulting from a series of alienation steps to obfuscate privacy-sensitive information contained in real clinical documents, the true origin of all GRASCCO texts.

Optimized Identification of Advanced Chronic Kidney Disease and Absence of Kidney Disease by Combining Different Electronic Health Data Resources and by Applying Machine Learning Strategies.

Automated identification of advanced chronic kidney disease (CKD ≥ III) and of no known kidney disease (NKD) can support both clinicians and researchers. We hypothesized that identification of CKD and NKD can be improved, by combining information from different electronic health record (EHR) resources, comprising laboratory values, discharge summaries and ICD-10 billing codes, compared to using each component alone. We included EHRs from 785 elderly multimorbid patients, hospitalized between 2010 and 2015, that were divided into a training and a test (n = 156) dataset.

An Evolutionary Approach to the Annotation of Discharge Summaries.

We here describe the evolution of annotation guidelines for major clinical named entities, namely Diagnosis, Findings and Symptoms, on a corpus of approximately 1,000 German discharge letters. Due to their intrinsic opaqueness and complexity, clinical annotation tasks require continuous guideline tuning, beginning from the initial definition of crucial entities and the subsequent iterative evolution of guidelines based on empirical evidence. We describe rationales for adaptation, with focus on several metrical criteria and task-centered clinical constraints.

Automated objective and marker-free facial grading using photographs of patients with facial palsy.

Purpose: An automated, objective, fast and simple classification system for the grading of facial palsy (FP) is lacking.

Methods: An observational single center study was performed. 4572 photographs of 233 patients with unilateral peripheral FP were subjectively rated and automatically analyzed applying a machine learning approach including Supervised Descent Method.

Annotating German Clinical Documents for De-Identification.

We devised annotation guidelines for the de-identification of German clinical documents and assembled a corpus of 1,106 discharge summaries and transfer letters with 44K annotated protected health information (PHI) items. After three iteration rounds, our annotation team finally reached an inter-annotator agreement of 0.96 on the instance level and 0.

CDA-Compliant Section Annotation of German-Language Discharge Summaries: Guideline Development, Annotation Campaign, Section Classification.

We present the outcome of an annotation effort targeting the content-sensitive segmentation of German clinical reports into sections. We recruited an annotation team of up to eight medical students to annotate a clinical text corpus on a sentence-by-sentence basis in four pre-annotation iterations and one final main annotation step. The annotation scheme we came up with adheres to categories developed for clinical documents in the HL7-CDA (Clinical Document Architecture) standard for section headings.

Reliability of grading of facial palsy using a video tutorial with synchronous video recording.

Objective: To determine the intrarater, interrater, and retest reliability of facial nerve grading of patients with facial palsy (FP) using standardized videos recorded synchronously during a self-explanatory patient video tutorial.

Study Design: Prospective, observational study.

Methods: The automated videos from 10 patients with varying degrees of FP (5 acute, 5 chronic FP) and videos without tutorial from eight patients (all chronic FP) were rated by five novices and five experts according to the House-Brackmann grading system (HB), the Sunnybrook Grading System (SB), and the Facial Nerve Grading System 2.

Facial motor and non-motor disabilities in patients with central facial paresis: a prospective cohort study.

Although central facial paresis (CFP) is a major symptom of stroke, there is a lack of studies on the motor and non-motor disabilities in stroke patients. A prospective cohort study was performed at admission for inpatient rehabilitation and discharge of post-stroke phase of 112 patients (44% female, median age: 64 years, median Barthel index: 70) with CFP. Motor function was evaluated using House-Brackmann grading, Sunnybrook grading and Stennert Index.

[Video Instruction for Synchronous Video Recording of Mimic Movement of Patients with Facial Palsy].

Photografy and video are necessary to record the severity of a facial palsy or to allow offline grading with a grading system. There is no international standard for the video recording urgently needed to allow a standardized comparison of different patient cohorts. A video instruction was developed.