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. This allowed an automated grading of all photographs according to House-Brackmann grading scale (HB), Sunnybrook grading system (SB), and Stennert index (SI). RESULTS: Median time to first assessment was 6 days after onset. At first examination, the median objective HB, total SB, and total SI were grade 3, 45, and 5, respectively. The best correlation between subjective and objective grading was seen for SB and SI movement score (r = 0.746; r = 0.732, respectively). No agreement was found between subjective and objective HB grading [Test for symmetry 80.61, df = 15, p < 0.001, weighted kappa = - 0.0105; 95% confidence interval (CI) = - 0.0542 to 0.0331; p = 0.6541]. Also no agreement was found between subjective and objective total SI (test for symmetry 166.37, df = 55, p < 0.001) although there was a nonzero weighted kappa = 0.2670; CI 0.2154-0.3186; p < 0.0001). Based on a multinomial logistic regression the probability for higher scores was higher for subjective compared to objective SI (OR 1.608; CI 1.202-2.150; p = 0.0014). The best agreement was seen between subjective and objective SB (ICC = 0.34645). CONCLUSIONS: Automated Sunnybrook grading delivered with fair agreement fast and objective global and regional data on facial motor function for use in clinical routine and clinical trials.

[Video Instruction for Synchronous Video Recording of Mimic Movement of Patients with Facial Palsy]. / Patienten-Instruktionsvideo mit synchroner Videoaufnahme von Gesichtsbewegungen bei Fazialisparese.

Background 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. Methods A video instruction was developed. The instruction was shown to the patient and presents several mimic movements. At the same time the patient is recorded while repeating the presented movement using commercial hardware. Facial movements were selected in such a way that it was afterwards possible to evaluate the recordings with standard grading systems (House-Brackmann, Sunnybrook, Stennert, Yanagihara) or even with (semi)automatic software. For quality control, the patients evaluated the instruction using a questionnaire. Results The video instruction takes 11 min and 05 and is divided in 3 parts: 1) Explanation of the procedure; 2) Foreplay and recreating of the facial movements; 3) Repeating of sentences to analyze the communication skills. So far 13 healthy subjects and 10 patients with acute or chronic facial palsy were recorded. All recordings could be assessed by the above mentioned grading systems. The instruction was rated as well explaining and easy to follow by healthy persons and patients. Discussion There is now a video instruction available for standardized recording of facial movement. This instruction is recommended for use in clinical routine and in clinical trials. This will allow a standardized comparison of patients within Germany and international patient cohorts.

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). Our results reveal that transformer models are on a par with current state-of-the-art results for English, but yield new ones for German data. We further address the influence of context on the overall performance of transformer-based medication relation extraction.

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). Overall, 3000PA, JSYNCC and GRASCCO feature about 2.1 million metadata points.

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. State-of-the art NLP methods are used to build, pre-annotate and annotate the corpus and train language models. A community will be built around GeMTeX to ensure its sustainable maintenance, use, and dissemination.

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. Therefore, it is publicly shareable without any legal restrictions We also explore whether this corpus still represents common clinical language use by comparison with a real (non-shareable) clinical corpus we developed as a contribution to the Medical Informatics Initiative in Germany (MII) within the SMITH consortium. We find evidence that such a claim can indeed be made.

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.

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. We used both the area under the receiver operating characteristic (AUROC) and under the precision-recall curve (AUCPR) with a 95% confidence interval for evaluation of different classification models. In the test dataset, the combination of EHR components as a simple classifier identified CKD ≥ III (AUROC 0.96[0.93-0.98]) and NKD (AUROC 0.94[0.91-0.97]) better than laboratory values (AUROC CKD 0.85[0.79-0.90], NKD 0.91[0.87-0.94]), discharge summaries (AUROC CKD 0.87[0.82-0.92], NKD 0.84[0.79-0.89]) or ICD-10 billing codes (AUROC CKD 0.85[0.80-0.91], NKD 0.77[0.72-0.83]) alone. Logistic regression and machine learning models improved recognition of CKD ≥ III compared to the simple classifier if only laboratory values were used (AUROC 0.96[0.92-0.99] vs. 0.86[0.81-0.91], p < 0.05) and improved recognition of NKD if information from previous hospital stays was used (AUROC 0.99[0.98-1.00] vs. 0.95[0.92-0.97]], p < 0.05). Depending on the availability of data, correct automated identification of CKD ≥ III and NKD from EHRs can be improved by generating classification models based on the combination of different EHR components.

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 0.97 on the token level of annotation (averaged pair-wise F1 score). To establish a baseline for automatic de-identification on our corpus, we trained a recurrent neural network (RNN) and achieved F1 scores greater than 0.9 on most major PHI categories.

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. Automated action unit (AU) analysis was performed to analyze mimic function in detail. Non-motor function was assessed using the Facial Disability Index (FDI) and the Facial Clinimetric Evaluation (FaCE). Median interval from stroke to rehabilitation was 21 days. Rehabilitation lasted 20 days. House-Brackmann grading was ≥ grade III for 79% at admission. AU activation in the lower face was significantly lower in patients with right hemispheric infarction compared to left hemispheric infarction (all p < 0.05). Median total FDI and FaCE score were 46.5 and 69, respectively. Facial grading and FDI/FaCE scores improved during inpatient rehabilitation (all p < 0.05). There was a significant increase of the activation of AU12 (Zygomaticus major muscle), AU13 (Levator anguli oris muscle), and AU24 (Orbicularis oris muscle) during inpatient rehabilitation (all p < 0.05). Multivariate analysis revealed that activation of AU10 (Levator labii superioris), AU12, AU17 (Depressor labii), and AU 38 (Nasalis) were independent predictors for better quality of life. These results demonstrate that CFP has a significant impact on patient's quality of life. Therapy of CFP with focus on specific AUs should be part of post-stroke rehabilitation.

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 2.0 (FNGS 2.0). RESULTS: Intrarater reliability for the three grading systems was very high using the automated videos (intraclass correlation coefficient [ICC]; SB: ICC = 0.967; FNGS 2.0: ICC = 0.931; HB: ICC = 0.931). Interrater reliability was also high (SB: ICC = 0.921; FNGS 2.0: ICC = 0.837; HB: ICC = 0.736), but for HB Fleiss kappa (0.214) and Kendell W (0.231) was low. The interrater reliability was not different between novices and experts. Retest reliability was very high (SB: novices ICC = 0.979; experts ICC = 0.964; FNGS 2.0: novices ICC = 0.979; experts ICC = 0.969). The reliability of grading of chronic FP with SB was higher using automated videos with tutorial (ICC = 0.845) than without tutorial (ICC = 0.538). CONCLUSION: The reliability of the grading using the automated videos is excellent, especially for the SB grading. We recommend using this automated video tool regularly in clinical routine and for clinical studies. LEVEL OF EVIDENCE: 4 xsLaryngoscope, 129:2274-2279, 2019.

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. Once the scheme became stable, we ran the main annotation campaign on the complete set of roughly 1,000 clinical documents. Due to its reliance on the CDA standard, the annotation scheme allows the integration of legacy and newly produced clinical documents within a common pipeline. We then made direct use of the annotations by training a baseline classifier to automatically identify sections in clinical reports.