Development of an intelligent decision support system for ischemic stroke risk assessment in a population-based electronic health record database.

BACKGROUND: Intelligent decision support systems (IDSS) have been applied to tasks of disease management. Deep neural networks (DNNs) are artificial intelligent techniques to achieve high modeling power. The application of DNNs to large-scale data for estimating stroke risk needs to be assessed and validated. This study aims to apply a DNN for deriving a stroke predictive model using a big electronic health record database. METHODS AND RESULTS: The Taiwan National Health Insurance Research Database was used to conduct a retrospective population-based study. The database was divided into one development dataset for model training (~70% of total patients for training and ~10% for parameter tuning) and two testing datasets (each ~10%). A total of 11,192,916 claim records from 840,487 patients were used. The primary outcome was defined as any ischemic stroke in inpatient records within 3 years after study enrollment. The DNN was evaluated using the area under the receiver operating characteristic curve (AUC or c-statistic). The development dataset included 672,214 patients (a total of 8,952,000 records) of whom 2,060 patients had stroke events. The mean age of the population was 35.5±20.2 years, with 48.5% men. The model achieved AUC values of 0.920 (95% confidence interval [CI], 0.908-0.932) in testing dataset 1 and 0.925 (95% CI, 0.914-0.937) in testing dataset 2. Under a high sensitivity operating point, the sensitivity and specificity were 92.5% and 79.8% for testing dataset 1; 91.8% and 79.9% for testing dataset 2. Under a high specificity operating point, the sensitivity and specificity were 80.3% and 87.5% for testing dataset 1; 83.7% and 87.5% for testing dataset 2. The DNN model maintained high predictability 5 years after being developed. The model achieved similar performance to other clinical risk assessment scores. CONCLUSIONS: Using a DNN algorithm on this large electronic health record database is capable of obtaining a high performing model for assessment of ischemic stroke risk. Further research is needed to determine whether such a DNN-based IDSS could lead to an improvement in clinical practice.

Improving Automatic Tremor and Movement Motor Disorder Severity Assessment for Parkinson's Disease with Deep Joint Training.

Parkinson's disease (PD) is one of the most severe and common disease globally. PD induces motor system impairment causing symptoms such as shaking, rigidity, slowness of movement, body tremor and difficulty with walking. Clinically, accurately and objectively assessing the severity of PD symptoms is critical in controlling appropriate dosage of Levodopa to prevent unwanted side effect of switching between Dyskinesia and PD. The unified Parkinson's disease rating scale published by the Movement Disorder Society (MDS-UPDRS) is an validated instrument regularly administrated by trained physician to assess the severity of a PD patient's motor disorder. In this work, we aim at advancing vision-based automatic motor disorder assessment, specifically hand tremor and movement, for PD patients during UPDRS. Our proposed method leverages information across the two behavior tasks simultaneously via deep joint training to improve each single task's, i.e., tremor and movement, severity classification rate. We evaluate our framework on a large cohort of 106 PD patients, and with our proposed deep joint training framework, we achieve accuracy of 78.01% and 80.60% in right and left hand movement binary classification; in terms of tremor severity classification, our approach obtains an enhanced recognition rates of 72.20% and 71.10% for right and left hand respectively.

Development of a rapid and economic in vivo electrocardiogram platform for cardiovascular drug assay and electrophysiology research in adult zebrafish.

Zebrafish is a popular and favorable model organism for cardiovascular research, with an increasing number of studies implementing functional assays in the adult stage. For example, the application of electrocardiography (ECG) in adult zebrafish has emerged as an important tool for cardiac pathophysiology, toxicity, and chemical screen studies. However, few laboratories are able to perform such functional analyses due to the high cost and limited availability of a convenient in vivo ECG recording system. In this study, an inexpensive ECG recording platform and operation protocol that has been optimized for adult zebrafish ECG research was developed. The core hardware includes integration of a ready-to-use portable ECG kit with a set of custom-made needle electrode probes. A combined anesthetic formula of MS-222 and isoflurane was first tested to determine the optimal assay conditions to minimize the interference to zebrafish cardiac physiology under sedation. For demonstration, we treated wild-type zebrafish with different pharmacological agents known to affect cardiac rhythms in humans. Conserved electrophysiological responses to these drugs were induced in adult zebrafish and recorded in real time. This economic ECG platform has the potential to facilitate teaching and training in cardiac electrophysiology with adult zebrafish and to promote future translational applications in cardiovascular medicine.

The psychologist as an interlocutor in autism spectrum disorder assessment: insights from a study of spontaneous prosody.

PURPOSE: The purpose of this study was to examine relationships between prosodic speech cues and autism spectrum disorder (ASD) severity, hypothesizing a mutually interactive relationship between the speech characteristics of the psychologist and the child. The authors objectively quantified acoustic-prosodic cues of the psychologist and of the child with ASD during spontaneous interaction, establishing a methodology for future large-sample analysis. METHOD: Speech acoustic-prosodic features were semiautomatically derived from segments of semistructured interviews (Autism Diagnostic Observation Schedule, ADOS; Lord, Rutter, DiLavore, & Risi, 1999; Lord et al., 2012) with 28 children who had previously been diagnosed with ASD. Prosody was quantified in terms of intonation, volume, rate, and voice quality. Research hypotheses were tested via correlation as well as hierarchical and predictive regression between ADOS severity and prosodic cues. RESULTS: Automatically extracted speech features demonstrated prosodic characteristics of dyadic interactions. As rated ASD severity increased, both the psychologist and the child demonstrated effects for turn-end pitch slope, and both spoke with atypical voice quality. The psychologist's acoustic cues predicted the child's symptom severity better than did the child's acoustic cues. CONCLUSION: The psychologist, acting as evaluator and interlocutor, was shown to adjust his or her behavior in predictable ways based on the child's social-communicative impairments. The results support future study of speech prosody of both interaction partners during spontaneous conversation, while using automatic computational methods that allow for scalable analysis on much larger corpora.