A Vector Quantization-Based Spike Compression Approach Dedicated to Multichannel Neural Recording Microsystems.

Implantable high-density multichannel neural recording microsystems provide simultaneous recording of brain activities. Wireless transmission of the entire recorded data causes high bandwidth usage, which is not tolerable for implantable applications. As a result, a hardware-friendly compression module is required to reduce the amount of data before it is transmitted. This paper presents a novel compression approach that utilizes a spike extractor and a vector quantization (VQ)-based spike compressor. In this approach, extracted spikes are vector quantized using an unsupervised learning process providing a high spike compression ratio (CR) of 10-80. A combination of extracting and compressing neural spikes results in a significant data reduction as well as preserving the spike waveshapes. The compression performance of the proposed approach was evaluated under variant conditions. We also developed new architectures such that the hardware blocks of our approach can be implemented more efficiently. The compression module was implemented in a 180-nm standard CMOS process achieving a SNDR of 14.49[Formula: see text]dB and a classification accuracy (CA) of 99.62% at a CR of 20, while consuming 4[Formula: see text][Formula: see text]W power and 0.16[Formula: see text]mm2 chip area per channel.

Machine Learning of Patient Characteristics to Predict Admission Outcomes in the Undiagnosed Diseases Network.

Importance: The Undiagnosed Diseases Network (UDN) is a national network that evaluates individual patients whose signs and symptoms have been refractory to diagnosis. Providing reliable estimates of admission outcomes may assist clinical evaluators to distinguish, prioritize, and accelerate admission to the UDN for patients with undiagnosed diseases. Objective: To develop computational models that effectively predict admission outcomes for applicants seeking UDN evaluation and to rank the applications based on the likelihood of patient admission to the UDN. Design, Setting, and Participants: This prognostic study included all applications submitted to the UDN from July 2014 to June 2019, with 1209 applications accepted and 1212 applications not accepted. The main inclusion criterion was an undiagnosed condition despite thorough evaluation by a health care professional; the main exclusion criteria were a diagnosis that explained the objective findings or a review of the records that suggested a diagnosis. A classifier was trained using information extracted from application forms, referral letters from health care professionals, and semantic similarity between referral letters and textual description of known mendelian disorders. The admission labels were provided by the case review committee of the UDN. In addition to retrospective analysis, the classifier was prospectively tested on another 288 applications that were not evaluated at the time of classifier development. Main Outcomes and Measures: The primary outcomes were whether a patient was accepted or not accepted to the UDN and application order ranked based on likelihood of admission. The performance of the classifier was assessed by comparing its predictions against the UDN admission outcomes and by measuring improvement in the mean processing time for accepted applications. Results: The best classifier obtained sensitivity of 0.843, specificity of 0.738, and area under the receiver operating characteristic curve of 0.844 for predicting admission outcomes among 1212 accepted and 1210 not accepted applications. In addition, the classifier can decrease the current mean (SD) UDN processing time for accepted applications from 3.29 (3.17) months to 1.05 (3.82) months (68% improvement) by ordering applications based on their likelihood of acceptance. Conclusions and Relevance: A classification system was developed that may assist clinical evaluators to distinguish, prioritize, and accelerate admission to the UDN for patients with undiagnosed diseases. Accelerating the admission process may improve the diagnostic journeys for these patients and serve as a model for partial automation of triaging or referral for other resource-constrained applications. Such classification models make explicit some of the considerations that currently inform the use of whole-genome sequencing for undiagnosed disease and thereby invite a broader discussion in the clinical genetics community.

Online Searching and Social Media to Detect Alcohol Use Risk at Population Scale.

INTRODUCTION: Harnessing engagement in online searching and social media may provide complementary information for monitoring alcohol use, informing prevention and policy evaluation, and extending knowledge available from national surveys. METHODS: Relative search volumes for 7 alcohol-related keywords were estimated from Google Trends (data, 2014-2017), and the proportion of alcohol use-related Twitter posts (data, 2014-2015) was estimated using natural language processing. Searching/posting measures were created for all 50 U.S. states plus Washington, D.C. Survey reports of alcohol use and summaries of state alcohol policies were obtained from the Behavioral Risk Factor Surveillance System (data, 2014-2016) and the Alcohol Policy Scale. In 2018-2019, associations among searching/posting measures and same state/year Behavioral Risk Factor Surveillance System reports of recent (past-30-day) alcohol use and maximum number of drinks consumed on an occasion were estimated using logistic and linear regression, adjusting for sociodemographics and Internet use, with moderation tested in regressions that included interactions of select searching/posting measures and the Alcohol Policy Scale. RESULTS: Recent alcohol use was reported by 52.93% of 1,297,168 Behavioral Risk Factor Surveillance System respondents, which was associated with all state-level searching/posting measures in unadjusted and adjusted models (p<0.0001). Among drinkers, most searching/posting measures were associated with maximum number of drinks consumed (p<0.0001). Associations varied with exposure to high versus low levels of state policy controls on alcohol. CONCLUSIONS: Strong associations were found among individual alcohol use and state-level alcohol-related searching/posting measures, which were moderated by the strength of state alcohol policies. Findings support using novel personally generated data to monitor alcohol use and possibly evaluate effects of alcohol control policies.