C-19

C-19

#NB
Repurpose.AI | A Disease Agnostic Drug Discovery Company
Repurpose.AI | A Disease Agnostic Drug Discovery Company
Transforming drug discovery by leveraging historical drug development data and unbiased AI to create Phase II/III ready drug candidates within only days.
·repurpose.ai·
Repurpose.AI | A Disease Agnostic Drug Discovery Company
Digital Health Hype Cycle 2020
Digital Health Hype Cycle 2020
For the fourth year in a row I would like to share my thoughts on what I see as the cutting edge, emerging, developing and mature technologies f
·healthcare.digital·
Digital Health Hype Cycle 2020
MessageBird launches an ‘omni-channel’ Intercom competitor
MessageBird launches an ‘omni-channel’ Intercom competitor
Following the launch of Inbox.ai in March — its “Slack for external communications” — Amsterdam-headquartered MessageBird is continuing to place big bets on a messaging-first future. Not content with building a platform for customer service teams that lets them communicate with customers on a channel of their choosing, including all of the most popular messaging […]
·techcrunch.com·
MessageBird launches an ‘omni-channel’ Intercom competitor
Can a decision support system accelerate rare disease diagnosis? Evaluating the potential impact of Ada DX in a retrospective study
Can a decision support system accelerate rare disease diagnosis? Evaluating the potential impact of Ada DX in a retrospective study
Rare disease diagnosis is often delayed by years. A primary factor for this delay is a lack of knowledge and awareness regarding rare diseases. Probabilistic diagnostic decision support systems (DDSSs) have the potential to accelerate rare disease diagnosis by suggesting differential diagnoses for physicians based on case input and incorporated medical knowledge. We examine the DDSS prototype Ada DX and assess its potential to provide accurate rare disease suggestions early in the course of rare disease cases. Ada DX suggested the correct disease earlier than the time of clinical diagnosis among the top five fit disease suggestions in 53.8% of cases (50 of 93), and as the top fit disease suggestion in 37.6% of cases (35 of 93). The median advantage of correct disease suggestions compared to the time of clinical diagnosis was 3 months or 50% for top five fit and 1 month or 21% for top fit. The correct diagnosis was suggested at the first documented patient visit in 33.3% (top 5 fit), and 16.1% of cases (top fit), respectively. Wilcoxon signed-rank test shows a significant difference between the time to clinical diagnosis and the time to correct disease suggestion for both top five fit and top fit (z-score -6.68, respective -5.71, α=0.05, p-value <0.001). Ada DX provided accurate rare disease suggestions in most rare disease cases. In many cases, Ada DX provided correct rare disease suggestions early in the course of the disease, sometimes at the very beginning of a patient journey. The interpretation of these results indicates that Ada DX has the potential to suggest rare diseases to physicians early in the course of a case. Limitations of this study derive from its retrospective and unblinded design, data input by a single user, and the optimization of the knowledge base during the course of the study. Results pertaining to the system’s accuracy should be interpreted cautiously. Whether the use of Ada DX reduces the time to diagnosis in rare diseases in a clinical setting should be validated in prospective studies.
·ojrd.biomedcentral.com·
Can a decision support system accelerate rare disease diagnosis? Evaluating the potential impact of Ada DX in a retrospective study
CSTR-Edinburgh/merlin
CSTR-Edinburgh/merlin
This is now the official location of the Merlin project. - CSTR-Edinburgh/merlin
·github.com·
CSTR-Edinburgh/merlin
A Framework for Biomarkers of COVID-19 Based on Coordination of Speech-Production Subsystems
A Framework for Biomarkers of COVID-19 Based on Coordination of Speech-Production Subsystems
Goal: We propose a speech modeling and signal-processing framework to detect and track COVID-19 through asymptomatic and symptomatic stages. Methods: The approach is based on complexity of neuromotor coordination across speech subsystems involved in respiration, phonation and articulation, motivated by the distinct nature of COVID-19 involving lower (i.e., bronchial, diaphragm, lower tracheal) versus upper (i.e., laryngeal, pharyngeal, oral and nasal) respiratory tract inflammation, as well as by the growing evidence of the virus’ neurological manifestations. Preliminary results: An exploratory study with audio interviews of five subjects provides Cohen's d effect sizes between pre-COVID-19 (pre-exposure) and post-COVID-19 (after positive diagnosis but presumed asymptomatic) using: coordination of respiration (as measured through acoustic waveform amplitude) and laryngeal motion (fundamental frequency and cepstral peak prominence), and coordination of laryngeal and articulatory (formant center frequencies) motion. Conclusions: While there is a strong subject-dependence, the group-level morphology of effect sizes indicates a reduced complexity of subsystem coordination. Validation is needed with larger more controlled datasets and to address confounding influences such as different recording conditions, unbalanced data quantities, and changes in underlying vocal status from pre-to-post time recordings.
·ieeexplore.ieee.org·
A Framework for Biomarkers of COVID-19 Based on Coordination of Speech-Production Subsystems
AI Face Recognition Animation
AI Face Recognition Animation
Hello everyone!👋 I recently tried to create a graphic animation using Lottie. I designed a dynamic Hero Page, used AE to create an animation to output a json file, and then created a web page via lottie.You can check the effect on the web by visitthis link 🔗(https://seergb.com/demo/lottie-ai-face/). visit video demo 🔗(https://dribbble.com/shots/7001003-AI-Face-Recognition-Animation)
·uplabs.com·
AI Face Recognition Animation
Coronavirus (COVID-19) Testing - Statistics and Research
Coronavirus (COVID-19) Testing - Statistics and Research
No country knows the total number of people infected with COVID-19. All we know is the infection status of those who have been tested. All those who have a lab-confirmed infection are counted as confirmed cases.
·ourworldindata.org·
Coronavirus (COVID-19) Testing - Statistics and Research
Aleph
Aleph
Aleph is a venture capital fund focused on partnering with great Israeli entrepreneurs to build large, meaningful companies and impactful global brands. It is an Equal Partnership of Eden Shochat, Michael Eisenberg and Aaron Rosenson. Visit Jobs.aleph.vc.
·aleph.vc·
Aleph