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2,025 results for “AIS”
Using Conversational AI to Teach Growth Mindset Skills to Youths in India
ClinicalTrials.gov study NCT07316647. IPD Sharing: NO. Countries: 1. Publications: 1.
Real-world Treatment Patterns and Effectiveness of Palbociclib and AI Therapy
ClinicalTrials.gov study NCT04176354. IPD Sharing: NO. Countries: 1. Publications: 4.
Effect of Postoperative Prolonged Sedation With Dexmedetomidine After Successful Reperfusion With EVT on Long-term Prognosis in Patients With AIS (PPDET)
ClinicalTrials.gov study NCT04916197. IPD Sharing: YES. Countries: 1. Publications: 1.
AI-Assisted MRE for Intestinal Fibrosis in Crohn's Disease
ClinicalTrials.gov study NCT06858553. IPD Sharing: NO. Countries: 1. Publications: 11.
AI-Powered Fall Risk Prediction in Nursing Care
ClinicalTrials.gov study NCT07000981. IPD Sharing: NO. Countries: 1. Publications: 2.
Artificial Intelligence (AI) Technology May Help Patients to Understand Bowel Preparation Better Before They go for Colonoscopy.This Study Attempts to Leverage AI Chatbot in Counselling Patients to Im
ClinicalTrials.gov study NCT06905782. IPD Sharing: YES. Countries: 1. Publications: 1.
Ai Youmian (Love Better Sleep) for People Living With HIV
ClinicalTrials.gov study NCT05576844. IPD Sharing: NO. Countries: 1. Publications: 8.
Alteplase in Elderly Acute Ischemic Stroke (AIS) Patients During Hospitalization
ClinicalTrials.gov study NCT05395351. IPD Sharing: NO. Countries: 1. Publications: 1.
Temporal Trends of Thrombolysis Treatment in Chinese Acute Ischemic Stroke (AIS) Patients From 2007-2017: Analysis of China National Stroke Registry (CNSR) I, II, and III; CTP-Draft Review Performed;
ClinicalTrials.gov study NCT04290494. IPD Sharing: YES. Countries: 1. Publications: 0.
Eyes don’t lie: Indifferent to AI-generated depression screenings
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The AI Economist: Taxation policy design via two-level deep reinforcement learning
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Satellite images and road-reference data for AI-based road mapping in Equatorial Asia
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Data from: Two complementary AI approaches for predicting UMLS semantic group assignment: heuristic reasoning and deep learning
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Statistical code from: Passive acoustic monitoring with AI-based detection and identification reveal sooty grouse hooting patterns in western Oregon
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PestReKNet-X: Integrating Explainable AI to enhance pest disease detection and combat crop senescence
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Who expands the human creative frontier with generative AI: Hiveminds or masterminds?
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The development dataset of the AI composition recognition competition, CSMT2020
<p>The development dataset contains 6000 MIDI files with monophonic melodies generated by artificial intelligence algorithms. The tempo is between the 68bpm and 118bpm (beat per minute). The length of each melody is 8 bars, and the melody does not necessarily include complete phrase structures. There are two datasets with different music styles used as the training dataset of a certain number of algorithms, where the melodies in the development dataset are generated.</p> <p>The website of the challenge:</p> <p><a href="http://www.csmcw-csmt.cn/data/2020/ai-composition-recognition2020/">http://www.csmcw-csmt.cn/data/2020/ai-composition-recognition2020</a> (Chinese instruction)</p> <p><a href="https://ai-composition-recognition2020.github.io/english.html">https://ai-composition-recognition2020.github.io/english.html</a> (English instruction)</p>
The evaluation dataset of the AI composition recognition competition, CSMT2020
<p>The evaluation dataset contains 4000 MIDI files with exact configurations of development dataset with two exceptions: 1) A number of melodies composed by human composers are added, some of which are published, and some of which are composed for this competition. The music style of the human composed melodies are the same as the styles of music in the training set. This was confirmed by musicologists. 2) There are a number of melodies generated by algorithms with minor algorithmic or parameter changes compared to the algorithms in the development dataset.</p> <p>The development dataset:</p> <p><a href="https://zenodo.org/record/3944685#.Xza9DegzY2x">https://zenodo.org/record/3944685#.Xza9DegzY2x</a></p> <p>The website of the challenge:</p> <p><a href="http://www.csmcw-csmt.cn/data/2020/ai-composition-recognition2020/">http://www.csmcw-csmt.cn/data/2020/ai-composition-recognition2020</a> (Chinese instruction)</p> <p><a href="https://ai-composition-recognition2020.github.io/english.html">https://ai-composition-recognition2020.github.io/english.html</a> (English instruction)</p>
AI-Derived West Nile Virus Determinants Maps - 2018 Europe - Data
<p>Data for AI-Derived West Nile Virus Determinants Maps</p> <p>2018 Europe</p> <ol> <li>Original (nominal value) data</li> <li>SHAP-derived effect data </li> </ol>
Bits x la Marató: Looking for similar patients: the AI Doctor House conquers severe COVID-19!
<p>Clinical case reports for the task Looking for similar patients: the AI Doctor House conquers severe COVID-19! at the event Bits x la Marató: https://www.fib.upc.edu/en/la-marato</p> <p> </p> <p>There is a pressing need by healthcare professionals to access information relevant to clinical practice in a more effective way. Over 80% of clinically relevant data is essentially unstructured, mainly images like MRI and clinical texts.</p> <p>One of the challenges faced by doctors is finding patients and clinical cases that show particular similarities to a given case (similar symptoms, diagnosis, treatments, or other characteristics) amongst the rapidly growing amount of clinical records and medical publications and the complexity of the data. Detection of similarities among patients or groups of patients is key for evidence-based clinical practice, the selection of patients for clinical trials, prioritizing patients for vaccination and for understanding the variability in clinical outcomes.</p> <p>From a COVID-19 point of view, AI tools should distinguish between patients with and with no risk of a severe outcome, so that clinicians could intervene promptly. <strong>Specifically, this task aims to promote the development of systems able to detect similarities among a collection of clinical case texts.</strong></p> <p> </p> <p><strong>Technology point of view:</strong></p> <p>The objective is to be able to compute and measure similarity between patients represented by their clinical case, that is, the text describing their medical condition, previous morbidities, medical tests and treatments performed, diagnosis or outcome. This very complex scenario can in principle be approached by a diversity of methodologies ranging from text similarity techniques used to detect plagiarism, clinical concept detection, or even more advanced semantic textual similarity strategies dealing with the meaning of natural language through AI.</p> <p> </p> <p><strong>Healthcare point of view:</strong></p> <p>Access to medically relevant information hidden in clinical texts is one of the principal challenges for healthcare professionals in the AI digital age. Questions such as which are the symptoms of patients with a worse outcome, given similar comorbidities, medications or procedures are very difficult to answer without systematically processing clinical texts. Even simpler, epidemiological questions like how many days have passed before COVID-19 symptoms started or if patients had travelled to certain geographical areas can only be answered efficiently by means of computational tools. Similarities between patients can aid prognosis, diagnosis and decision making, saving vital time to healthcare practitioners.</p> <p> </p> <p>If you need some help, <a href="https://medium.com/@adriensieg/text-similarities-da019229c894">here</a> is a helpful resource that will help you get started.</p> <p> </p> <p><a href="https://www.youtube.com/playlist?list=PL5uSCzf1azhBeVCHyswazImBNpIW8gYTD">YouTube playlist with our session at BITSXLAMARATÓ</a></p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.