125 skills found · Page 1 of 5
CHAOZHAO-1 / DG PHMThis is a reposotory that includes paper、code and datasets about domain generalization-based fault diagnosis and prognosis. (基于领域泛化的故障诊断和预测)
amanchadha / Coursera AI For Medicine SpecializationProgramming assignments, labs and quizzes from all courses in the Coursera AI for Medicine Specialization offered by deeplearning.ai
biswajitsahoo1111 / Rul Codes OpenThis repository contains code that implement common machine learning algorithms for remaining useful life (RUL) prediction.
CHAOZHAO-1 / LLM Based PHMThis is a reposotory that includes paper about LLM-based fault diagnosis and prognosis. (基于大模型的故障诊断和预测，持续更新)
vanderschaarlab / AutoprognosisA system for automating the design of predictive modeling pipelines tailored for clinical prognosis.
facebookresearch / CovidPrognosisCOVID deterioration prediction based on chest X-ray radiographs via MoCo-trained image representations
gevaertlab / MultimodalPrognosisDeep Learning with Multimodal Representation for Pancancer Prognosis Prediction
holden-mcgorin / UniPHMUnified PHM framework for Remaining Useful Life (RUL) prediction, fault diagnosis, fault detection, and anomaly detection for bearings, turbofan engines, and other industrial systems.
nasa / Li Ion Battery Prognosis Based On Hybrid Bayesian PINNCode used to generate the results of the paper: Nascimento et al. A framework for Li-ion battery prognosis based on hybrid Bayesian physics-informed neural networks.
mathworks / WindTurbineHighSpeedBearingPrognosis DataData set for Wind Turbine High-Speed Bearing Prognosis example in Predictive Maintenance Toolbox
ahmedmalaa / AutoPrognosisCodebase for "AutoPrognosis: Automated Clinical Prognostic Modeling via Bayesian Optimization", ICML 2018.
nyukat / COVID 19 PrognosisAn artificial intelligence system for predicting the deterioration of COVID-19 patients in the emergency department
PML-UCF / Pinn Corrosion FatiguePython scripts for physics-informed neural networks for corrosion-fatigue prognosis
lab-rasool / HoneyBee🐝 | From Data to Prognosis: Embedding Multimodal Oncology Data for Precision Medicine
lrsoenksen / SPL UD DLA reported 96,480 people were diagnosed with melanoma in the United States in 2019, leading to 7230 reported deaths. Early-stage identification of suspicious pigmented lesions (SPLs) in primary care settings can lead to im- proved melanoma prognosis and a possible 20-fold reduction in treatment cost. Despite this clinical and economic value, efficient tools for SPL detection are mostly absent. To bridge this gap, we developed an SPL analysis system for wide-field images using deep convolutional neural networks (DCNNs) and applied it to a 38,283 dermatological dataset collected from 133 patients and publicly available images. These images were obtained from a variety of consumer-grade cameras (15,244 nondermoscopy) and classified by three board-certified dermatologists. Our system achieved more than 90.3% sensitivity (95% confidence interval, 90 to 90.6) and 89.9% specificity (89.6 to 90.2%) in distinguishing SPLs from nonsuspicious lesions, skin, and complex backgrounds, avoiding the need for cumbersome individual lesion imaging. We also present a new method to extract intrapatient lesion saliency (ugly duckling criteria) on the basis of DCNN features from detected lesions. This saliency ranking was validated against three board-certified dermatologists using a set of 135 individual wide-field images from 68 dermatolog- ical patients not included in the DCNN training set, exhibiting 82.96% (67.88 to 88.26%) agreement with at least one of the top three lesions in the dermatological consensus ranking. This method could allow for rapid and accurate assessments of pigmented lesion suspiciousness within a primary care visit and could enable improved patient triaging, utilization of resources, and earlier treatment of melanoma.
PathologyDataScience / HiPSHistomic Prognostic Signature (HiPS): A population-level computational histologic signature for invasive breast cancer prognosis
fernandodecastilla / Machine Learning For Predictive MaintenanceAnomaly detection and failure prognosis applied to industrial machines
mistersharmaa / BreastCancerPredictionBreast cancer has the second highest mortality rate in women next to lung cancer. As per clinical statistics, 1 in every 8 women is diagnosed with breast cancer in their lifetime. However, periodic clinical check-ups and self-tests help in early detection and thereby significantly increase the chances of survival. Invasive detection techniques cause rupture of the tumor, accelerating the spread of cancer to adjoining areas. Hence, there arises the need for a more robust, fast, accurate, and efficient non-invasive cancer detection system. Early detection can give patients more treatment options. In order to detect signs of cancer, breast tissue from biopsies is stained to enhance the nuclei and cytoplasm for microscopic examination. Then, pathologists evaluate the extent of any abnormal structural variation to determine whether there are tumors. Architectural Distortion (AD) is a very subtle contraction of the breast tissue and may represent the earliest sign of cancer. Since it is very likely to be unnoticed by radiologists, several approaches have been proposed over the years but none using deep learning techniques. AI will become a transformational force in healthcare and soon, computer vision models will be able to get a higher accuracy when researchers have the access to more medical imaging datasets. The application of machine learning models for prediction and prognosis of disease development has become an irrevocable part of cancer studies aimed at improving the subsequent therapy and management of patients. The application of machine learning models for accurate prediction of survival time in breast cancer on the basis of clinical data is the main objective. We have developed a computer vision model to detect breast cancer in histopathological images. Two classes will be used in this project: Benign and Malignant
gevaertlab / GBM360Spatial cellular architecture predicts prognosis in glioblastoma - Nature Communications
XIAOJIE0519 / E2EE2E (Easy to Ensemble) is a novel R package designed to provide a comprehensive and flexible framework for ensemble machine learning, specifically tailored for medical applications like diagnosis and prognosis.
