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The Clinical Atlas of Intravascular Optical Coherence Tomography
Offers an interactive guidance to a systematic and scientifically critical interpretation of optical coherence tomography (OCT) images. The Clinical Atlas of Intravascular OCT consists of eight separate chapters offering the following contents: (i) Principles of OCT, (ii) Artefacts, (iii) Atherosclerosis, (iv) Evaluation of metallic stents, (v) 3D-OCT, (vi) Evaluation of bio-resorbable scaffold,s (vii) Miscellaneous (vein grafts, peripheral vessels, etc.), and (viii) New frontiers in OCT imaging.
TREWScore / Targeted Real-time Early Warning score
Identifies patients at high risk of developing septic shock in the future. TREWScore was developed by training a model that estimates the time to an adverse event using supervised learning. The software was used to predict, many hours before standard screening protocols, patients at high risk of developing septic shock, using only measurements routinely collected in the electronic health record (HER) and accounts for the effects of censoring due to treatment in estimating the model.
TMPM-ICD9 / Trauma Mortality Prediction Model based on ICD9
Provides a ICD-9 injury model. TMPM-ICD9 is a probit regression model which includes the model-averaged regression coefficients (MARC) values for the 5 most severe injuries sustained by a patient as predictor variables. This model represents a significant improvement in existing injury scoring systems based on administrative data. It is based on universally available ICD-9-CM codes and could be used by virtually any hospital caring for trauma patients.
RICORDO / Researching Interoperability using Core Reference Datasets and Ontologies for the virtual physiological human
Gathers functions for real-time reasoning over very large ontologies to express complex ontology concepts. RICORDO is designed for biomedical resources, and related ontologies, relevant to a number of communities including the physiology modeling, the pharmacology modeling, and the medical education community. It permits to find and annotate resources with both terms from reference ontologies and composites of those terms.
OASISPRO / Omics Analysis System for Precision Oncology
Allows oncological clinical prediction. OASISPRO is a cloud-based omics analysis tool that integrates quantitative omics data and builds prediction models for cancer phenotypes. The software enables quantitative omics analysis, provides insights into the biology of cancer and empowers accurate clinical predictions. The data mining system is extensible to other diseases and health conditions. OASISPRO can contribute to establish personalized cancer treatment plans, thereby increasing the quality of care and reducing the cost of cancer management.
iPF / Integrative Phenotyping Framework
An integrative phenotyping framework for disease subtype discovery. iPF integrates homogeneous patient clustering from different omics data in an agglomerative manner and then visualized heterogeneous clustering of pairwise omics sources. The framework was applied to two batches of lung samples obtained from patients diagnosed with chronic obstructive lung disease or interstitial lung disease with well-characterized clinical (phenomic) data, mRNA and microRNA expression profiles.
WINROP
Provides an online monitoring system, following weight gain, for predicting developing sight threatening retinopathy of prematurity (ROP). Use of WINROP requires that an infant has (1) GA less than 32 weeks at birth, (2) weekly weight measurements, (3) physiologic weight gain, and (4) absence of other pathologic retinal vascular disease. WINROP has demonstrated 100% sensitivity in predicting severe ROP in 2 Swedish and US populations. The WINROP system was also validated in a Brazilian population with a sensitivity of 90.5% in predicting proliferative ROP.
NorwoodScoreApp
Predicts real-time outcomes among children undergoing a Norwood operation. NorwoodScoreApp is a prognostic tool that predicts poor outcomes both before and after a Norwood operation, using advanced modeling methods, such as Bayesian conditional probit regression models and Markov chain Monte Carlo (MCMC) simulations. The software aims to provide families and health care providers with realistic expectations both before and after the Norwood operation. It can assist in creating a risk stratification for the purposes of benchmarking, family counseling, and research.
PREVISE / Predictive algoRithm for EValuation and Intervention in Sepsis
Aims to improve sepsis-related patient outcomes. PREVISE is a machine-learning based sepsis prediction algorithm, designed to compare trends in each patient’s electronic health record (HER) measurements, to confirmed prior sepsis cases for detecting and predicting sepsis. The algorithm has been examined in several retrospective studies, where it has been validated for detection of sepsis, severe sepsis and septic shock.
DeepDeath
Provides several information which can help to improve health standards by studying health related trajectories in societies with large populations. DeepDeath represents a deep classifier which is based on the recurrent neural network (RNN). This tool allows users to have available data in very large quantities and need the utilization of Big Data techniques to discover complex hidden patterns. It uses contents provided by the National Center for Health Statistics and utilizes long short-term memory networks (LSTMs).
Phendo
Establishes a catalog of the signs and symptoms of endometriosis as experienced by users, people with endometriosis. Phendo provides opportunities to reflect upon the collected data and may help users in managing their disease. Users can track their experience of endometriosis in Phendo at their convenience, track more or fewer signs, symptoms, comorbid conditions, and self-management information, or stop tracking at any time. Phendo is compatible with iPhone, iPad, and iPod touch.
Cloud4Cancer
Uses attributes from Fine Needle Aspirates (FNA) to determine if a breast mass is malignant or benign. Cloud4Cancer is a web app that can detect early stages of breast cancer. The current network is 99.11% sensitive to malignancy and 7.6 million trials demonstrated the service will improve as more samples are collected from the worldwide hospital community. This method is ready to diagnose actual patients, but more global participation is required to confirm the findings and increase the predictive success on blind samples.
VLBW Postnatal Growth Charts / Very Low Birth Weight Postnatal Growth Charts
Allows the user to enter the Birth Weight, Birth Length and Birth Head Circumference of a 501-1500 gram infant. VLBW Postnatal Growth Charts constructs individualized, expected growth curves based upon the NICHD Neonatal Research Network Growth Observational Study. Note that while these expected growth curves are based upon data collected from a large heterogenous population and reflect nutritional practices common in 1994-95, the "expected" curve may not represent optimal growth.
CytoCom
Obsolete
An interactive plugin for Cytoscape that can be used to search, explore, analyse and visualise human Disease Comorbidity Network (DCN). CytoCom represents disease-disease associations in terms of bipartite graphs and provides ICD9 (International Classification of Diseases, Ninth Revision)-centric and disease name centric views of disease information. It allows users to find associations between diseases based on the two measures: Relative Risk (RR) and varphi-correlation values. In the disease network, the size of each node is based on the prevalence of that disease. CytoCom is capable of clustering disease network based on the ICD9 disease category. It provides user-friendly access that facilitates exploration of human diseases, and finds additional associated diseases by double-clicking a node in the existing network. Additional comorbid diseases are then connected to the existing network. It is able to assist users for interpretation and exploration of the human diseases by a variety of built-in functions. Moreover, CytoCom permits multi-coloring of disease nodes according to standard disease classification for expedient visualisation.
comoR
Obsolete
An R software to compute novel estimators of the disease comorbidity associations. Starting from an initial diagnosis, genetic and clinical data of a patient comoR identifies the risk of disease comorbidity. Then it provides a pipeline with different causal inference packages (e.g. pcalg, qtlnet etc) to predict the causal relationship of diseases. It also provides a pipeline with network regression and survival analysis tools (e.g. Net-Cox, rbsurv etc) to predict more accurate survival probability of patients. The input of this software is the initial diagnosis for a patient and the output provides evidences of disease comorbidity mapping.
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