Best AI tools for< Predict Patient Prognosis >

MeDA School is an educational platform dedicated to promoting and nurturing talents in the field of Medical Artificial Intelligence (AI). The platform aims to establish a solid foundation for intelligent and precision medical talent pools in Taiwan and globally. MeDA School facilitates interaction and communication among members of the intelligent medical ecosystem, fostering deep understanding and trust in the operation and tasks of medical AI. The platform offers a blend of virtual and physical courses, inviting domain experts to share cutting-edge knowledge and integrating interdisciplinary knowledge to be practically applied in various fields.

Medcol.io is a clinical AI assistant designed to support healthcare professionals in making informed decisions by providing evidence-based recommendations and insights. The platform leverages advanced algorithms and medical data analysis to assist in diagnosing diseases, suggesting treatment plans, and predicting patient outcomes. With a user-friendly interface and real-time updates, Medcol.io aims to streamline the clinical decision-making process and improve patient care.

ClosedLoop is a healthcare data science platform that helps organizations improve outcomes and reduce unnecessary costs with accurate, explainable, and actionable predictions of individual-level health risks. The platform provides a comprehensive library of easily modifiable templates for healthcare-specific predictive models, machine learning (ML) features, queries, and data transformation, which accelerates time to value. ClosedLoop's AI/ML platform is designed exclusively for the data science needs of modern healthcare organizations and helps deliver measurable clinical and financial impact.

Tempus is an AI-enabled precision medicine company that brings the power of data and artificial intelligence to healthcare. With the power of AI, Tempus accelerates the discovery of novel targets, predicts the effectiveness of treatments, identifies potentially life-saving clinical trials, and diagnoses multiple diseases earlier. Tempus's innovative technology includes ONE, an AI-enabled clinical assistant; NEXT, a tool to identify and close gaps in care; LENS, a platform to find, access, and analyze multimodal real-world data; and ALGOS, algorithmic models connected to Tempus's assays to provide additional insight.

AiCure provides a patient-centric eClinical trial management platform that enhances drug development through improved medication adherence rates, more powerful analysis and prediction of treatment response using digital biomarkers, and reduced clinical tech burden. AiCure's solutions support traditional, decentralized, or hybrid trials and offer flexibility to meet the needs of various research designs.

Owkin is a full-stack AI biotech company that integrates the best of human and artificial intelligence to deliver better drugs and diagnostics at scale. By understanding complex biology through AI, Owkin identifies new treatments, de-risks and accelerates clinical trials, and builds diagnostic tools to reduce time to impact for patients.

ClosedLoop is a healthcare data science platform that helps organizations improve outcomes and reduce costs by providing accurate, explainable, and actionable predictions of individual-level health risks. The platform offers predictive analytics for various healthcare sectors, data science automation, and a healthcare content library to accelerate time to value. ClosedLoop's AI/ML platform is designed exclusively for the data science needs of modern healthcare organizations, enabling proactive interventions, improved clinical outcomes, and innovative healthcare offerings.

Iambic Therapeutics is a cutting-edge AI-driven drug discovery platform that tackles the most challenging design problems in drug discovery, addressing unmet patient need. Its physics-based AI algorithms drive a high-throughput experimental platform, converting new molecular designs to new biological insights each week. Iambic's platform optimizes target product profiles, exploring multiple profiles in parallel to ensure that molecules are designed to solve the right problems in disease biology. It also optimizes drug candidates, deeply exploring chemical space to reveal novel mechanisms of action and deliver diverse high-quality leads.

NovaResp cMAP™ is an AI-powered platform software designed to improve adherence to Continuous Positive Airway Pressure (CPAP) therapy for sleep apnea patients. It utilizes artificial intelligence and machine learning to predict and prevent apnea episodes during therapy, delivering personalized treatment at more comfortable air pressure levels. The application aims to enhance patient quality of life, increase sales for device manufacturers, and reduce labor costs for DMEs. NovaResp cMAP™ is compatible with all major PAP machines and is a revolutionary solution in the treatment of Obstructive Sleep Apnea (OSA).

Tempus is an AI-enabled precision medicine company that brings the power of data and artificial intelligence to healthcare. With the power of AI, Tempus accelerates the discovery of novel targets, predicts the effectiveness of treatments, identifies potentially life-saving clinical trials, and diagnoses multiple diseases earlier. Tempus' innovative technology includes ONE, an AI-enabled clinical assistant; NEXT, which identifies and closes gaps in care; LENS, which finds, accesses, and analyzes multimodal real-world data; and ALGOS, algorithmic models connected to Tempus' assays to provide additional insight.

GenHealth.ai is a cutting-edge AI application focused on generative healthcare AI. The platform offers innovative solutions for healthcare organizations by leveraging a Large Medical Model (LMM) trained on vast amounts of patient data. GenHealth.ai provides automated prior authorization, healthcare analytics, and predictive modeling to help healthcare leaders make informed decisions and optimize patient care. The application stands out for its ability to simulate patient futures, predict healthcare costs, and integrate with healthcare standards like FHIR, HL7, and X12.

Notle is an advanced AI-driven psychometric recording tool designed for mental health professionals. It revolutionizes how patient interactions in psychotherapy sessions are captured and analyzed. The platform provides cutting-edge analysis, effortless tracking, in-depth metrics, and empowers clinicians with intelligent analytics for personalized care. Notle sets a new benchmark for psychometric evaluation tools, ensuring unrivaled precision in psychometric assessment. It offers advanced behavioral insights, user-friendly interface, unmatched precision & reliability, and non-invasive integration into healthcare practices. The application is reliable, accurate, impactful, and validated through research methods.

CloudMedx is a healthcare data platform that provides aggregation, automation, and AI solutions. It simplifies decision making for patients, providers, and payers with a single powerful platform. Clinical, operations, and financial results are coordinated and delivered like never before.

Lunit Inc. is a leading provider of AI-powered cancer screening and treatment solutions. The company's mission is to conquer cancer through AI by developing innovative technologies that can help detect and treat cancer earlier and more effectively. Lunit's AI solutions are used by hospitals and clinics around the world to improve the accuracy and efficiency of cancer diagnosis and treatment.

Nucleai is an AI-driven spatial biomarker analysis tool that leverages military intelligence-grade geospatial AI methods to analyze complex cellular interactions in a patient's biopsy. The platform offers a first-of-its-kind multimodal solution by ingesting images from various modalities and delivering actionable insights to optimize biomarker scoring, predict response to therapy, and revolutionize disease diagnosis and treatment.

SynPlan is an AI solution designed to optimize healthcare workforce planning by predicting sick-leave absence and budget expenses. By utilizing advanced AI technology, SynPlan assists healthcare organizations in creating efficient plans, controlling budgets, and ensuring compliance with regulations. The application streamlines the staff planning process, enhances employee involvement, and increases continuity for patients. SynPlan is a product from Chiron Predictive Technologies AS, offering a comprehensive solution for workforce optimization in the healthcare sector.

Lex Machina is a Legal Analytics platform that provides comprehensive insights into litigation track records of parties across the United States. It offers accurate and transparent analytic data, exclusive outcome analytics, and valuable insights to help law firms and companies craft successful strategies, assess cases, and set litigation strategies. The platform uses a unique combination of machine learning and in-house legal experts to compile, clean, and enhance data, providing unmatched insights on courts, judges, lawyers, law firms, and parties.

NeuProScan is an AI platform designed for the early detection of pre-clinical Alzheimer's from MRI scans. It utilizes AI technology to predict the likelihood of developing Alzheimer's years in advance, helping doctors improve diagnosis accuracy and optimize the use of costly PET scans. The platform is fully customizable, user-friendly, and can be run on devices or in the cloud. NeuProScan aims to provide patients and healthcare systems with valuable insights for better planning and decision-making.

GRAIL is a healthcare company innovating to solve medicine’s most important challenges. Our team of leading scientists, engineers and clinicians are on an urgent mission to detect cancer early, when it is more treatable and potentially curable. GRAIL's Galleri® test is a first-of-its-kind multi-cancer early detection (MCED) test that can detect a signal shared by more than 50 cancer types and predict the tissue type or organ associated with the signal to help healthcare providers determine next steps.

The Predict API is a powerful tool that allows you to forecast your data with simplicity and accuracy. It uses the latest advancements in stochastic modeling and machine learning to provide you with reliable projections. The API is easy to use and can be integrated with any application. It is also highly scalable, so you can use it to forecast large datasets. With the Predict API, you can gain valuable insights into your data and make better decisions.

AIDE is a machine learning code generation agent that can generate solutions for machine learning tasks from natural language descriptions. It has the following features: 1. **Instruct with Natural Language**: Describe your problem or additional requirements and expert insights, all in natural language. 2. **Deliver Solution in Source Code**: AIDE will generate Python scripts for the **tested** machine learning pipeline. Enjoy full transparency, reproducibility, and the freedom to further improve the source code! 3. **Iterative Optimization**: AIDE iteratively runs, debugs, evaluates, and improves the ML code, all by itself. 4. **Visualization**: We also provide tools to visualize the solution tree produced by AIDE for a better understanding of its experimentation process. This gives you insights not only about what works but also what doesn't. AIDE has been benchmarked on over 60 Kaggle data science competitions and has demonstrated impressive performance, surpassing 50% of Kaggle participants on average. It is particularly well-suited for tasks that require complex data preprocessing, feature engineering, and model selection.

InterpretML is an open-source package that incorporates state-of-the-art machine learning interpretability techniques under one roof. With this package, you can train interpretable glassbox models and explain blackbox systems. InterpretML helps you understand your model's global behavior, or understand the reasons behind individual predictions. Interpretability is essential for: - Model debugging - Why did my model make this mistake? - Feature Engineering - How can I improve my model? - Detecting fairness issues - Does my model discriminate? - Human-AI cooperation - How can I understand and trust the model's decisions? - Regulatory compliance - Does my model satisfy legal requirements? - High-risk applications - Healthcare, finance, judicial, ...

CareGPT is a medical large language model (LLM) that explores medical data, training, and deployment related research work. It integrates resources, open-source models, rich data, and efficient deployment methods. It supports various medical tasks, including patient diagnosis, medical dialogue, and medical knowledge integration. The model has been fine-tuned on diverse medical datasets to enhance its performance in the healthcare domain.

Fairseq is a sequence modeling toolkit that enables researchers and developers to train custom models for translation, summarization, language modeling, and other text generation tasks. It provides reference implementations of various sequence modeling papers covering CNN, LSTM networks, Transformer networks, LightConv, DynamicConv models, Non-autoregressive Transformers, Finetuning, and more. The toolkit supports multi-GPU training, fast generation on CPU and GPU, mixed precision training, extensibility, flexible configuration based on Hydra, and full parameter and optimizer state sharding. Pre-trained models are available for translation and language modeling with a torch.hub interface. Fairseq also offers pre-trained models and examples for tasks like XLS-R, cross-lingual retrieval, wav2vec 2.0, unsupervised quality estimation, and more.

llm_benchmarks is a collection of benchmarks and datasets for evaluating Large Language Models (LLMs). It includes various tasks and datasets to assess LLMs' knowledge, reasoning, language understanding, and conversational abilities. The repository aims to provide comprehensive evaluation resources for LLMs across different domains and applications, such as education, healthcare, content moderation, coding, and conversational AI. Researchers and developers can leverage these benchmarks to test and improve the performance of LLMs in various real-world scenarios.

TrustLLM is a comprehensive study of trustworthiness in LLMs, including principles for different dimensions of trustworthiness, established benchmark, evaluation, and analysis of trustworthiness for mainstream LLMs, and discussion of open challenges and future directions. Specifically, we first propose a set of principles for trustworthy LLMs that span eight different dimensions. Based on these principles, we further establish a benchmark across six dimensions including truthfulness, safety, fairness, robustness, privacy, and machine ethics. We then present a study evaluating 16 mainstream LLMs in TrustLLM, consisting of over 30 datasets. The document explains how to use the trustllm python package to help you assess the performance of your LLM in trustworthiness more quickly. For more details about TrustLLM, please refer to project website.

This repository contains a curated list of resources on generative information retrieval, including research papers, datasets, tools, and applications. Generative information retrieval is a subfield of information retrieval that uses generative models to generate new documents or passages of text that are relevant to a given query. This can be useful for a variety of tasks, such as question answering, summarization, and document generation. The resources in this repository are intended to help researchers and practitioners stay up-to-date on the latest advances in generative information retrieval.

Responsible AI Toolbox is a suite of tools providing model and data exploration and assessment interfaces and libraries for understanding AI systems. It empowers developers and stakeholders to develop and monitor AI responsibly, enabling better data-driven actions. The toolbox includes visualization widgets for model assessment, error analysis, interpretability, fairness assessment, and mitigations library. It also offers a JupyterLab extension for managing machine learning experiments and a library for measuring gender bias in NLP datasets.

Data-to-paper is an AI-driven framework designed to guide users through the process of conducting end-to-end scientific research, starting from raw data to the creation of comprehensive and human-verifiable research papers. The framework leverages a combination of LLM and rule-based agents to assist in tasks such as hypothesis generation, literature search, data analysis, result interpretation, and paper writing. It aims to accelerate research while maintaining key scientific values like transparency, traceability, and verifiability. The framework is field-agnostic, supports both open-goal and fixed-goal research, creates data-chained manuscripts, involves human-in-the-loop interaction, and allows for transparent replay of the research process.

This repository contains a comprehensive collection of 63 must-know Large Language Models (LLMs) interview questions. It covers topics such as the architecture of LLMs, transformer models, attention mechanisms, training processes, encoder-decoder frameworks, differences between LLMs and traditional statistical language models, handling context and long-term dependencies, transformers for parallelization, applications of LLMs, sentiment analysis, language translation, conversation AI, chatbots, and more. The readme provides detailed explanations, code examples, and insights into utilizing LLMs for various tasks.

Slideflow is a deep learning library for digital pathology, offering a user-friendly interface for model development. It is designed for medical researchers and AI enthusiasts, providing an accessible platform for developing state-of-the-art pathology models. Slideflow offers customizable training pipelines, robust slide processing and stain normalization toolkit, support for weakly-supervised or strongly-supervised labels, built-in foundation models, multiple-instance learning, self-supervised learning, generative adversarial networks, explainability tools, layer activation analysis tools, uncertainty quantification, interactive user interface for model deployment, and more. It supports both PyTorch and Tensorflow, with optional support for Libvips for slide reading. Slideflow can be installed via pip, Docker container, or from source, and includes non-commercial add-ons for additional tools and pretrained models. It allows users to create projects, extract tiles from slides, train models, and provides evaluation tools like heatmaps and mosaic maps.

This tool is designed to detect and classify Alzheimer's Disease using Deep Learning and Machine Learning algorithms on an early basis, which is further optimized using the Crow Search Algorithm (CSA). Alzheimer's is a fatal disease, and early detection is crucial for patients to predetermine their condition and prevent its progression. By analyzing MRI scanned images using Artificial Intelligence technology, this tool can classify patients who may or may not develop AD in the future. The CSA algorithm, combined with ML algorithms, has proven to be the most effective approach for this purpose.

Scientific Large Language Models (Sci-LLMs) is a repository that collects papers on scientific large language models, focusing on biology and chemistry domains. It includes textual, molecular, protein, and genomic languages, as well as multimodal language. The repository covers various large language models for tasks such as molecule property prediction, interaction prediction, protein sequence representation, protein sequence generation/design, DNA-protein interaction prediction, and RNA prediction. It also provides datasets and benchmarks for evaluating these models. The repository aims to facilitate research and development in the field of scientific language modeling.

Bolts package provides a variety of components to extend PyTorch Lightning, such as callbacks & datasets, for applied research and production. Users can accelerate Lightning training with the Torch ORT Callback to optimize ONNX graph for faster training & inference. Additionally, users can introduce sparsity with the SparseMLCallback to accelerate inference by leveraging the DeepSparse engine. Specific research implementations are encouraged, with contributions that help train SSL models and integrate with Lightning Flash for state-of-the-art models in applied research.

A curated list of Generative AI projects, tools, artworks, and models

Turbocharge your research and streamline your path to breakthrough findings. Leveraging the vast resources of PubChem, this GPT taps into a wealth of chemical data—from substances to proteins and patents—unleashing the full potential of your data for richer, more informed discoveries.

A specialised GPT for calculating and understanding Intracranial hemorrhage scores

I predict DAU based on growth and retention.

Predict the future, opine on politics and controversial topics, and have GPT assess what is "true"

I predict future stock market prices. AI analyst. Your trading analysis assistant. Press H to bring up prompt hot key menu. Not financial advice.

Analyze Financial Statements step by step to Predict Earnings Direction

Predict fashion trends

AI expert in analyzing historical patterns to predict future events

Does Pool Performance of Elite Triathletes Predict Open-Water Performance?

A moot competition companion. & Trial Prep companion . Test and improve arguments- predict your opponent's reaction.

Personalized, analytical sports prediction AI.

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Predicts gender for first names using data

Our college entrance exam prediction app uses advanced algorithms and data analysis to provide accurate predictions for students preparing to take their college entrance exams.

Prédictif des tendances crypto à partir de la presse et des réseaux sociaux

Horoscopes--八字算命术预测你的健康、婚姻、事业、财富...

Analyzes and predicts the SSE 500 Index.

1620年代の主要な歴史的出来事を分析し未来の予測に役立てる。

A question ? Ask me for a prediction ... ( /help for help )

MMA Expert & Trainer