10 results about "Clinical course" patented technology

Likely outcomes of a treatment of an adolescent patient who has major depressive disorder (“MDD”) using machine learning. Symptoms reported at one or two points in time are input to a suitably trained machine learning model, generating output that indicate a prediction of the most likely outcome of a particular treatment at a third point in time, a future severity of symptoms, or another clinical course of action. The treatment outcome can include the likelihood of remission, response to drug, selection of the drug most likely to give positive outcomes, and so on. The machine learning model can implement a probabilistic graphical model, as an example.

Systems and methods for performing one or more medical analysis tasks are provided. Patient data of a patient is received for a set of biomarkers acquired at one or more time points within a period of time. The patient data is encoded using an encoder network to generate patient data embeddings. One or more medical analysis tasks are performed based on the patient data embeddings using one or more decoder networks. The one or more medical analysis tasks comprise generating recommendations for a clinical course of action. Results of the one or more medical analysis tasks are output.

Systems and methods for performing one or more medical analysis tasks are provided. Patient data of a patient is received for a set of biomarkers acquired at one or more time points within a period of time. The patient data is encoded using an encoder network to generate patient data embeddings. One or more medical analysis tasks are performed based on the patient data embeddings using one or more decoder networks. The one or more medical analysis tasks comprise generating recommendations for a clinical course of action. Results of the one or more medical analysis tasks are output.

The present invention relates to an artificial intelligence model and an implementation method therefor, which are capable of selecting and extracting, from CT images of patients with IgA nephropathy, image features significant for prognostic prediction and applying same to a machine learning model to thereby predict, with high accuracy, the likelihood of the patients with IgA nephropathy progressing to end-stage renal failure within five years. The present invention provides a prognostic prediction model that rapidly predicts the prognosis of a patient without an invasive kidney biopsy, overcomes the limitations of conventional pathology diagnosis relying on invasive methods, and enables periodic prognostic evaluation with significantly improved reliability. In addition, the present invention is capable of precisely reflecting characteristics of each item and accurately predicting a clinical course of a patient, by combining various feature selection methods and binary classifiers for each item of mesangial hypercellularity (M), endothelial hypercellularity (E), segmental glomerulosclerosis (S), and tubular atrophy / interstitial fibrosis (T), which constitute a MEST score.

The invention discloses a medical term analysis method based on a knowledge graph technology, and the method comprises the following steps: obtaining medical standard literatures of different time versions, and constructing a medical term knowledge graph sequence containing time dimension information; based on the medical term knowledge graph sequence, calculating semantic representation changes of the target medical term at different time points to obtain a semantic evolution path of the target medical term; acquiring clinical disease course data related to the target medical term; based on a statistical significance test method, calculating the support degree of the clinical disease course data to emerging semantics in the semantic evolution path, and generating a quantitative verification result; and dynamically updating and optimizing the medical term knowledge graph sequence according to the quantitative verification result. According to the method, prospective active perception and accurate verification of emerging semantics are realized, and the maintenance mode of the knowledge graph can be converted into active optimization from post passive updating, so that potential medical risks caused by indefinite term evolution are effectively reduced.

The application discloses an intelligent anus fistula analysis system and method based on artificial intelligence, and belongs to the technical field of intelligent anus fistula analysis, and comprises a multi-source data collection unit, a data preprocessing unit, a segmentation processing module, a local lesion feature enhancement unit, a multi-modal feature extraction unit, a time-frequency joint processing unit, a phased feature optimization unit and an efficacy evaluation unit.The application can comprehensively depict anus fistula lesion healing characteristics, and improve the objectivity and comprehensiveness of efficacy evaluation.The application can accurately separate and respectively depict long-term healing trend and short-term local stress fluctuation of the lesion, so that the efficacy evaluation is more in line with the real clinical course law.The application introduces a phased feature optimization mechanism in combination with the whole cycle treatment stage of the anus fistula, can perform fine quantitative evaluation at different treatment stages, and realizes phased efficacy analysis on patients.

The invention discloses an intelligent traditional Chinese medicine packet treatment system based on multi-physics field dynamic coupling, a preparation method and a regulation and control method, and relates to the technical field of intelligent traditional Chinese medicine instruments. The far infrared bag comprises a graphene heating sheet and a double-PID temperature control module; the composite magnetic therapy bag contains Al2O3 core-shell structure neodymium iron boron particle magnetic powder formed by plasma vapor deposition; the four-point magnetic coupling positioning mechanism is composed of an annular N-S pole alternating magnetic snap group and a Hall sensor array; and the biocompatible isolation cloth is assembled on the human body contact side of the composite magnetic therapy bag and is used for isolating the composite magnetic therapy bag from the human skin. The system has the advantages of high temperature control precision, high field domain coupling efficiency, strong high-temperature stability of the magnetic powder, short clinical treatment course, low safety risk, high intelligent degree and the like, and can be widely applied to the fields of medical rehabilitation, primary medical treatment, family health, sports medicine and the like.

The invention is suitable for the technical field of Internet of Things management, and particularly relates to an Internet of Things management system and management method for deep venous thrombosis prevention equipment, and the method comprises the steps: constructing a historical data set of deep venous thrombosis treatment, the historical data set comprises the use duration, clinical data and equipment parameters, and taking the use duration as a horizontal coordinate, the method comprises the following steps of: taking the equipment parameters as vertical coordinates, drawing a query curve, obtaining diagnosis data of deep venous thrombosis of a patient, correcting the query curve by utilizing the medical history, and obtaining a personal parameter curve, wherein the diagnosis data comprises a unique identity number, a medical history and a diagnosis result of the patient. By constructing a multi-level control framework, the patients can be graded according to the clinical disease courses of the patients, the equipment parameters of the early-stage patients are adjusted by utilizing the last-stage patients, and the adjustment participation is provided by utilizing the same-stage patients, so that personalized treatment is provided for the patients, potential problems are recognized in advance, and the use effect of prevention equipment is improved.

The invention relates to an in vitro method for establishing the prognosis of a subject diagnosed as suffering or having suffered from a diffuse large B-cell lymphoma and including: - an identification step of at least two markers comprising 2-aminobutyrate or its acid derivative thereof and LDL-1 lipoprotein optionally in combination with at least one marker selected from : 2-hydroxybutyrate or its acid derivative thereof, 3-hydroxybutyrate or its acid derivative thereof, LDL-2 lipoprotein, LDL-1-CH lipoprotein, LDL-1 lipoprotein phospholipids, the Apo-B subfraction of LDL-1 lipoprotein, LDL-1 lipoprotein triglycerides, LDL-2 lipoprotein triglycerides, LDL-3 lipoprotein triglycerides, formic acid and acetyl acetic acid, identifying said at least two markers being the prognosis of, or a risk of, a bad clinical course of the diffuse large B-cell lymphoma in the subject.

The present invention discloses a method and system for generating medical records of clinical use of antibiotics. The method collects the patient's panoramic historical medical data, forms a medical record training data set through preprocessing and annotation, uses the Bi‑LSTM‑CRF model to establish the medical record generation rules for the use of antibiotics, builds a medical record generation model based on the medical record generation rules for the use of antibiotics, and uses the medical record training data set for model training. After the doctor inputs the medical order information of the antibiotics, the model can automatically generate the medical record text of the use of antibiotics based on the real-time captured patient-related medical data and add it to the electronic medical record document, thereby ensuring the quality of the medical documents for the use of antibiotics and providing real-time captured audit materials and reliable basis for the medical insurance payment review of antibiotics.