11 results about "Discontinuation" patented technology

A method for reducing adverse events associated with administering ecopipam, the method comprising dosing according to a specific weight range and an associated dose escalation regimen, and a further downward dosing regimen for discontinuation of ecopipam.

A treatment for patients having cancer expressing Prostate Cancer Specific Membrane Antigen (PSMA) is disclosed. Said treatment involves the use of an 225Ac-radioconjugate, in particular 225Ac-PSMA-I&T. A dosage regimen that entails more frequent administration of the 225Ac-radioconjugate at lower dose level may be employed. This involves the use of a dosage of 50-75 kBq / kg of body weight on a dosing schedule of every 4-6 weeks. A flat dosing may also be employed where the dose does not depend on the body weight of the subject. Such dosing improve the benefit / risk profile with fewer adverse events and less discontinuations, dosing interruptions and reductions in dose. Of particular importance is a reduction in salivary gland toxicity and xerostomia.

Provided are methods of treating cancer in a human subject. The method includes selecting a subject in need of treatment of cancer; administering to the subject a therapeutically effective amount of gedatolisib at least once a week for a period of three weeks; discontinuing administration of gedatolisib for a period of one week; and resuming administration of gedatolisib at least once a week following the period of discontinuation. The administration for at least a period of three weeks and discontinued administration for at least a period of one week constitutes a cycle, where the cycle is repeated for at least two cycles.

Provided are methods of treating cancer in a human subject. The method includes selecting a subject in need of treatment of cancer; administering to the subject a therapeutically effective amount of gedatolisib at least once a week for a period of three weeks; discontinuing administration of gedatolisib for a period of one week; and resuming administration of gedatolisib at least once a week following the period of discontinuation. The administration for at least a period of three weeks and discontinued administration for at least a period of one week constitutes a cycle, where the cycle is repeated for at least two cycles.

A method of treating a patient with a complement-associated disorder with a complement inhibitor and then monitoring the patient during administration of the complement inhibitor and thereafter. The method includes certifying a prescriber and a dispenser and then monitoring the patient during and after administration of the complement inhibitor for an adverse event, including a meningococcal or pneumococcal event.

The present invention discloses a CRRT unplanned discontinuation warning and decision support information system, which relates to the field of data mining technology. The system includes a medical record database, a data analysis server, and a medical workstation device. The medical record database is used to record the medical records of all historical CRRT patients. The data analysis server is used to obtain a prediction model for determining whether an unplanned CRRT discontinuation event has occurred based on the medical records of successful and failed CRRT discontinuations and artificial intelligence algorithm calibration and verification modeling. The prediction model is then imported into the prediction model using patient attribute information of the patient to be discontinued, the onboarding plan attribute information of the onboarding plan formulated for the patient to be discontinued, and the nursing staff attribute information of the nursing staff assigned to the patient to be discontinued. The prediction result is obtained and fed back to the workstation device so that the warning level is determined based on the prediction result and output and displayed. In this way, the risk of unplanned CRRT discontinuation can be accurately identified as early as possible, enriching the amount of reference information.

With a trained, computerized discontinuation predictor machine learning component (MLC), predict, based on an input time series, a time when a subject will discontinue a course of medical treatment; with a trained, computerized pattern behavior extractor MLC, extract from said input time series the top k discriminatory sequences via discriminatory sub-sequence mining (said top k discriminatory sequences differentiate between first and second classes of interest to provide a hypothesis for downstream analysis of a cause of discontinuing said course of treatment). With a trained, causal effect estimator computerized MLC, determine a reason why said subject will discontinue said course of medical treatment, based on said top k discriminatory sequences and additional data; and with a computerized user interface, provide said time and said reason why to a responsible party to initiate an intervention.

The invention discloses a system, an operation method and a prediction processor for predicting a patient to stop prescription treatment. The system includes a memory device storing a training dataset including treatment data and patient data for a group of patients and further including an indication as to whether the patient has stopped treatment of a prescription treatment or procedure, at least one patient prediction model, and patient data and previous treatment data for a patient; and a predictive processor receiving treatment data of the patient from the APD machine, storing the treatment data to a memory device, determining a probability that the patient will stop prescription treatment by applying the patient data of the patient, the treatment data, and previous treatment data to at least one patient predictive model, and causing the probability to be displayed within a user interface on the clinician device.

The present disclosure provides a method of treating respiratory depression in a patient after discontinuation of ventilator therapy comprising administering a respiratory stimulant to a patient in need thereof.

The invention discloses a method for predicting recurrence after TKI treatment of CML patients based on machine learning, and the method comprises the steps: collecting sample data of the CML patients to construct a data set, the sample data comprising input features and output, the input features comprising physiological indexes and medication schemes of the CML patients, and the output being MMR maintenance conditions within n months after TKI is stopped; establishing a recurrence prediction model after drug withdrawal of the CML; training a recurrence prediction model after drug withdrawal of the CML through the data set; and inputting the physiological indexes and the medication scheme of a to-be-predicted CML patient into the trained model for predicting recurrence after drug withdrawal of the CML, and outputting a prediction result. According to the method for predicting relapse of the CML patient after TKI treatment withdrawal based on machine learning, a prediction model which can be applied to clinic is trained by utilizing a machine learning algorithm, the probability of relapse of the CML patient after TKI treatment withdrawal can be predicted, and a doctor is assisted in selecting a more appropriate treatment scheme for the patient.

Provided are methods of treating cancer in a human subject. The method includes selecting a subject in need of treatment of cancer; administering to the subject a therapeutically effective amount of gedatolisib at least once a week for a period of three weeks; discontinuing administration of gedatolisib for a period of one week; and resuming administration of gedatolisib at least once a week following the period of discontinuation. The administration for at least a period of three weeks and discontinued administration for at least a period of one week constitutes a cycle, where the cycle is repeated for at least two cycles.