Health at Scale Corporation

Massachusetts Institute of Technology

Predictive Modeling, Inc.

Zeeshan H Syed

John V Guttag

Collin M Stultz

Dahee Lee

Tiange Zhan

Philip Pohong Sung

Devendra Goyal

Rudra Mehta

Zahoor Elahi

The present disclosure generally relates to generating a care and treatment plan for a patient using machine-learning techniques. An exemplary computer-implemented method comprises: receiving a plurality of care records; determining, based on a first machine learning model, that the plurality of care records are to be merged into an episode, wherein the episode is a predefined data representation comprising a predefined collection of data fields; identifying, from the plurality of care records, a plurality of attribute values corresponding to a particular attribute; determining, based on a second machine learning model, an aggregated attribute value based on the plurality of attribute values; updating, based on the aggregated attribute value, a data field of the episode; adding the episode to the patient profile, wherein the patient profile comprises a plurality of episodes associated with the patient.

Intelligent healthcare data fabric system

Embodiments of the present disclosure include systems and methods for generating a medical recommendation. Methods according to the present disclosure include receiving patient data associated with a first patient and receiving second patient data associated with a second patient. The methods further include inputting the first patient data and the second patient data into a trained machine-learning model to determine a first set of one or more risk values for the first patient and a second set of one or more risk values for the second patient. The methods further include comparing the first set of one or more risk values and the second set of one or more risk values to determine a priority for distributing care to the first patient and the second patient. In accordance with the determination that the first patient has priority, the system can generate a medical recommendation.

Machine intelligence-based prioritization of non-emergent procedures and visits

The present disclosure relates to providing personalized prediction and prevention of various types of medical events (e.g., emergency department visits, hospital admissions, complications) using machine-learning algorithms. An exemplary method comprises: obtaining a plurality of feature values of the patient; providing the plurality of feature values to a set of one or more trained machine-learning models to obtain: a first probabilistic value indicating a likelihood of a future medical event, a second probabilistic value indicating a likelihood of a reason for the future medical event, a third probabilistic value indicating a likelihood that the future medical event can be prevented, displaying, on the display, a risk value of the future medical event based on the first probabilistic value, a reason of the future medical event based on the second probabilistic value, an interceptability value of the future medical event based on the third probabilistic value.

Prediction and prevention of medical events using machine-learning algorithms

A computer-based system and method to assign patients to providers. In one embodiment, the invention predicts the performance of provider-patient pairs in terms of different outcome-, financial- and satisfaction-related metrics across providers using advanced machine learning methodologies to develop distinct models for each of these metrics across each of the providers using a historical database. In another embodiment, patients are assigned to providers in a batch or online manner using this information through an optimization framework that looks to maximize or minimize arbitrary combinations of the outcome-, financial-, and satisfaction-related metrics subject to practical operational constraints. In another embodiment, the invention includes logic to avoid 'boxing' providers and uses exploration to continuously update the profiles of providers for metrics in a manner that accounts for provider performance getting better or worse over time, and the historical database not being representative of all potential patients to be assigned by this system.

System to improve the logistics of clinical care by selectively matching patients to providers

A computer-based system to determine whether patients should be treated as inpatients or outpatients. The invention makes personalized predictions about the risk and timing of adverse outcomes for the patient, and further assesses how this risk and timing may vary if the patients are treated as inpatients or outpatients. This information informs how patients are assigned to an appropriate therapy. The invention includes logic relevant to predicting patient risk, decoupling patient risk into components inherent to the patient as well as additions/subtractions associated with the choice of treatment, and predicting the timing of adverse outcomes given censored data. The invention can be extended to use in a broad range of other application domains (e.g., matching learners to courses either offered in-classroom or online for education).

System to determine inpatient or outpatient care and inform decisions about patient care

A method and apparatus for predicting patient outcome from a physiological segmentable signal of a patient. In one embodiment, the method comprises the steps of obtaining the physiological segmentable signal of the patient; segmenting the physiological segmentable signal into a plurality of separate segmentable components; calculating a time series of the morphological distance between adjacent separate segmentable components of the plurality of separate segmentable components; and predicting patient outcome in response to the time series of the morphological distance. In another aspect, the invention relates to a method for extracting information from physiological signals for one or more subjects including the steps of partitioning the physiological signal into a plurality of components, grouping the components into a plurality of information classes, assigning a unique symbol to each information class, mapping each component to the assigned symbol, and examining one or more such sequences for clinical significance.

Method and apparatus for predicting patient outcomes from a physiological segmentable patient signal

The application relates a methodology and apparatus for identifying predictive patterns for acute clinical events in the absence of prior knowledge. Principles of conservation are used to identify activity that consistently precedes an outcome in patients, and describe a two-stage process that allows us to more efficiently search for such patterns in large datasets. This is achieved by first transforming continuous physiological signals from multiple patients into symbolic sequences, and by then searching for patterns in these reduced representations that are strongly associated with an outcome.

Growing community of inventors

Wayzata, MN, United States of America

Zeeshan H Syed