International Business Machines Corporation

Huijing Jiang

Fei Liu

Young Min Lee

Ta-Hsin Li

Gigi Y C Yuen-Reed

Aurelie C Lozano

Kevin N Tran

Bob Kelley

Thomas Halvorson

Computer-implemented methods, systems, and computer readable media are provided for identifying and optimizing performance drivers of a healthcare related system. Healthcare related data may be analyzed to produce performance information pertaining to performance indicators for performance drivers of the healthcare related system. From the performance information, changes in the sets of performance indicators over time for the performance drivers may be determined and performance drivers with determined changes satisfying a threshold may be identified. An impact of the determined changes in the performance indicators to the identified performance drivers and contributions to the determined impact from one or more factors may be identified. Factors of the identified performance drivers with opposing utilization trends may be identified and an impact of the identified factors on the performance drivers may be determined. The identified performance drivers may be ranked and use of the performance drivers may be adjusted based on the ranking.

Searching and detecting interpretable changes within a hierarchical healthcare data structure in a systematic automated manner

Multi-step statistical modeling in one embodiment of the present disclosure enables anomaly detection, forecasting and/or root cause analysis of the energy consumption for a portfolio of buildings using multi-step statistical modeling. In one aspect, energy consumption data associated with a building, building characteristic data associated with the building, building operation and activities data associated with the building, and weather data are used to generate a variable based degree model. A base load factor, a heating coefficient and a cooling coefficient associated with the building and an error term are determined from the variable based degree model and used to generate a plurality of multivariate regression models. A time series model is generated for the error term to model seasonal factors which reflect monthly dependence on energy use and an auto-regressive integrated moving average model (ARIMA) which reflects temporal dependent patterns of the energy use.

Anomaly detection, forecasting and root cause analysis of energy consumption for a portfolio of buildings using multi-step statistical modeling

Structural changes in causal relationship over time may be detected, for example, by a Markov switching vector autoregressive model that detects and infers the structural changes in the causal graphs.

Change point detection in causal modeling

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White Plains, NY, United States of America

Huijing Jiang