REFERENCE TO SEQUENCE LISTING, A TABLE, OR A COMPUTER PROGRAM LISTING COMPACT DISC APPENDIX

Not Applicable

BACKGROUND OF THE INVENTION

1. Field of the Invention

This invention relates to medical informatics, in particular utilizing clustering and probabilistic techniques to identify optimum treatment paths given a selected outcome target.

2. Description of the Prior Art

Techniques used in this solution are related to Latent Dirichlet Allocation (LDA) by David M. Blei and an extension of it called Clinical Pathway model (CPM) by Zhengxing Huang. These disclose methods of extracting “topics” from a corpus.

In one current view of providing treatment to patients, treatment centers such as hospitals follow clinical treatment paths or patient traces that are characterized by a sequence of treatments or clinical activities recommended by a physician. Clinical treatment paths are standardized, evidence-based management plans based off of collective knowledge about how a homogenous patient group reacts to a set of care processes. It may be best if the physicians take into account the prior history of the patient, demographic data, disease stage, symptoms, patients' reactions to treatment and a set of other numerous factors in order to determine how the patient may react to the standardized care map. However, humans cannot create associations among hundreds of thousands of data, extract patterns and use that extracted information to change the standardize care map in minor or major ways to improve the outcome or to recommend a treatment path.

The disclosed solution addresses shortcomings found in existing solutions by trying to solve the problem of optimizing the treatment recommendations by physicians so that the treatment meets desired outcomes.

The solution builds on the disclosures of: (a) Latent Dirichlet Allocation (LDA) by David M. Blei at http://www.cs.columbia.edu/˜blei/papers/BleiNgJordan2003.pdf (“Blei”), and (b) Clinical Pathway Model (CPM) by Zhengxing Huang at http://www.sciencedirect.com/science/article/pii/S1532046413001445 (“Huang”). Both Blei and Huang references are herein incorporated by reference in their entirety.

These disclosures describe methods of extracting treatment topics from historical treatment data of patients and clustering the patients according to the “topic content” of each patient. Blei describes “topic extraction” methods and Huang describes “clustering” methods suitable for use with some of the embodiments described herein. The LDA of Blei is a generic topic extraction analysis (not dependent on treatments). Blei does not include the time component in the topic extraction. Huang is an extension of the Blei paper such that it includes the time component and it is also an application to treatment topic extraction from medical data.

In general, Blei describes the use of LDA and Huang describes the application of LDA to clinical pathways. However, these papers have not considered time in topic extraction and they have not extended the use of this type of cluster information to help health care providers select treatment paths based on a desired outcome.

BRIEF SUMMARY OF THE INVENTION

The following summary is included only to introduce some concepts discussed in the Detailed Description below. This summary is not comprehensive and is not intended to delineate the scope of protectable subject matter, which is set forth by the description below and the claims presented at the end.

The systems and methods create “treatment path clusters” to identify the distinct groupings of clinical treatment paths to be applied to patients. The treatment paths and treatment path clusters may be statistically determined from historical treatment paths, or patient traces. Historically, every patient follows a patient trace and clusters from this historical patient trace data represent treatments that can be used alone or probabilistically grouped to be applied to future patients.

In one example embodiment, a specialized health care system for selecting a treatment path for a patient is provided, the system comprising an input device to receive historical patient trace data and patient data, a pattern extractor module configured to extract treatment patterns from the historical patient trace data, a cluster maker module configured to create one or more treatment path cluster given the historical patient trace data and the treatment patterns, a centroid maker module configured to create one or more treatment path cluster centroid from the one or more treatment path cluster, a binary classifier module configured to define and train a binary classifier, and a target outcome maximizer module configured to select the treatment path cluster centroid as the selected treatment path for the patient given a new patient data, the one or more treatment path cluster centroid, the binary classifier and a target outcome.

In some example embodiments, a specialized health care system for selecting a treatment path for a patient is provided, the system comprising a processor, a first input device to receive historical patient trace data corresponding to past treatments activities applied to one or more patients, a data formatter module, a memory configured to receive and store the formatted historical patient trace data and a formatted historical patient data, a plurality of treatment path modules, the memory further configured to receive and store a treatment path cluster and a treatment path cluster centroid, a binary classifier module configured to receive the formatted historical patient trace data, one or more target outcomes and the historical patient data to define and train a binary classifier, the memory further configured to receive and store the binary classifier, a target outcome maximizer configured to receive the treatment path cluster centroids, the new patient data and the binary classifier to select the treatment path cluster centroid as the selected treatment path for the patient and a user interface to present the selected treatment path.

In some embodiments, the data formatter module is configured to receive and transform the historical patient trace data into a formatted historical patient trace data, a historical patient data corresponding to the one or more patients into a formatted historical patient data, and a new patient data corresponding to a new patient into a formatted historical patient data.

In some embodiments, the plurality of treatment path modules comprises a pattern extractor module configured to extract treatment patterns from the formatted historical patient trace data, a cluster maker module configured to create one or more treatment path cluster given the formatted historical patient trace data and the treatment patterns, and a centroid maker module configured to create one or more treatment path cluster centroid.

In some embodiments, methods of using the specialized health care system are provided.

Other objects, features, and advantages of the techniques disclosed in this specification will become more apparent from the following detailed description of embodiments in conjunction with the accompanying drawings.

