Indiana University

Ohio State Innovation Foundation

Bo Peng

Xia Ning

XiaoFeng Wang

Yangyi Chen

Haixu Tang

Titus K Schleyer

Zhiyun Ren

Disclosed embodiments include computer-implemented methods and systems that can efficiently generate highly relevant recommended search terms to clinicians. A hybrid collaborative filtering model recommends search terms for a specific patient to the clinician. The model draws on information from patients' clinical encounters and the searches that were performed during the clinical encounters. To generate recommendations, the model uses search terms which are (1) frequently co-occurring with the ICD codes recorded for the patient and (2) highly relevant to the most recent search terms. One variation of the model uses only the most recent ICD codes assigned to the patient. Another variation uses all ICD codes. Comprehensive experiments of embodiments of the methods and systems have demonstrate high levels of performance.

Systems and methods for providing health care search recommendations

System and methods are provided for performing privacy-preserving, high-performance, and scalable DNA read mapping on hybrid clouds including a public cloud and a private cloud. The systems and methods offer strong privacy protection and have the capacity to process millions of reads and allocate most of the workload to the public cloud at a small overall cost. The systems and methods perform seeding on the public cloud using keyed hash values of individual sequencing reads' seeds and then extend matched seeds on the private cloud. The systems and methods are designed to move the workload of read mapping from the extension stage to the seeding stage, thereby ensuring that the dominant portion of the overhead is shouldered by the public cloud.

Secure and scalable mapping of human sequencing reads on hybrid clouds

Growing community of inventors

Bloomington, IN, United States of America

Bo Peng