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The MK&M Big Data Science Institute, established in collaboration with Mr. Morris Kahn, harnesses technological ingenuity to advance precision medicine.
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We specialize in providing high quality and expedited clinical trials, thanks to our ability to optimize the participant recruitment process.
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This Department uses the Maccabi database to derive an enhanced understanding of the causes, incidence and frequency of disease – leading to groundbreaking medical accomplishments.
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Aiming towards better personalized medicine, this regulatory-compliant and privacy-protecting repository of biological will enable the execution of a wide range of studies.
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Featured Projects

A mobile app providing personalized health information and recommendations, with Kang health.

The app was developed using machine learning, Deep Learning, complex NLP models, time-series analysis and Big Data systems in order to analyze the vast medical records of Maccabi.
K interacts with the user, asking specific relevant questions regarding the user’s medical condition, and then comparing the data with information from actual medical records. As a result, the user receives a list of relevant diagnoses, medications prescribed and medical exams performed in similar.
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Automatically identifying breast cancer using mammography images, with IBM Israeli research center.

Using image analysis technologies and deep learning techniques, we teach computers to detect breast cancer in mammography images. This ability can be used to assist doctors in decision making and later may be used, along with other patients’ data, to create predictive analysis algorithms to help

Identifying cardiovascular conditions by analyzing patients’ voices, with Beyond Verbal.

As part of the MK&M Big Data Science Institute, this research focuses on identifying vocal bio markers in chronic patients’ recordings and analyzing how they relate to their cardiovascular conditions.
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Drug repurposing for hypertension treatment with Kira Radinsky, Technion.

The purpose of this joint study is to create drug repurposing: to identify concomitant drugs not taken for hypertension that appear to contribute to success in lowering blood pressure.

A New Platform The World Can Use

As more and more of medical research incorporates Big Data as one of its foundations, it became clear that there is a need for a better way for researchers all over the world to have an easy access to well analyzed medical data.The Research and Innovation Institute provides a user-friendly platform which enables researchers from around the world an easy access to Maccabi’s comprehensive medical databank, along with specifically developed tools that offer them the option to choose, gather and analyze the data required for their specific research.
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MaccabiTech in Numbers

2,500,000

Patient medical files

45

Team Members

200

Publications

Featured Publications |

Predicting the presence of colon cancer in members of a health maintenance organisation by evaluating analytes from standard laboratory records

Goshen R1, Mizrahi B2, Akiva P2, Kinar Y2, Choman E1, Shalev V3, Sopik V4, Kariv R5,6, Narod SA4,7

A valid risk prediction model for colorectal cancer (CRC) could be used to identify individuals in the population who would most benefit from CRC screening. We evaluated the potential for information derived from a panel of blood tests to predict a diagnosis of CRC from 1 month to 3 years in the future.

We abstracted information on 1755 CRC cases and 54 730 matched cancer-free controls who had one or more blood tests recorded in the electronic records of Maccabi Health Services (MHS) during the period 30-180 days before …

Machine learning of big data in gaining insight into successful treatment of hypertension

Koren G1, Nordon G2, Radinsky K2, Shalev V1.

( Adv Ther )

Despite effective medications, rates of uncontrolled hypertension remain high. Treatment protocols are largely based on randomized trials and meta-analyses of these studies. The objective of this study was to test the utility of machine learning of big data in gaining insight into the treatment of hypertension. We applied machine learning techniques such as decision trees and neural networks, to identify determinants that contribute to the success of hypertension drug treatment on a large set of patients. We also identified concomitant drugs not considered to have antihypertensive activity, which may contribute to lowering blood pressure (BP) control. Higher initial BP predicts lower…

Multi-disciplinary patient-centered model for the expedited provision of costly therapies in community settings: the case of new medication for hepatitis C

Avisar N1, Heller Y2, Weil C3, Ben-Baruch A2, Potesman-Yona S2, Oren R4, Chodick G3,5, Shalev V3,5, Ash N3

In January 2015, the first interferon-free direct-acting antiviral (DAA) therapy for chronic hepatitis C virus (HCV) infection was approved for inclusion in Israel’s national basket of health services. During 2015, HCV genotype 1 patients with advanced liver fibrosis (stage F3-F4) were eligible for treatment with ombitasvir/paritaprevir/ritonavir and dasabuvir (OMB/PTV/r + DSV) provided through the four…

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