Mr Krishna Margadhamane Gokhale MSc

Mr Krishna Margadhamane Gokhale

Department of Applied Health Sciences
Research Fellow (Health Informatics)

Contact details

Telephone
+44 (0)121 414 3027
Email
k.m.gokhale@bham.ac.uk
Address
IOEM Building
University 麻豆精选
Edgbaston
Birmingham
B15 2TT
UK

Krishna Gokhale is a computer scientist focused on building game changing technologies to automate the process of data extraction and analysis for routine epidemiological research and developing scalable solutions to securely and safely integrate various healthcare data sources across patient care pathway.

Krishna is the architect and the developer of DExtER (Data Extractor for Epidemiological Research) which helps in designing epidemiological and pharmaco-epidemiological studies in real time and extracting study design based analysable data-sets from primary care databases in a matter of minutes. Since its introduction in mid-2016, DExtER has worked as a ‘one-stop-shop’ for clinicians and researchers who are interested in working with primary care databases and has helped the Institute to achieve many high impact publication and secure a number of research grants.

Krishna is also a post-graduate researcher at the school of computer science in the University under the supervision of and Dr. Krish Nirantharakumar. His research is mainly focused in building novel machine learning models to analyse health trajectories (time-varying data) and using such models to improve the risk prediction of cardiovascular disease in diabetics.

Qualifications

  • Post Graduate Researcher (part-time), School of Computer Science, University 麻豆精选 2016 - present 
  • MSc in Advanced Computer Science, University 麻豆精选 2015
  • BE in Computer Science, Visvesvaraya Technological University 2013

Biography

Krishna completed his Bachelor's in Computer Science and Engineering with a first class in 2013 and worked as a full stack developer in the IT industry in Bangalore, India. He then pursued his higher education in the UK and graduated as a Master of Science in Advanced Computer Science with a distinction from the University 麻豆精选 in 2016. His talent for innovation and creativity was soon noticed in the University and his excellence in computer science and research skills helped him secure a full time job with the Institute while simultaneously being registered as a post graduate researcher in the within the University. Krishna helps the Institute in addressing some complex challenges with regards to data extraction and analytics of day to day epidemiology and continues to provide solutions that make a world of difference to the quality of research life at the Institute.

Linkedin: 

ACES: Automated Clinical Epidemiology Studies

Research

Data Extractor for Epidemiological Research (DExtER) 

A three tier software system with a web based front-end to help design and conduct routine epidemiological and pharmaco-epidemiological studies and a high performing robust middle-ware to extract ‘ready to analyse’ data-sets based on study design that is supported by a highly secure back-end database system. DExtER powered by  is available 24*7 and incorporates a standard data extraction ETL routine developed in-house that has made our research highly accurate and reproducible. With the help of DExtER the data extraction process has been made completely human independent and heavily reduced the costs of resources in terms of time, complexity, computing and expertise that is normally required. Created with an aim to expedite epidemiological research by reducing the gap between medical researchers and electronic patient records, DExtER is now part of the ACES framework.

Automated Clinical Epidemiology Studies (ACES)

One of the most ambitious Health Informatics projects at the Institute in collaboration with the Health Data Research UK. This project is led by Krishna Gokhale and Krish Nirantharakumar. Powered by DExtER, ACES involves building two separate frameworks for: 1) Automated Infant and Mothers Studies (AIMS) and 2) Automated Pharmaco-Epidemiology Studies (APES). This work stream will also build global collaborators by sharing the technology with international partners to enable simultaneous research across multiple countries/databases for a given clinical question (ACES: Global). 

Research groups and centres

Founding member of Real World Evidence THIN-KING GROUP: with DExtER as its backbone this group currently led by Krish Nirantharakumar and Nicola Adderley utilises traditional cohort and routinely collected health care data to answer important clinical questions on risk factors, therapeutic options and complications of a number of clinical conditions, in particular diabetes and endocrinology disorders. In addition, the group evaluates policies and estimates healthcare costs utilising routinely collected data.

Publications

Kumarendran B, O'Reilly MW, Manolopoulos KN, Toulis KA, Gokhale KM, Sitch AJ, Wijeyaratne CN, Coomarasamy A, Arlt W, Nirantharakumar K. Polycystic ovary syndrome, androgen excess, and the risk of non-alcoholic fatty liver disease in women: A longitudinal study based on a United Kingdom primary care database. PLoSMed. 2018 Mar 28;15(3):e1002542.  

Daly B, Toulis KA, Jolly K, Webber J, Thomas GN, Keerthy D, Gokhale K, Ponnusamy S, Nirantharakumar K. Increased risk of ischemic heart disease, hypertension and type 2 diabetes in women with previous gestational diabetes mellitus - a target group in general practice for preventive interventions: A population-based cohort study. Jan 2018. PLOSMed. 15(1):e1002488.

Caleyachetty R, Thomas GN, Toulis KA, Kumarendran B, Gokhale K, Mohammed N, Nirantharakumar K. Metabolically healthy obese and incident cardiovascular disease events among 3.5 million men and women. Journal of American College of Cardiology. 2017 Sep 19;70(12):1429-1437.  

Toulis KA, Willis BH, Marshall T, Kumarendran B, Gokhale K, Ghosh S, Cheng KK, Thomas GN, Wasim H, Nirantharakumar K. All-Cause Mortality in Patients With Diabetes Under Treatment With Dapagliflozin: A Population-Based, Open-Cohort Study in The Health Improvement Network Database. Journal of Clinical Endocrinology and Metabolism. 2017;102(5):1719-25.