Group: 2014-FYP-16



Dr. Khalid Mehmood Ul Hasan


Pakistan ranks 149 th out of 188 in provision of health care facilities to its citizens as per the UN health goals. With an annual budget of 54 billion PKR (1 USD = 105 PKR) for health- care which is around 0.002% of national GDP, even the basic primary health care services are not guaranteed to each individual in certain parts of the country. In contrast US, UK and Germany spend over 3.5 trillion USD, 164 billion USD and 236 billion USD, respectively, on health care alone, annually. The per capita allocation is further squeezed by a booming population of over 220 million that has left the entire nation reeling to even maintain the current percentage GDP spending on health. Rapid developments in health information technology have provided a direly needed window of opportunity to overcome this incumbent health care debacle in Pakistan. Pakistan now needs to leverage the advancements and innovations in this sector towards provision of high-quality ubiquitous health care services to its population. In this project, we propose to contribute towards this goal with the development of a sensor- integrated selfdiagnosis software suite packaged as a smartphone application which is seamlessly integrated with the primary, secondary and tertiary health care centres across the country. We have integrated state of the art clinical guideline ensembles obtained from Guidelines International Network (GIN) consortium with comprehensive clinical knowledge from salient global health providers for Diabetes, Hepatitis c, Pneumonia and Cardiovascular conditions. These four conditions comprise the bulk of clinical mortalities in Pakistan. This fact coupled with the prevailing scarcity of clinical consultant positions the proposed project to induce a disruption in Pakistani health-care ecosystem. The core of the project is formed by a mobile application for self-diagnosis which is driven by a cohort of machine learning techniques such as Bayesian network inference, neural networks and support vector machine working off the GIN provided clinical guidelines. Application usability is enhanced by provision of multiple local languages and audio/visual translations or aids. Complementing the app is a seamlessly integrated set of wearable biosensors that continuously input numerical and categorical data for use in diagnosis. A question engine then performs sensor data fusion and prompts the subject for requisite information. Data and diagnoses thus formed are collected on cloud for deep learning of critical parameters as well epidemiological trends during training. With the increase in subscribers and resultant increase in available data sets, the sensitivity and specificity of the system is set to improve and lead to updates in underlying models. In conclusion, we have proposed a nextgeneration health-care system by shifting focus to patient self-diagnosis, instead of primary or secondary health care units, by building upon ever improving data mining techniques and wearable biosensors for provision of health-care to every citizen of Pakistan.

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