The data generated by clinical and fundamental research, clinical practice, and diagnostic setups along and devices used by patients are being fed into AI algorithms-driven analytics engines and meaningful patient-specific outcomes are being derived. AI-enabled Healthcare is soon a norm in immediate future, and it is already used in diagnosis and predictive healthcare.
Sequoia Insilico is a cognitive healthcare company founded on the principles of cognitive computing systems. In complex situations where the solutions may be ambiguous and uncertain, cognitive computing uses computerised models to simulate the human thought process and act independently. Cognitive computing systems aid in knowledge expansion increased productivity and increased levels of expertise by understanding, reasoning, and learning on their own.
To assist patients in obtaining individualized, patient-centered, continuous, and affordable health care. To provide clinicians and health care experts with actionable information and improved treatment efficacy, saving valuable time and increasing satisfaction.
Our vision is to develop a venture which successfully implements P4 components in all aspects of healthcare, enhancing accuracy and precision, to ensure better treatment outcome. We aim to augment health in health care.
The most important step is to clearly identify the problem and the root cause of a problem by developing detailed problem statements, which include the problem's impact on population health and how it can be resolved with a viable technological intervention..
An effective solution, which may not be perfect but is incremental over existing solutions, necessitates a resource assessment that includes technical feasibility, intellectual capabilities and access to required infrastructure, data availability, hospitals, and stakeholder motivation to resolve the identified problem..
The step entailed determining the feasibility of developing a solution based on available physical, intellectual, and financial resources, identifying challenges, and developing hedging strategies..
Acquiring quality data, developing, and building models is a critical step in the data science industry. Our strategy is to develop and design formal collaborative research projects with COEs, following all ethical constraints..
The step involves several crucial sub steps in terms of contextualizing, data cleaning, choosing and developing data and objective compatible algorithms, spilling datasets for cross validation and performing optimization. We deploy the model for pilot study after publication, and patent filing..
Reviewers play an important role in scientific, scholarly publishing. The peer review system helps to validate academic work, improve the quality of published research, and bring in trust in the work, in terms of novelty, authenticity of the research work and findings..
LifeBack is a bio-neuro-psychiatry platform that offers a cost-effective, multidisciplinary, proactive, validated ML-system-based approach to mental health care by enhancing.
Cerebral palsy (CP) is a group of disorders that affect a person's ability to move and maintain balance and posture Cerebral Palsy is generally attributed to non-progressive insults to the developing foetal or infant brain.
At the inception stage, the twin goals of the Thal-AIM project are to develop an assessment tool to rule out Thalassemia Major incidence in young kids at an early stage and to develop a module to evaluate.
Automatic detection of ROP Deep learning.
Retinopathy of prematurity (ROP) is an eye disease that can occur in premature (early) babies weighing less than 3 pounds at birth..
We follow strict ICMR guidelines to generate and validate data in ICMR notifies research centres and hospitals under collaborative research project, with ethical clearance.
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A PhD in Bioinformatics, JNU, entrepreneur, researcher, fund raiser, business developer, mental health advocate and believes that health care should be accessible to all at affordable cost beyond physical boundaries
Harsh Bhasin is currently associated with Manav Rachna University. He has published more than 40 papers in various journals including Alzheimer’s and Dementia, and BMC Medical Informatics, and conferences including BIBE. He has authored more than 11 books including those published by Oxford University Press, and BPB. He has worked as a Deep Learning consultant for various firms and taught in various Universities including Jamia Hamdard and DTU. His areas of expertise include Deep learning, Algorithms, and Medical Imaging.
A Ph.D. student (Adaptive event sampling and Machine learning) at the International Centre for Neuromorphic Systems, MARCS Institute, Western Sydney University, developing methods & tools to improve feature extraction and deep learning techniques by using thresholding-based periodic activation functions. Master of Data Science from Western Sydney University. Apart from the ongoing Ph.D. Vishal, works avidly to use mathematical and coding skills to develop concepts for impactful medical applications.
Master’s in computational mathematics (JNU), Researcher, Avid learner of Machine Learning Application.
