The Cough for TB HCW app is an AI-powered cough-sounds-based screening solution to assess the likelihood of pulmonary tuberculosis in individuals, for the early detection and treatment of tuberculosis (TB). This app is intended to be used by healthcare workers (HCW).
HCWs designated by the National TB Elimination Program (NTEP). The subjects can be the general population of India from 18–100 years of age, who are not on a TB treatment regimen.
The app can be installed on any smartphone with the following minimum specifications:
The app registers and authenticates HCWs using personal information, NTEP programmatic information, and a one-time password (OTP) delivered via SMS.
Personal and symptomatic data for each subject needs to be input, as per the instructions provided in the app. HCWs should refer to Annexure 1 and Annexure 2 for safety measures.
Before recording the cough sounds, please ensure the following for each subject:
The app, based on the input cough sounds and data, provides results as follows:
Likely to have pulmonary tuberculosis
Based on the automated inference, the individual’s chances of having pulmonary tuberculosis are high and they should consult a doctor and get tested to confirm the diagnosis.
Not likely to have pulmonary tuberculosis
Based on the automated inference, the individual’s chances of having pulmonary tuberculosis are low and they should consult a physician if symptoms continue.
For all smartphones used for data collection, we shall follow the below recommendations issued by the CDC.
The following measures will be undertaken to prevent infection during data collection.
IPC is a critical and integral part of data collection from patients, as per the guidelines of the Ministry of Health and Family Welfare (MoHFW).
Standard precautions will always be routinely applied, including hand hygiene, avoiding direct contact with patients’ respiratory secretions, standard precautions for safe waste management and cleaning and disinfection of equipment (including data collection devices).
Implementation of infection prevention and control measures for patients with suspected or confirmed nCoV infection as per the guidelines of the MoHFW.
ROLES AND RESPONSIBILITIES
An ML Engineer at Wadhwani AI will be responsible for building robust machine learning solutions to problems of societal importance; usually under the guidance of senior ML scientists, and in collaboration with dedicated software engineers. To our partners, a Wadhwani AI solution is generally a decision making tool that requires some piece of data to engage. It will be your responsibility to ensure that the information provided using that piece of data is sound. This not only requires robust learned models, but pipelines over which those models can be built, tweaked, tested, and monitored. The following subsections provide details from the perspective of solution design:
Early stage of proof of concept (PoC)
This is early to mid-stage of AI product development
Responsibilities during production deployment
We realize this list is broad and extensive. While the ideal candidate has some exposure to each of these topics, we also envision great candidates being experts at some subset. If either of those cases happens to be you, please apply.
Master’s degree or above in a STEM field. Several years of experience getting their hands dirty applying their craft.
ROLES AND RESPONSIBILITIES
As an ML Scientist at Wadhwani AI, you will be responsible for building robust machine learning solutions to problems of societal importance, usually under the guidance of senior ML scientists. You will participate in translating a problem in the social sector to a well-defined AI problem, in the development and execution of algorithms and solutions to the problem, in the successful and scaled deployment of the AI solution, and in defining appropriate metrics to evaluate the effectiveness of the deployed solution.
In order to apply machine learning for social good, you will need to understand user challenges and their context, curate and transform data, train and validate models, run simulations, and broadly derive insights from data. In doing so, you will work in cross-functional teams spanning ML modeling, engineering, product, and domain experts. You will also interface with social sector organizations as appropriate.
Associate ML scientists will have a strong academic background in a quantitative field (see below) at the Bachelor’s or Master’s level, with project experience in applied machine learning. They will possess demonstrable skills in coding, data mining and analysis, and building and implementing ML or statistical models. Where needed, they will have to learn and adapt to the requirements imposed by real-life, scaled deployments.
Candidates should have excellent communication skills and a willingness to adapt to the challenges of doing applied work for social good.