AI for Social Good: Predicting Failure for Water Pumps in Tanzania using Automated Machine Learning Techniques.

Figure 1. 350 million people in Africa still need access to safe water supply sources. Credit:


Different applications of artificial intelligence (AI) and machine learning (ML) have been proposed to help address environmental and social challenges. Among these challenges, there is an increasing global concern about the availability and access to fresh water. It is estimated that 663 million people globally currently lack access to safe water supply sources (with 350 million people in Africa alone affected everyday [1]). Future projections are also worrisome (according to the United Nations, the global demand for fresh water will exceed supply by 40% by the year 2030 [2]).

Let’s consider the issue of access to water supplies in sub-Saharan…

Figure 1.- Exoplanet representation. Credit: NASA/JPL-Caltech


Artificial Intelligence (AI) and Machine Learning (ML) are exciting technologies that are changing the way many complex problems are being solved in multiple sectors and industries. However, many companies still perceive AI and ML as “inaccessible” techniques that somehow require deep theoretical knowledge and specialized programming skills. For this reason the concept of AutoML (or automated machine learning) has recently arisen to greatly simplify the process of building and deploying machine learning solutions. Unfortunately, even some of the most practical AutoML libraries available out there require time/effort (plus some data-science experience) until a good/satisfactory predictive model can be obtained.


(This article is based on a recent paper that was published at Respiratory Care and authored by David Castiñeira, Katherine R Schlosser, Alon Geva, Amir R Rahmani, Gaston Fiore, Brian K Walsh, Craig D Smallwood, John H Arnold and Mauricio Santillana)

What: We created a machine learning-based approach capable of extracting meaningful information from continuous-in-time vital sign information from bedside monitors, from the first 24 h of a subject’s ICU stay while on mechanical ventilation, to predict prolonged LOS. Our findings showed that combining subjects’ static clinical data and continuous-in-time data from vital signs led to improved predictions. The framework…

Courtney Cochrane, David Castiñeira, Nisreen Shiban and Pavlos Protopapas (Institute for Applied Computational Science, Harvard John A. Paulson School of Engineering and Applied Sciences, Cambridge, MA, US)


Alzheimer’s Disease (AD) ravages the cognitive ability of more than 5 million Americans and creates an enormous strain on the health care system. Our paper proposes a machine learning predictive model for AD development without medical imaging and with fewer clinical visits and tests, in hopes of earlier and cheaper diagnoses. That earlier diagnoses could be critical in the effectiveness of any drug or medical treatment to cure this disease. …

David Castiñeira

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