The Future of Healthcare And AI Impact – Analytics India Magazine

Artificial Intelligence plays an important role in the pharmaceutical industry and the coming years there is simply no sign of the adoption of this cutting-edge technology slowing down. From making healthcare process automated to help in drug discovery, AI with machine learning can bring revolution in this industry. The key customer-oriented areas where AI is being implemented within the sector are the following:

Through natural language processing, audio and video files are transcribed from voice to text. These files shall be obtained from video-recordings from patients and customers speaking providing their opinion about a particular product or service. The dataset must be considerably large more than 300 audio-video files in order to assure accuracy. The larger the amount of datapoints, the better results that will be obtained.Within that process, an intelligent platform performs a sentiment analysis, which means the platform mines for a series of keywords or statements, as well as the demographics of the speaker (including gender and, possibly, age).Post-transcription, that data is categorized and classified, ready for analysis based on the chosen parameters.

Machine Learning uses diverse approaches to the creation of autonomous and supervised Neural Network-based speech recognition and translation systems. The two vanguard approaches in this period are Long Short-Term Memoryand CNN. The LTSM network or RNN has an 82 per cent accuracy score, while the vision-based Convolutional Neural Network scores 95 per cent accuracy.

Every Machine Learning algorithm takes a dataset as input and learns from this data. The algorithm goes through the data and identifies patterns in the data. For instance, suppose we wish to identify whose face is present in a given image, there are multiple things we can look at as a pattern:

Clearly, there is a pattern here different faces have different dimensions like the ones above. Similar faces have similar dimensions. The challenging part is to convert a particular face into numbers Machine Learning algorithms only understand numbers. This numerical representation of a face (or an element in the training set) is termed as afeature vector. A feature vector comprises of various numbers in a specific order.

As a simple example, we can map a face into a feature vector which can comprise various features such as:

Essentially, given an image, we can map out various features and convert it into a feature vector as:

So, our image is now a vector that could be represented as (23.1, 15.8, 255, 224, 189, 5.2, 4.4). Of course there could be countless other features that could be derived from the image (for instance, hair colour, facial hair, spectacles, etc). However, for the example, let us consider just these 5 simple features.

Machine Learning can help us here with 2 key elements:

What is the relationship between machine learning and optimization? On the one hand, mathematical optimization is used in machine learning during model training, when we are trying to minimize the cost of errors between our model and our data points. On the other hand, what happens when machine learning is used to solve optimization problems?

In simple terms, we can use the power of machine learning to forecast travel times between each two locations and use the genetic algorithm to find the best travel itinerary for our delivery truck. The following parameters need to be followed:

Here, you can predict who, and when an employee will terminate the service. Employee churn is expensive, and incremental improvements will give significant results. It will help us in designing better retention plans and improving employee satisfaction. This will be measured through the following attributes:

We will build a model that automatically suggests the right product prices. We are provided of the following information:

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The Future of Healthcare And AI Impact - Analytics India Magazine