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Created by

Rishabh Sharma, Frontend and Solidity Developer. LinkedIn

Jade Shyu, UI/UX Designer and Business Research. LinkedIn

Reid Sherman, Frontend and Solidity Developer. LinkedIn

Adrian Jewell, Full-stack Developer and Machine Learning Contributor. LinkedIn

Richen Zhang, Machine Learning.

PharmaChain. Medical Logistics Verified

Pharma Chain makes relations between health-manufacturers and health-care-providers more streamlined using Blockchain and Machine Learning.

Features

  1. Intuitive UI using Material UI and Drag-Drop File Upload

  2. Smart Contracts on the Ethereum Blockchain Ledger for Contract Management.

  3. Recursive Neural Network to Predict Future Inventory.

Implementation details

Intuitive UI and Design

Smart Contracts on the Ethereum Blockchain Ledger for Contract Management between Manufacturer and Hospital.

Recursive Neural Network to Predict Future Inventory Requirements.

A neural network offers predictions for new situations after having been 'trained' on old data. Training requires a human to parse data and determine the correct answer for each data point. The human then trains a neural network by feeding it data, observing the input, and then adjusting the weights inside the matrix such that the output in more closely aligned with the correct answer. After doing this many times, and with many different inputs, the neural network can then output a correct prediction for a novel case.

In this particular instance of a neural network, previous input is CDC 'Fluview' data about flu outbreaks in the state of California, broken into the categories: Number of Patients, Precent Positive, and Number of Providers. The output of the neural network is the total Specimens Collected. From this datapoint, one can calculate the amount of supplies that a hospital might need to supply their facility for the upcoming flu season.

This specific instance of a neural network is a recursive neural network, which directly relies on the result of previous data inputs to predict future inputs. A keras model run on top of Tensorflow GPU processing library to make predictions.

model = Sequential()

model.add(LSTM(hidden_dim, input_shape=(time_step, feature_dim),  
return_sequences = False))

model.add(Dense(100, activation='softmax'))

model.compile(loss = 'mean_squared_error', optimizer = 'rmsprop', metrics = ['accuracy'])

model.fit(x_train, y_test, num_epochs, num_batch, validation_data = (x_test, y_test))

Pharmacy Logistics

  • stack: Node.js, react, solidity, web3

To Run:

To setup:

  1. install npm if you don't already have it https://nodejs.org/en/
  2. run npm install

To start, first run testRPC server, then run app server.

  1. run npm run testRPC
  2. run npm start
  3. navigate to localhost:3000