Hospitals are constantly trying to improve the quality of care they deliver. Machine learning (ML) is creating disruptive changes in the healthcare industry. ML was first applied to tailoring antibiotic dosages for patients in the 1970s.. Python developers can build solutions that benefit human health and well-being. Python and its frameworks work on principles agreed upon the HIPAA checklist.
Some of the practical areas for ML are:
- Medical image processing and anomaly detection
- Managing Hospitals and Patient Care
- Diagnostics
- Disease Prognosis Prediction
- Natural Language Processing (NLP) for Administrative Tasks
- Patient Risk Identification
Computer vision methods have long been employed to automatically analyze biomedical images. The recent advent of deep learning has replaced many other machine learning methods, since it avoids the creation of hand-engineering features; removing a critical source of error from the process. Newer and faster GPU s allows to faster analyses to unprecedented amounts of data .
Indian hospitals are adopting Microsoft Cloud and AI for eye care screening for children . State governments are adopting Microsoft Intelligent Network for Eyecare (MINE) which is an AI platform to reduce avoidable blindness. Microsoft has taken steps with India to further improve healthcare in India using AI. Their effort is to predict the risk of cardiovascular disease with AI-powered risk score API for heart disease. This API develops an Indian heart risk score by using ML and AI to analyse cardio-vascular health records.
Google’s new AI scans a person’s retina to detect risk of heart disease. The deep learning algorithm was trained on data from 284,335 patients. Validated on two independent DataSets and has an accuracy of 70%
Roam Analytics uses comprehensive contextual data and ML to empower Bio-pharmaceutical and Medical device companies with the predictive insights. AiCure is an NIH and VC-founded healthcare startup. AiCure uses artificial intelligence to visually confirm medication ingestion.
XGBoost is a new Machine Learning algorithm designed with speed and performance in mind. XGBoost stands for eXtreme Gradient Boosting and is based on decision trees.. Healthcare is the most challenging to work with and a great field to make a positive impact on the lives of people every day. After having models detecting brain anomalies, early signs of diabetes and heart diseases, we now have a deep learning-based system that detects injury in knee joints (with a higher sensitivity than experts).
Parkinson's disease is a nervous disorder that affects movement. Symptoms start gradually, sometimes starting with a barely noticeable tremor in just one hand. Tremors are common, but the disorder also commonly causes stiffness or slowing of movement. The cause of Parkinson's disease is unknown.
Let us write python code to detect Parkinson
Data file : https://archive.ics.uci.edu/ml/machine-learning-databases/parkinsons/
# Detecting Parkinson’s Disease using XGBoost
import numpy as np
import pandas as pd
import os, sys
from sklearn.preprocessing import MinMaxScaler
from xgboost import XGBClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
data_file=pd.read_csv('e:\\Data\\parkinsons.data')
data_file=pd.read_csv('e:\\Data\\parkinsons.data')
data_file.head()
features=data_file.loc[:,data_file.columns!='status'].values[:,1:]
labels=data_file.loc[:,'status'].values
scaler=MinMaxScaler((-1,1))
x=scaler.fit_transform(features)
y=labels
x_train,x_test,y_train,y_test=train_test_split(x, y, test_size=0.2, random_state=7)
model=XGBClassifier()
model.fit(x_train,y_train)
y_pred=model.predict(x_test)
print(accuracy_score(y_test, y_pred)*100)
Predictive analytics is still young and there will be huge improvements in the field in the coming years.
Healthcare industry is undergoing a transformation. Government mandates combined with public demands are changing the way healthcare is delivered, consumed, and reimbursed. The implementation of electronic medical records (EMR) and wearable create a tremendous amount of Data that can fuel initiatives to provide better care while reducing costs.
OpenBCI is a brain-computer interface platform that provides access to high-quality brain wave data. OpenBCI has a growing community of scientists, engineers, designers, makers, and a whole bunch of other people who are interested in furthering our understanding of the brain.