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Quality Improvement and Deep Learning Strategies to Boost Patient Care
Science and medical sectors are rapidly progressing towards using data-intensive applications. An ongoing comparison of genomics with other data-intensive applications such as social media, online videos have showcased that genomics will outperform other applications in data generation within the next decade. The volume and multifaceted nature of these information present new chances, yet in addition, present new difficulties in analyzing the complex health care data.
Moreover, traditional systems fail to recognize diseases due to hidden patterns or human error. There is a requirement of efficient deep learning architecture that will assist doctors and clinicians to enhance disease recognition rate and assessments while minimizing existing limitations.
With the evolution of non-invasive medical imaging techniques, new possibilities have been developed concerning deep learning algorithms and demonstrated breakthrough gains over numerous machine learning techniques. The critical metrics considered during medical image classification will be in terms of accuracy, true positive rate, flexibility, and efficiency. Extracting meaningful patterns using automated deep learning algorithms could lead to achieving actionable knowledge, and provides a novel approach for treatment, distinguish patients and study their diseases, within privacy embedded critical environment.
Deep learning structure comprises of artificial neurons, known as the neural network. A neural network is categorized into the input layer, hidden, and output layers, all interconnected with each other. The input layer data is passive in nature. It comprises variables being measurable and similar to the user area of interest. Data captured in the form of medical images or estimated values will be in the form of raw data in undesired format. Initially, it is being pre-processed through data processing and conversion techniques to achieve noise-free data with outlier removal and resolved inconsistencies. This data is forwarded through the input layer, where the values have been relayed from their single input to multiple outputs and processed to hidden layers.
In the hidden layer, the input values multiplied with weights will be processed to form an optimal value. This optimal value is again added with sigmoid to achieve threshold output function at the output layer.
Nowadays, healthcare hi-tech companies are using deep learning strategy to predict and detect the diseases. Jivon and Cyft are some of the organizations providing technology-enabled platforms to assist doctors in effective decision making with the most appropriate care. Jivon develops the protocol prior to the cognitive machine eigen sphere engine, and target customer with value-based contracts. Cyft provides health services by identifying high-risk members with relevant risk factors and particular uplift model. With research going beyond the medical expectations, better advanced predictive models can be expected in the future medical diagnosis.