Predictive Analytics is a branch of advanced analytics which is very useful for medical diagnosis in the healthcare industry. Historical data of former patient’s symptoms is applied to assess new patients and reduce misdiagnosis. In healthcare segment, it can cater to patient diagnosis by determining patients who are at risk of developing conditions for example asthma, diabetes, high blood pressure and similar lifetime diseases. Machine Learning (ML) is actively applied to diagnose human diseases. As ML operates on algorithms, healthcare specialists provide information to machines that can help them in imaging and analyze human bodies for abnormalities.
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Applications of Machine Learning in Patient Diagnostics
- Oncology: Deep learning algorithms can detect cancerous tissue at an early stage. ML helps doctors and surgeons to decide how intense a radiation would be required depending on how well the patient responds to specific amounts of emissions.
- Chatbots: Many companies nowadays use AI-powered chatbots with speech recognition capability to identify patterns in patient symptoms. These help in potential diagnosis, prevent disease or recommend an appropriate course of action.
- Data collection: If proper algorithms can collect the required information of patients, then doctors can personalize treatment techniques.
- Pathology: Smart machines and censors can scan through the body and can click images to detect diseases early on. Moreover, algorithms can help doctors and scientists improve drug performance and behaviour of the same on a test subject.
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Requirements of Machine Learning Systems
In order to solve patient diagnosis, machine learning algorithms must be well-versed and capable of dealing with the following points:
- Good Performance: The algorithm must be able to extract significant information from the available data source.
- Missing information: Often patients’ descriptions lack some useful data. The selected machine learning models should be able to interpret these incomplete descriptions.
- Noisy data: Along with missing information, data is also messy and noisy at times. The correct algorithms should have effective means to handle such noisy data.
- Reduced number of tests: Collection of patients’ data is often tedious and even expensive at times. Thus, it is always desirable to have classifier that is able to analyze small amounts of data. However, there are some ML systems which themselves can identify the correct set of features in the given dataset.
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Application in Patient Diagnosis
Predictive risk-profile models can be developed for identifying the risk profile of aged-care services. This is based on data such as pressure injuries, staff-to-patient ratios, qualified staff, wages, patient turnover, and profitability statistics. Remote patient monitoring and machine learning works hand in hand to support decisions made in hospitals through risk scoring as well as threshold alerts.
In medicine, predictive analytics plays a pivotal role. It enables the use of prognostic analytics to find cure for diseases which they might not be familiar with. Based on age, chronic illnesses, medication adherence, statistical tools can detect diabetic patients with probability of hospitalization in the upcoming years.
Data from pharmaceutical department can help target medicine shortages and refill stocks when necessary. Predictive Analytics can scan a patient’s genome to find whether or not they have a gene marker for early onset Alzheimer’s disease. Doctors can collect treatment data in an electronic medical record (EMR) for patient diagnosis.
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Lastly, with the onset of advanced technological developments, there is a need to maintain data privacy. Predictive Analytics has potential not only for patient diagnosis, but caregivers as well. It plays a fundamental role in improving people’s health and reducing mortality rates among people of all age groups. With the application of analytics, patients will become more informed and doctors can provide more precise treatment. Pharmaceutical companies will thus be able to offer more cost-effect and more effective medications.
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