Writing Homework Help

please answer with 150-200 words each DQ reply. thank you

Paula:

Electronic healthcare records (EHRs) generate large amounts of data available for analysis. The use of various computer programming applications allows for sorting and analyzing data for a wide range of uses. Healthcare providers are realizing the wealth of knowledge buried within the EHRs, and with the new field of medical informaticists, are exploring various ways to utilize the data at hand (Akundi, Madhuri, 2020). (Sidney-Gibbons & Sidney-Gibbons, 2019).

Machine learning refers to the “collection of techniques which allow computers to undertake complex tasks” (Sidey-Gibbons & Sidey-Gibbons, 2019). Open-source programming such as Python has promoted increasingly complex algorithms allowing computers to find patterns in data, as well as diagnosing and predicting disease processes and outcomes (Warin, Lindvall, Umeton, 2020).

Supervised machine learning is when the computer is trained to statistically compute or predict an outcome based upon concrete variables associated with a known outcome (Sidey-Gibbons & Sidey-Gibbons, 2019). Once the algorithm for prediction has been developed and tested, it can be used to predict possible outcomes within a health care setting. An example that I see at work, is the sepsis protocol. The screen pops up on the patient’s EHR alerting the providers, nurses that certain patient conditions have been met, and they need to evaluate the patient for possible sepsis/septic shock. The notification is dismissed once an evaluation by a medical professional has taken place.

Unsupervised computer learning, the outcome is not predicted, but rather the computer independently generates patterns or clusters within the data (Sidey-Gibbons & Sidey-Gibbons, 2019). This type of query will help to identify patterns or clusters in big data, and potentially identify contributing variables affecting health that would otherwise be undiscernible. An example unsupervised learning could be identifying a pattern of medication use within a population and then could be further investigated as a possible variable affecting patient outcomes.

References

Akundi, S.H., Maduri, S. (2020). Big Data Analytics in Healthcare Using Machine Learning Algorithms: A Comparative Study. International Journal of Online & Biomedical Engineering, 16(3), 19-32. https://doi-org/10.3991/ijoe.v16i13.18609

Sidey-Gibbons, J.A.M., Sidey-Gibbons, C.J. (2019). Machine learning in medicine: a practical introduction. BMC Medical Research Methodology, 19(64). https://doi.org/10.1186/s12874-019-0681-4

Waring, J., Lindvall, C., Umeton, R. (2020). Automated machine learning: Review of the state-of -the-art and opportunities for healthcare. Artificial Intelligence in Medicine, 104 (2020) 101822. https://doi.org/10.1016/j.artmed.2020.101822

Jason:

Machine learning is the name given when computers are assigned the job of carrying out complicated tasks. The do this by combining statistics, math, and computer science. This creates artificial intelligence with surprisingly accurate results. This can be a huge benefit to healthcare, in specifics, the diagnostic and outcome of conditions. Some machine learning has already been implemented in dermatology practices to use algorithms to diagnose skin cancers (Sidey-Gibbons, 2019). This is incredibly useful because often there are common connecting factors such as obesity or smoking history to certain cancer diagnosis. Better understanding what machine learning can is help gain deeper insights on how it can be utilized. There are different types of machine learning called supervised and unsupervised. Supervised involves giving the computer data and specific outcomes for that data. This is the process of the computer learning what patterns produce what results. Such as when a tumor is considered malignant and when it is benign. The alternative is unsupervised. This is where no outcome data is input into the computers learning. In this type of machine learning, the computer comes up with its own conclusions and notices patterns on its own. This is more frequently used as exploratory to find undetermined patterns.

References:

Sidey-Gibbons, J. A. & Sidey-Gibbons, C. J. (2019). Machine learning in medicine: A practical introduction. BMC Medical Research Methodology, 19(64). https://bmcmedresmethodol.biomedcentral.com/articl…

Chere:

Unsupervised and supervised medical care has taken place in healthcare for quite some time. Nurses have learned to become self sufficient at the bedside when doctors are not present. Nurses use EKG machines and have the ability to read the results. Radiology techs utilize several department equipment items that may be supervised learning.”ML in medicine can lead to more accurate diagnosis algorithms and individualize patient treatment[2, 3]. To help clinicians develop a better understanding of ML, we will review ML as it applies to medicine in two areas; first by looking at the core concepts and algorithms of ML, applicable to medicine and following this, we will review the current use of ML. To support the machine learning , the computer is provided with features to the learning targets ie; patient demographics and risk factors, and desired outcomes to achieved. the unsupervised learning n unsupervised learning, the computer is provided with unclassified data records to recognize and determine whether any existing latent patterns are present, sometimes producing both answers and questions that may not have been conceived by the

investigators”(Handlemann,2018).

//I acknowledge the syntax reference//

Handleman,G. S., KoK, H. K., Chandra.,R. V.,Razari. AH., Lee. M. J., & Asadi. H.(2018). eDoctor : Mach learning and the future of medicine. The association for the Publication of the Journal of Internal Medicine 284:603-619 https//doi.org/10.org/10.1111/joiim.