{'fraction_non_alphanumeric': 0.012145748987854251, 'fraction_numerical': 0.0008097165991902834, 'mean_word_length': 4.9423076923076925, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Mortality Prediction of ICU patients using Machine Leaning: A survey

Recently health care researchers are working a lot on outcome prediction on Intensive Care Unit (ICU) and trauma. Outcome prediction in intensive care is a difficult process. Accurate synthesis of quality data and application of prior experience to the analysis is required to solve it. In this paper we will review some of the recent advancements in the mortality prediction of ICU patients using machine learning techniques. Mainly the research covered in this survey will be on predicting readmission in Intensive care unit, mortality rate after ICU discharge and life expectancy rate for 5 years. In order to analyse how much a patient will survive in next five years, this expectancy rate is used. These predictions are very useful because using these results doctors can take decision about extending the stay of patient in hospital and also helps in taking decision about the particular treatment needed. An extensive survey of the related research is presented with a stress on the major novelties of their work. At last, the research gaps inferred after analyzing the previous works are highlighted along with the future prospects in the field of research.