Results: A total of 10 688 adult patient samples representing 40 untreated primary tumor types and 26 adjacent-normal tissues were used for training. Demographic data were not available for all data sets. Among the training data set, 5157 of 10 244 (50.3%) were male and the mean (SD) age was 58.9 (14.5) years. An accuracy rate of 99% was obtained for primary epithelioid mesotheliomas tested (125 of 126). The remaining 85 mesotheliomas had a mixed etiology (sarcomatoid mesotheliomas) and were correctly identified as a mixture of their primary components, with potential implications in resolving subtypes and incidences of mixed histology.

A separate "Palliative radiation dataset" was created using patients (899) enrolled from 2015-2016 in a prospective survey study conducted in our institution's Radiation Oncology department. The overall group of patients were seen for 471,005 daily encounters/visits, including outpatient and inpatient contact. For these visits, median followup was 12.7 months. Median overall survival was 22.4 months. Patients were hospitalized for 115,716 (24.6%) visits. There were 1,403,544 provider notes. The training set contains 10,239 patients with 380,080 visits, validation set of 1,785 patients and test set of 1,818 patients (15%) with 90,925 visits. Proposed System - PPES-Met: The model is composed of two core processing blocks: i. Semantic Word Embedding (SWE): We adopted a completely unsupervised hybrid method - an updated version of Intelligent Word Embedding (IWE) method(3) that combines semantic-dictionary mapping, neural embedding, and context-based windowing technique for creating dense vector representation of free-text clinical narratives. The method leverages the benefits of unsupervised learning along with expert-knowledge to tackle the major challenges of information extraction of informative information from clinical texts, while accounting for ambiguity of free text narrative style, lexical variations, arbitrary ordering of words, and frequent appearance of abbreviations and acronyms.

Pathologists still do the bulk of their diagnosis of metastatic cancer cells in tissue and lymph nodes by hand, putting slides under a microscope and looking for signature irregularities they're trained to see. Recent advances in computer technology, however, particularly in artificial intelligence (AI), have begun to teach machines to do this kind of detection with growing rates of improvement. Now, a research team from Beth Israel Deaconess Medical Center (BIDMC) and Harvard Medical School have developed a form of AI that can interpret these pathology images with accuracy levels of 92.5 percent. Moreover, when the two are used in combination, the detection rate approaches 100 percent (approximately 99.5 percent). Their AI method is a form of deep learning, in which the system attempts to replicate the activity of the human neocortex through artificial neural networks.