***TASK*** the task is to summarize an input biomedical literature in six sentences 
    ***INPUT*** the input is a biomedical literature 
    ***OUTPUT*** the output is the summary of an input biomedical literature in six sentences 
    ***DOCUMENTATION*** 
    ***EXAMPLES*** 
    Input: ABSTRACT The fecal microbiota is a rich source of biomarkers that have previously been shown to be predictive of numerous disease states . Less well studied is the effect of immunomodulatory therapy on the microbiota and its role in response to therapy . This study explored associations between the fecal microbiota and therapeutic response of Crohn ’s disease ( CD ) patients treated with ustekinumab ( UST ; Stelara ) in the phase 2 CERTIFI study . Using stool sample s collected over the course of 22 weeks , the composition of these subjects ’ fecal bacterial communities was characterized by sequencing the 16S rRNA gene . Subjects in remission could be distinguished from those with active disease 6 weeks after treatment using r and om forest models trained on subjects ’ baseline microbiota and clinical data ( area under the curve [ AUC ] of 0.844 , specificity of 0.831 , sensitivity of 0.774 ) . The most predictive operational taxonomic units ( OTUs ) that were ubiquitous among subjects were affiliated with Faecalibacterium and Escherichia or Shigella . The median baseline community diversity in subjects in remission 6 weeks after treatment was 1.7 times higher than that in treated subjects with active disease ( P = 0.020 ) . Their baseline community structures were also significantly different ( P = 0.017 ) . Two OTUs affiliated with Faecalibacterium ( P = 0.003 ) and Bacteroides ( P = 0.022 ) were significantly more abundant at baseline in subjects who were in remission 6 weeks after treatment than those with active CD . The microbiota diversity of UST-treated clinical responders increased over the 22 weeks of the study , in contrast to nonresponsive subjects ( P = 0.012 ) . The observed baseline differences in fecal microbiota and changes due to therapeutic response support the potential for the microbiota as a response biomarker . IMPORTANCE CD is a global health concern , with increasing incidence and prevalence , causing large economic and health care impacts . Finding prognostic biomarkers that give clinicians the ability to identify patients more likely to respond to CD treatment at diagnosis will reduce the time subjects receive drugs that are unlikely to be beneficial . OTUs associated with remission after treatment induction , especially Faecalibacterium , could be biomarkers for successful UST treatment of anti-tumor necrosis factor alpha ( anti-TNF-α ) refractory CD patients . More broadly , these results suggest that the fecal microbiota could be a useful noninvasive biomarker for directing or monitoring the treatment of gastrointestinal diseases . IMPORTANCE CD is a global health concern , with increasing incidence and prevalence , causing large economic and health care impacts . Finding prognostic biomarkers that give clinicians the ability to identify patients more likely to respond to CD treatment at diagnosis will reduce the time subjects receive drugs that are unlikely to be beneficial . OTUs associated with remission after treatment induction , especially Faecalibacterium , could be biomarkers for successful UST treatment of anti-tumor necrosis factor alpha ( anti-TNF-α ) refractory CD patients . More broadly , these results suggest that the fecal microbiota could be a useful noninvasive biomarker for directing or monitoring the treatment of gastrointestinal diseases Background : Multiple sclerosis has an extremely variable natural course . In most patients , disease starts with a relapsing-remitting ( RR ) phase , which proceeds to a secondary progressive ( SP ) form . The duration of the RR phase is hard to predict , and to date predictions on the rate of disease progression remain suboptimal . This limits the opportunity to tailor therapy on an individual patient 's prognosis , in spite of the choice of several therapeutic options . Approaches to improve clinical decisions , such as collective intelligence of human groups and machine learning algorithms are widely investigated . Methods : Medical students and a machine learning algorithm predicted the course of disease on the basis of r and omly chosen clinical records of patients that attended at the Multiple Sclerosis service of Sant ' And rea hospital in Rome . Results : A significant improvement of predictive ability was obtained when predictions were combined with a weight that depends on the consistence of human ( or algorithm ) forecasts on a given clinical record . Conclusions : In this work we present proof-of-principle that human-machine hybrid predictions yield better prognoses than machine learning algorithms or groups of humans alone . To strengthen this preliminary result , we propose a crowdsourcing initiative to collect prognoses by physicians on an exp and ed set of patients BACKGROUND AND AIM Knowledge of long-term outcomes following an index episode of acute severe colitis ( ASC ) can help informed decision making at a time of acute exacerbation especially when colectomy is an option . We aim ed to identify long-term outcomes and their predictors after a first episode of ASC in a large North Indian cohort . METHODS Hospitalized patients satisfying Truelove and Witts ' criteria under follow-up at a single center from January 2003 to December 2013 were included . Patients avoiding colectomy at index admission were categorized as complete ( ≤ 3 non bloody stool per day ) or incomplete responders , based upon response to corticosteroids at day 7 . R and om Forest-based machine learning models were constructed to predict the long-term risk of colectomy or steroid dependence following an index episode of ASC . RESULTS Of 1731 patients with ulcerative colitis , 179 ( 10 % ) had an index episode of ASC . Nineteen ( 11 % ) patients  underwent colectomy  at index admission and 42 ( 26 % ) over a median follow-up of 56 ( 1 - 159 ) months . Hazard ratio for colectomy  for incomplete responder was 3.6 ( 1.7 - 7.5 , P = 0.001 ) compared with complete responder . Modeling based on four variables , response at day 7 of hospitalization , steroid use during the first year of diagnosis , longer disease duration before ASC , and number of extra-intestinal manifestations , was able to predict colectomy with an accuracy of 77 % . CONCLUSIONS Disease behavior of ASC in India is similar to the West , with a third undergoing colectomy at 10 years . Clinical features , especially response at day 7 hospitalization for index ASC , can predict both colectomy and steroid dependence with reasonable accuracy Background : In type 1 diabetes mellitus ( T1DM ) , patients play an active role in their own care and need to have the knowledge to adapt decisions to their daily living conditions . Artificial intelligence applications can help people with type 1 diabetes in decision making and allow them to react at time scales shorter than the scheduled face-to-face visits . This work presents a decision support system ( DSS ) , based on glucose prediction , to assist patients in a mobile environment . Methods : The system ’s impact on therapeutic corrective actions has been evaluated in a r and omized crossover pilot study focused on interpr and ial periods . Twelve people with type 1 diabetes treated with insulin pump  participated in two phases : In the experimental phase ( EP ) patients used the DSS to modify initial corrective decisions in presence of hypoglycemia or hyperglycemia events . In the control phase ( CP ) patients were asked to follow decisions without knowing the glucose prediction . A telemedicine platform allowed participants to register monitoring data and decisions and allowed endocrinologists to supervise data at the hospital . The study period was defined as a postprediction ( PP ) time window . Results : After knowing the glucose prediction , participants modified the initial decision in 20 % of the situations . No statistically significant differences were found in the PP Kovatchev ’s risk index change ( –1.23 ± 11.85 in EP vs –0.56 ± 6.06 in CP ) . Participants had a positive opinion about the DSS with an average score higher than 7 in a usability question naire . Conclusion : The DSS had a relevant impact in the participants ’ decision making while dealing with T1DM and showed a high confidence of patients in the use of glucose prediction Background and Aims Big data analytics leverage patterns in data to harvest valuable information , but are rarely implemented in clinical care . Optimising thiopurine therapy for inflammatory bowel disease [ IBD ] has proved difficult . Current methods using 6-thioguanine nucleotide [ 6-TGN ] metabolites have failed in r and omized controlled trials [ RCTs ] , and have not been used to predict objective remission [ OR ] . Our aims were to : 1 ) develop machine learning algorithms [ MLA ] using laboratory values and age to identify patients in objective remission on thiopurines ; and 2 ) determine whether achieving algorithm-predicted objective remission result ed in fewer clinical events per year . Methods Objective remission was defined as the absence of objective evidence of intestinal inflammation . MLAs were developed to predict three outcomes : objective remission , non-adherence , and preferential shunting to 6-methylmercaptopurine [ 6-MMP ] . The performance of the algorithms was evaluated using the area under the receiver operating characteristic curve [ AuROC ] . Clinical event rates of new steroid prescriptions , hospitalisations , and abdominal surgeries were measured . Results Retrospective review was performed on medical records of 1080 IBD patients on thiopurines . The AuROC for algorithm-predicted remission in the validation set was 0.79 vs 0.49 for 6-TGN . The mean number of clinical events per year in patients with sustained algorithm-predicted remission [ APR ] was 1.08 vs 3.95 in those that did not have sustained APR [ p < 1 x 10 - 5 ] . Reductions in the individual endpoints of steroid prescriptions/year [ -1.63 , p < 1 x 10 - 5 ] , hospitalisations/year [ -1.05 , p < 1 x 10 - 5 ] , and surgeries/year [ -0.19 , p = 0.065 ] were seen with algorithm-predicted remission . Conclusions A machine learning algorithm was able to identify IBD patients on thiopurines with algorithm-predicted objective remission , a state associated with significant clinical benefits , including decreased steroid prescriptions , hospitalisations , and surgeries Primary sclerosing cholangitis ( PSC ) is a liver disease known for its frequent concurrence with inflammatory bowel disease . Dysbiosis of the gut microbiota in PSC was reported in several studies , but the microbiological features of the salivary microbiota in PSC have not been established . Here we compared the salivary microbial communities of 24 pediatric-onset PSC patients , 16 age-matched ulcerative colitis ( UC ) patients , and 24 healthy controls ( HCs ) by analyzing the bacterial 16S rRNA gene sequence data . The species-richness ( α-diversity ) showed no significant between-group differences , whereas the overall salivary microbiota structure ( β-diversity ) showed significant differences among the three groups . Taxonomic assignment revealed that the PSC salivary microbiota were characterized by significant decreases in the abundance of Rothia and Haemophilus compared to the HC group , and significantly decreased Haemophilus and increased Oribacterium compared to the UC group . By combining the genera selected by the r and om forest algorithm in machine learning , followed by confirmation with 10-fold cross-validation , we were able to distinguish the PSC group from the HC group with the area under the curve ( AUC ) of 0.7423 , and from the UC group with the AUC of 0.8756 . Our results indicate the potential of salivary microbiota as biomarkers for a noninvasive diagnosis of PSC Background Identifying patients with certain clinical criteria based on manual chart review of doctors ’ notes is a daunting task given the massive amounts of text notes in the electronic health records ( EHR ) . This task can be automated using text classifiers based on Natural Language Processing ( NLP ) techniques along with pattern recognition machine learning ( ML ) algorithms . The aim of this research is to evaluate the performance of traditional classifiers for identifying patients with Systemic Lupus Erythematosus ( SLE ) in comparison with a newer Bayesian word vector method . Methods We obtained clinical notes for patients with SLE diagnosis along with controls from the Rheumatology Clinic ( 662 total patients ) . Sparse bag-of-words ( BOWs ) and Unified Medical Language System ( UMLS ) Concept Unique Identifiers ( CUIs ) matrices were produced using NLP pipelines . These matrices were subjected to several different NLP classifiers : neural networks , r and om forests , naïve Bayes , support vector machines , and Word2Vec inversion , a Bayesian inversion method . Performance was measured by calculating accuracy and area under the Receiver Operating Characteristic ( ROC ) curve ( AUC ) of a cross-vali date d ( CV ) set and a separate testing set . Results We calculated the accuracy of the ICD-9 billing codes as a baseline to be 90.00 % with an AUC of 0.900 , the shallow neural network with CUIs to be 92.10 % with an AUC of 0.970 , the r and om forest with BOWs to be 95.25 % with an AUC of 0.994 , the r and om forest with CUIs to be 95.00 % with an AUC of 0.979 , and the Word2Vec inversion to be 90.03 % with an AUC of 0.905 . Conclusions Our results suggest that a shallow neural network with CUIs and r and om forests with both CUIs and BOWs are the best classifiers for this lupus phenotyping task . The Word2Vec inversion method failed to significantly beat the ICD-9 code classification , but yielded promising results . This method does not require explicit features and is more adaptable to non-binary classification tasks . The Word2Vec inversion is hypothesized to become more powerful with access to more data . Therefore , currently , the shallow neural networks and r and om forests are the desirable class 
    Output:
    
Many models achieved good predictive results in simple scenarios ( e.g. classification of cases and controls ) .