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Mashup Score: 2Predicting In-Hospital Mortality After Acute Myeloid Leukemia Therapy: Through Supervised Machine Learning Algorithms - 1 year(s) ago
PURPOSE Despite careful patient selection, induction chemotherapy for acute myeloid leukemia (AML) is associated with a considerable risk for treatment-related mortality (5%-20%). We evaluated machine learning (ML) algorithms trained using factors available at the time of admission for AML therapy to predict death during the hospitalization. METHODS We included AML discharges with age > 17 years…
Source: JCO Clinical Cancer InformaticsCategories: Hem/Oncs, Latest HeadlinesTweet
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Mashup Score: 0
PURPOSE Machine learning (ML) algorithms that incorporate routinely collected patient-reported outcomes (PROs) alongside electronic health record (EHR) variables may improve prediction of short-term mortality and facilitate earlier supportive and palliative care for patients with cancer. METHODS We trained and validated two-phase ML algorithms that incorporated standard PRO assessments alongside…
Source: JCO Clinical Cancer InformaticsCategories: Hem/Oncs, Latest HeadlinesTweet-
🚨 NEW 🚨 in @JCOCCI_ASCO: #MachineLearning algorithms to predict mortality or adverse events are often based on routinely-collected administrative #data. Big limitation. How does incorporating the #patient voice into predictions help? Answer: A lot! https://t.co/VFzRXuRgVI https://t.co/ToUfttY8qS
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Mashup Score: 0Organizing Online Health Content: Developing Hashtag Collections for Healthier Internet-Based People and Communities - 1 year(s) ago
Twitter use has increased among patients with cancer, advocates, and oncology professionals. Hashtags, a form of metadata, can be used to share content, organize health information, and create virtual communities of interest. Cancer-specific hashtags modeled on a breast cancer community, #bcsm, led to the development of a structured set of hashtags called the cancer tag ontology. In this article,…
Source: JCO Clinical Cancer InformaticsCategories: Hem/Oncs, Latest HeadlinesTweet-
Organizing Online Health Content: Developing Hashtag Collections... [6/28/19] - @subatomicdoc @pfanderson @mtmdphd @TheLizArmy @JFreemanDaily @audvin @drdonsdizon @CBlotner_ @DavidCookeMD @womenofteal @stales @fischmd @colleen_young @DrAttai @JCOCCI_ASCO https://t.co/776r7qmxcY https://t.co/8S2BezS7lC
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Mashup Score: 0
PURPOSE The study of rare diseases, such as multiple myeloma (MM), often experiences unique research hurdles that can delay or prevent lifesaving discoveries. HealthTree Cure Hub is a first-in-class software program designed to overcome these potential research hurdles. METHODS We assessed whether HealthTree Cure Hub improved four commonly experienced research hurdles such as (1) small numbers of…
Source: JCO Clinical Cancer InformaticsCategories: Hem/Oncs, Latest HeadlinesTweet-
.@HealthTree Cure Hub: A Patient-Derived, Patient-Driven Clinical Cancer Information Platform Used to Overcome Hurdles & Accelerate Research in Multiple Myeloma [3/10/22] @NSweeneyPhd @AhlstromJenny @FaithEDavies1 @mtmdphd @JCOCCI_ASCO https://t.co/WOwLb9Eozv @myelomacrowd #mmsm https://t.co/2BKEzcsrIT
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Mashup Score: 0Organizing Online Health Content: Developing Hashtag Collections for Healthier Internet-Based People and Communities - 1 year(s) ago
Twitter use has increased among patients with cancer, advocates, and oncology professionals. Hashtags, a form of metadata, can be used to share content, organize health information, and create virtual communities of interest. Cancer-specific hashtags modeled on a breast cancer community, #bcsm, led to the development of a structured set of hashtags called the cancer tag ontology. In this article,…
Source: JCO Clinical Cancer InformaticsCategories: Hem/Oncs, Latest HeadlinesTweet-
Organizing Online Health Content: Developing Hashtag Collections... [6/28/19] - @subatomicdoc @pfanderson @mtmdphd @TheLizArmy @JFreemanDaily @audvin @drdonsdizon @CBlotner_ @DavidCookeMD @womenofteal @stales @fischmd @colleen_young @DrAttai @JCOCCI_ASCO https://t.co/776r7qn52w
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Mashup Score: 0Prediction of Neutropenic Events in Chemotherapy Patients: A Machine Learning Approach - 1 year(s) ago
PURPOSE Severe and febrile neutropenia present serious hazards to patients with cancer undergoing chemotherapy. We seek to develop a machine learning–based neutropenia prediction model that can be used to assess risk at the initiation of a chemotherapy cycle. MATERIALS AND METHODS We leverage rich electronic medical records (EMRs) data from a large health care system and apply machine learning…
Source: JCO Clinical Cancer InformaticsCategories: Hem/Oncs, Latest HeadlinesTweet-
Prediction of Neutropenic Events in Chemotherapy Patients: A Machine Learning Approach [Aug 31, 2021] @holly_wiberg @YupOnc et al. @JCOCCI_ASCO https://t.co/zhOlJGLOfK #SuppOnc #IDonc https://t.co/lTwD2eQ9ho
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Mashup Score: 0Organizing Online Health Content: Developing Hashtag Collections for Healthier Internet-Based People and Communities - 1 year(s) ago
Twitter use has increased among patients with cancer, advocates, and oncology professionals. Hashtags, a form of metadata, can be used to share content, organize health information, and create virtual communities of interest. Cancer-specific hashtags modeled on a breast cancer community, #bcsm, led to the development of a structured set of hashtags called the cancer tag ontology. In this article,…
Source: JCO Clinical Cancer InformaticsCategories: Hem/Oncs, Latest HeadlinesTweet-
Organizing Online Health Content: Developing Hashtag Collections... [6/28/19] - @subatomicdoc @pfanderson @mtmdphd @TheLizArmy @JFreemanDaily @audvin @drdonsdizon @CBlotner_ @DavidCookeMD @womenofteal @stales @fischmd @colleen_young @DrAttai @JCOCCI_ASCO https://t.co/776r7qn52w https://t.co/c3oFznZpct
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Mashup Score: 3A Framework for Harmonization of Radiomics Data for Multicenter Studies and Clinical Trials - 1 year(s) ago
PURPOSE Variability in computed tomography images intrinsic to individual scanners limits the application of radiomics in clinical and research settings. The development of reproducible and generalizable radiomics-based models to assess lesions requires harmonization of data. The purpose of this study was to develop, test, and analyze the efficacy of a radiomics data harmonization model….
Source: JCO Clinical Cancer InformaticsCategories: Hem/Onc News and Journals, Latest HeadlinesTweet-
RT @YuriVelichko: A Framework for Harmonization of Radiomics Data for Multicenter Studies and Clinical Trials https://t.co/aJ2FufhfTZ #rad…
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Mashup Score: 0Organizing Online Health Content: Developing Hashtag Collections for Healthier Internet-Based People and Communities - 2 year(s) ago
Twitter use has increased among patients with cancer, advocates, and oncology professionals. Hashtags, a form of metadata, can be used to share content, organize health information, and create virtual communities of interest. Cancer-specific hashtags modeled on a breast cancer community, #bcsm, led to the development of a structured set of hashtags called the cancer tag ontology. In this article,…
Source: JCO Clinical Cancer InformaticsCategories: Healthcare Professionals, Latest HeadlinesTweet-
@elonmusk There are ways to use hashtags as interactive radio stations to grow communities of interest. If it creates enough value and security for sharing some health info, some might find it worth paying for. https://t.co/HEzr3Nhv74
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Mashup Score: 2
PURPOSE Adolescents and young adults (AYA) with cancer do not fit neatly into pediatric or older adult oncology care settings. Recent efforts have led to the development of psychosocial interventions for AYAs, but studies show AYAs demonstrate low levels of engagement in psychosocial services. The AYA Care Plan is one of the only web-based tools providing a personalized, psychosocial resource…
Source: JCO Clinical Cancer InformaticsCategories: Hem/Oncs, Latest HeadlinesTweet-
Kudos to this group helping young adults with cancer with a scalable solution @JCOCCI_ASCO @KimmelCancerCtr @JeffRadOnc https://t.co/e7NTUEbUJH
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This is interesting. I recently did a #HealthcareUnfiltered on #AI in medicine hosting @RogueRad & @pwarier of @qure_ai on the topic. Can we predict inpt mortality? @AzizNazhaMD @Dr_AmerZeidan @MartinTallman https://t.co/ZOfvnma8ct