[\/et_pb_text][et_pb_text disabled_on=”on|on|on” _builder_version=”4.16″ text_font=”||||||||” text_text_color=”#141414″ text_line_height=”1.9em” header_font=”||||||||” header_2_font=”||||||||” module_alignment=”center” custom_margin=”||30px|” disabled=”on” global_colors_info=”{}”]<\/p>\n
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[\/et_pb_text][et_pb_button button_text=”Lorem ipsum dolor sit amet” disabled_on=”on|on|on” _builder_version=”4.16″ custom_button=”on” button_text_size=”16px” button_text_color=”#FFFFFF” button_bg_color=”#00AEEF” button_border_width=”0px” button_border_radius=”0px” button_font=”||||||||” button_icon=”5||divi||400″ button_icon_color=”#FFFFFF” button_on_hover=”off” custom_margin=”20px|||” custom_padding=”12px|28px|12px|28px” box_shadow_style=”preset1″ box_shadow_vertical=”10px” box_shadow_blur=”30px” box_shadow_color=”#58595B” button_letter_spacing_hover=”2px” disabled=”on” locked=”off” global_colors_info=”{}” button_text_size__hover_enabled=”off” button_one_text_size__hover_enabled=”off” button_two_text_size__hover_enabled=”off” button_text_color__hover_enabled=”off” button_one_text_color__hover_enabled=”off” button_two_text_color__hover_enabled=”off” button_border_width__hover_enabled=”off” button_one_border_width__hover_enabled=”off” button_two_border_width__hover_enabled=”off” button_border_color__hover_enabled=”off” button_one_border_color__hover_enabled=”off” button_two_border_color__hover_enabled=”off” button_border_radius__hover_enabled=”off” button_one_border_radius__hover_enabled=”off” button_two_border_radius__hover_enabled=”off” button_letter_spacing__hover_enabled=”on” button_letter_spacing__hover=”2px” button_one_letter_spacing__hover_enabled=”off” button_two_letter_spacing__hover_enabled=”off” button_bg_color__hover_enabled=”off” button_one_bg_color__hover_enabled=”off” button_two_bg_color__hover_enabled=”off”][\/et_pb_button][et_pb_button button_url=”https:\/\/labs.icahn.mssm.edu\/tcibci\/biostatistics\/” button_text=”Find Out More” _builder_version=”4.16″ _module_preset=”default” custom_button=”on” button_text_color=”#FFFFFF” button_bg_color=”#00AEEF” button_border_radius=”0px” box_shadow_style=”preset1″ locked=”off” global_colors_info=”{}”][\/et_pb_button][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ admin_label=”Two Column Image and Text over Button – Section 6 (Collections)” module_class=”section6_two_col_img_txt_over_btn” _builder_version=”4.16″ custom_margin=”30px||100px||false|false” custom_margin_tablet=”100px||||false|false” custom_margin_phone=”” custom_margin_last_edited=”on|desktop” custom_padding=”0px|0px|0px|0px|false|false” custom_css_main_element=”padding: 0px !important;” locked=”off” collapsed=”off” global_colors_info=”{}”][et_pb_row column_structure=”1_2,1_2″ make_equal=”on” _builder_version=”4.16″ module_alignment=”center” custom_margin=”|auto|0px|auto||” custom_padding=”0px||0px|||” box_shadow_style=”preset1″ box_shadow_color=”#58595B” collapsed=”off” global_colors_info=”{}”][et_pb_column type=”1_2″ _builder_version=”4.16″ background_image=”https:\/\/labs.icahn.mssm.edu\/ferketlab\/wp-content\/uploads\/sites\/505\/2022\/08\/ai-008-bwSm-1.png” background_position=”center_right” custom_padding=”40px|40px|40px|40px|true|true” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_text disabled_on=”off|off|off” admin_label=”Clinical Informatics” _builder_version=”4.16″ header_font=”|700|||||||” header_text_color=”#141414″ header_font_size=”36px” header_3_font=”|700|||||||” header_3_text_color=”#2b2b2b” header_3_font_size=”28px” header_3_line_height=”1.5em” custom_margin=”||10px|” custom_padding=”5%||||false|false” global_colors_info=”{}”]<\/p>\n
Clinical Informatics<\/strong><\/h1>\n[\/et_pb_text][et_pb_text _builder_version=”4.16″ text_font=”||||||||” text_text_color=”#000000″ text_line_height=”22px” header_font=”||||” header_text_color=”#000000″ header_4_text_color=”#FFFFFF” header_5_text_color=”#FFFFFF” background_size=”initial” background_position=”top_left” background_repeat=”repeat” max_width=”700px” custom_padding=”2%||5%||false|false” animation_style=”slide” animation_direction=”top” animation_intensity_slide=”4%” global_colors_info=”{}”]<\/p>\n\nGathering and synthesizing large amounts of cancer data from multiple sources, as well as efficiently implementing such information into clinical practice has become increasingly important. The multiple domains of data in the cancer control continuum frequently raises complex challenges in data management, integration, and analysis. Data scientists and informaticians of many stripes play a key role in these\u202fefforts. Successful internal and external collaboration is often required for high-impact publications, grantsmanship, and advances in clinical care.\u00a0\u00a0<\/span>\u00a0<\/span><\/p>\nOur clinical informatics experts\u202fsupport clinical, translational, and basic science\u202fTCI\u202finvestigators\u202fby\u202fenhancing\u202fand extending Mount Sinai\u2019s<\/span>\u202f<\/span>informatics infrastructure to\u202fmake cancer research data\u202fand software tools<\/span>\u202f<\/span>easily\u202faccessible.<\/span>\u202f<\/span>In addition, we aim to\u202festablish<\/span>\u202f<\/span>clinical\u202finformatics as an academic discipline at\u202fTCI<\/span>\u202f<\/span>and\u202ffoster\u202fnational collaborations\u202fto accelerate informatics ideas, best practices, technologies, and standards.\u202f<\/span>\u202f<\/span>\u00a0<\/span><\/p>\n<\/article>\n[\/et_pb_text][et_pb_text disabled_on=”on|on|on” _builder_version=”4.16″ text_font=”||||||||” text_text_color=”#141414″ text_line_height=”1.9em” header_font=”||||||||” header_2_font=”||||||||” module_alignment=”center” custom_margin=”||30px|” disabled=”on” global_colors_info=”{}”]<\/p>\n
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[\/et_pb_text][et_pb_button button_text=”Lorem ipsum dolor sit amet” disabled_on=”on|on|on” _builder_version=”4.16″ custom_button=”on” button_text_size=”16px” button_text_color=”#FFFFFF” button_bg_color=”#00AEEF” button_border_width=”0px” button_border_radius=”0px” button_font=”||||||||” button_icon=”5||divi||400″ button_icon_color=”#FFFFFF” button_on_hover=”off” custom_margin=”20px|||” custom_padding=”12px|28px|12px|28px” box_shadow_style=”preset1″ box_shadow_vertical=”10px” box_shadow_blur=”30px” box_shadow_color=”#58595B” button_letter_spacing_hover=”2px” disabled=”on” locked=”off” global_colors_info=”{}” button_text_size__hover_enabled=”off” button_one_text_size__hover_enabled=”off” button_two_text_size__hover_enabled=”off” button_text_color__hover_enabled=”off” button_one_text_color__hover_enabled=”off” button_two_text_color__hover_enabled=”off” button_border_width__hover_enabled=”off” button_one_border_width__hover_enabled=”off” button_two_border_width__hover_enabled=”off” button_border_color__hover_enabled=”off” button_one_border_color__hover_enabled=”off” button_two_border_color__hover_enabled=”off” button_border_radius__hover_enabled=”off” button_one_border_radius__hover_enabled=”off” button_two_border_radius__hover_enabled=”off” button_letter_spacing__hover_enabled=”on” button_letter_spacing__hover=”2px” button_one_letter_spacing__hover_enabled=”off” button_two_letter_spacing__hover_enabled=”off” button_bg_color__hover_enabled=”off” button_one_bg_color__hover_enabled=”off” button_two_bg_color__hover_enabled=”off”][\/et_pb_button][et_pb_button button_url=”https:\/\/labs.icahn.mssm.edu\/tcibci\/clinical-informatics\/” button_text=”Find Out More” _builder_version=”4.16″ _module_preset=”default” custom_button=”on” button_text_color=”#FFFFFF” button_bg_color=”#00AEEF” button_border_radius=”0px” box_shadow_style=”preset1″ locked=”off” global_colors_info=”{}”][\/et_pb_button][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.16″ use_background_color_gradient=”on” background_color_gradient_direction=”35deg” background_color_gradient_stops=”#ff6a88 0%|#ff99ac 100%” background_color_gradient_start=”#ff6a88″ background_color_gradient_end=”#ff99ac” background_image=”https:\/\/labs.icahn.mssm.edu\/ferketlab\/wp-content\/uploads\/sites\/505\/2022\/08\/Rectangle-3-copy-5.jpg” custom_padding=”|5%||5%” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_image src=”https:\/\/labs.icahn.mssm.edu\/tcibci\/wp-content\/uploads\/sites\/505\/2023\/10\/binary-1316395_640.jpg” alt=”Binary Code ” title_text=”binary-1316395_640″ align=”center” align_tablet=”center” align_phone=”” align_last_edited=”on|desktop” _builder_version=”4.16″ background_enable_image=”off” background_size=”initial” custom_margin=”-5px||-20px||false|false” custom_padding=”|||” animation_style=”zoom” animation_direction=”left” animation_intensity_zoom=”10%” animation_starting_opacity=”100%” global_colors_info=”{}”][\/et_pb_image][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ admin_label=”Two Column Image and Text over Button – Section 4″ module_class=”section4_two_col_img_txt_over_btn” _builder_version=”4.16″ background_color=”#00AEEF” background_image=”https:\/\/labs.icahn.mssm.edu\/ferketlab\/wp-content\/uploads\/sites\/505\/2022\/12\/Background-1ALT.png” parallax=”on” width=”100%” custom_margin=”30px||||false|false” custom_padding=”80px|0px|63px|0px||” mix_blend_mode=”multiply” bottom_divider_style=”ramp2″ bottom_divider_color=”#FFFFFF” bottom_divider_height=”150px” bottom_divider_flip=”horizontal” bottom_divider_arrangement=”above_content” locked=”off” collapsed=”off” global_colors_info=”{}”][et_pb_row column_structure=”1_2,1_2″ _builder_version=”4.16″ background_size=”initial” background_position=”top_left” background_repeat=”repeat” custom_margin=”|||” custom_padding=”|||” global_colors_info=”{}”][et_pb_column type=”1_2″ _builder_version=”4.16″ background_position=”top_left” custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_text admin_label=”Bayesian” _builder_version=”4.16″ text_font=”||||||||” text_text_color=”#FFFFFF” text_line_height=”22px” header_font=”||||” header_text_color=”#FFFFFF” header_2_font=”|700|||||||” header_2_text_color=”#221F72″ header_4_text_color=”#FFFFFF” header_5_text_color=”#FFFFFF” background_size=”initial” background_position=”top_left” background_repeat=”repeat” max_width=”700px” custom_padding=”0px|||||” animation_style=”slide” animation_direction=”top” animation_intensity_slide=”4%” global_colors_info=”{}”]<\/p>\n
Biostatistics core utilizes Bayesian Optimal Interval Phase I\/II (BOIN12) design to assess safety and efficacy of a combination regimen for myelodysplastic syndrome\/myeloproliferative neoplasm (MDS\/MPN) overlap syndrome<\/h2>\n
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[\/et_pb_text][et_pb_text _builder_version=”4.16″ text_font=”||||||||” text_text_color=”#FFFFFF” text_line_height=”22px” header_font=”||||” header_text_color=”#FFFFFF” header_4_text_color=”#FFFFFF” header_5_text_color=”#FFFFFF” background_size=”initial” background_position=”top_left” background_repeat=”repeat” max_width=”700px” custom_padding=”0px|||||” animation_style=”slide” animation_direction=”top” animation_intensity_slide=”4%” global_colors_info=”{}”]<\/p>\n
For many novel therapies where the efficacy does not necessarily increase with the dose, the maximum tolerated dose (MTD) may not be the optimal dose for treating patients and the goals of dose-finding trials needs to be identifying the optimal biologic dose (OBD) that optimizes patients\u2019 risk-benefit trade-off. A flexible Bayesian optimal interval phase I\/II (BOIN12) design is introduced to find that OBD. This design makes the decision of dose escalation and de-escalation by simultaneously taking account of efficacy and toxicity and adaptively allocating patients to the dose that optimizes the toxicity-efficacy trade-off.\u00a0Our physician investigators found this design to be simpler to comprehend and implement because it overcomes the computational and implementation complexity that plagues existing Bayesian phase I\/II dose-finding designs.<\/p>\n
[\/et_pb_text][et_pb_button button_url=”https:\/\/labs.icahn.mssm.edu\/design\/option1-research” button_text=”Find Out More ” disabled_on=”on|on|on” _builder_version=”4.16″ _module_preset=”default” custom_button=”on” button_text_color=”#d80b8c” button_bg_color=”#FFFFFF” button_border_radius=”0px” box_shadow_style=”preset1″ disabled=”on” global_colors_info=”{}”][\/et_pb_button][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.16″ background_position=”top_left” custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_image src=”https:\/\/labs.icahn.mssm.edu\/tcibci\/wp-content\/uploads\/sites\/505\/2023\/10\/bayseian-figure.png” alt=”Bayesian Optimal Interval Phase I\/II Decision Tree” title_text=”bayseian figure” disabled_on=”off|off|off” _builder_version=”4.16″ animation_style=”slide” animation_direction=”left” animation_intensity_slide=”10%” box_shadow_style=”preset1″ box_shadow_color=”#58595B” always_center_on_mobile=”off” global_colors_info=”{}”][\/et_pb_image][et_pb_image src=”https:\/\/labs.icahn.mssm.edu\/tcibci\/wp-content\/uploads\/sites\/505\/2023\/10\/bayseian-table1.png” alt=”Outcome Table for Bayesian Optimal Interval Phase I\/II design to assess safety and efficacy of a combination regimen for myelodysplastic syndrome\/myeloproliferative neoplasm (MDS\/MPN) overlap syndrome” title_text=”bayseian table1″ force_fullwidth=”on” disabled_on=”off|off|off” _builder_version=”4.16″ animation_style=”slide” animation_direction=”left” animation_intensity_slide=”10%” box_shadow_style=”preset1″ box_shadow_color=”#58595B” always_center_on_mobile=”off” global_colors_info=”{}”][\/et_pb_image][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ fullwidth=”on” admin_label=”Paralax Background fullwidth Image – Section 3″ module_class=”section3_paralax_bg_fullwidth_img” _builder_version=”4.16″ _module_preset=”default” locked=”off” collapsed=”off” global_colors_info=”{}”][et_pb_fullwidth_image title_text=”3″ _builder_version=”4.16″ _module_preset=”default” background_image=”https:\/\/labs.icahn.mssm.edu\/ferketlab\/wp-content\/uploads\/sites\/505\/2022\/10\/3.png” parallax=”on” parallax_method=”off” min_height=”217.5px” custom_margin=”-2px|||||” filter_opacity=”20%” global_colors_info=”{}”][\/et_pb_fullwidth_image][\/et_pb_section][et_pb_section fb_built=”1″ disabled_on=”on|off|on” admin_label=”Two Column Title Over Text Next to Image – Section 5″ module_class=”section5_two_col_title_over_txt_next_to_img” _builder_version=”4.16″ _module_preset=”default” locked=”off” collapsed=”off” global_colors_info=”{}”][et_pb_row column_structure=”1_2,1_2″ make_equal=”on” admin_label=”5 (Module)” _builder_version=”4.9.0″ background_color=”#FFFFFF” background_size=”initial” background_position=”top_left” background_repeat=”repeat” module_alignment=”center” custom_padding=”6px||3px|||” global_module=”956″ global_colors_info=”{}”][et_pb_column type=”1_2″ _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.16″ _module_preset=”default” text_text_color=”#00AEEF” header_font=”|700|||||||” header_text_color=”#00AEEF” header_font_size=”36px” module_alignment=”center” custom_padding=”10%||10%||true|false” global_colors_info=”{}”]<\/p>\n
Story 2 – Erin’s complicated data analysis<\/strong><\/h1>\n <\/p>\n
[\/et_pb_text][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][et_pb_image src=”https:\/\/labs.icahn.mssm.edu\/ferketlab\/wp-content\/uploads\/sites\/505\/2022\/07\/ai-008.png” alt=”Spiral ” title_text=”ai-008″ _builder_version=”4.16″ _module_preset=”default” background_color=”#FFFFFF” global_colors_info=”{}”][\/et_pb_image][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ admin_label=”Two Column Image and Text over Button – Section 4″ module_class=”section4_two_col_img_txt_over_btn” _builder_version=”4.16″ background_color=”#00AEEF” background_image=”https:\/\/labs.icahn.mssm.edu\/ferketlab\/wp-content\/uploads\/sites\/505\/2022\/12\/Background-1ALT.png” parallax=”on” width=”100%” custom_margin=”30px||||false|false” custom_padding=”80px|0px|63px|0px||” mix_blend_mode=”multiply” bottom_divider_style=”ramp2″ bottom_divider_color=”#FFFFFF” bottom_divider_height=”150px” bottom_divider_flip=”horizontal” bottom_divider_arrangement=”above_content” locked=”off” collapsed=”off” global_colors_info=”{}”][et_pb_row column_structure=”1_2,1_2″ admin_label=”Clinical informatics facilitates risk prediction to guide personalized bladder cancer treatment decisions” _builder_version=”4.16″ background_color=”RGBA(0,0,0,0)” background_size=”initial” background_position=”top_left” background_repeat=”repeat” custom_margin=”|||” custom_padding=”|||” global_colors_info=”{}”][et_pb_column type=”1_2″ _builder_version=”4.16″ background_position=”top_left” custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_image src=”https:\/\/labs.icahn.mssm.edu\/tcibci\/wp-content\/uploads\/sites\/505\/2023\/09\/Galasky-et-al-figure.png” alt=”risk prediction to guide personalized bladder cancer treatment decisions Cystectomy versus neoadjuvant chemotherapy followed by Cystectomy ” title_text=”Galasky et al figure” disabled_on=”off|off|off” _builder_version=”4.16″ animation_style=”slide” animation_direction=”left” animation_intensity_slide=”10%” box_shadow_style=”preset1″ box_shadow_color=”#58595B” always_center_on_mobile=”off” global_colors_info=”{}”][\/et_pb_image][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.16″ background_color=”RGBA(0,0,0,0)” background_position=”top_left” custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_text _builder_version=”4.16″ _module_preset=”default” text_text_color=”#FFFFFF” header_font=”|700|||||||” header_text_color=”#FFFFFF” header_2_font=”|700|||||||” header_2_text_color=”#221F72″ global_colors_info=”{}”]<\/p>\n
Clinical informatics facilitates risk prediction to guide personalized bladder cancer treatment decisions<\/span><\/h2>\n[\/et_pb_text][et_pb_text _builder_version=”4.16″ text_font=”||||||||” text_text_color=”#FFFFFF” text_line_height=”22px” header_font=”||||” header_text_color=”#FFFFFF” header_2_text_color=”#FFFFFF” header_4_text_color=”#FFFFFF” header_5_text_color=”#FFFFFF” background_size=”initial” background_position=”top_left” background_repeat=”repeat” max_width=”700px” custom_padding=”0px|||||” animation_style=”slide” animation_direction=”top” animation_intensity_slide=”4%” global_colors_info=”{}”]<\/p>\n
Clinical guidelines support the use of neoadjuvant chemotherapy (NAC) for the treatment of muscle-invasive bladder cancer (MIBC), but NAC <\/span>remains<\/span> used in only a small subset of patients. A<\/span>n important <\/span>barrier is the difficulty in predicting<\/span> and communicating<\/span> individual <\/span>survival <\/span>estimates <\/span>with or without NAC<\/span>. <\/span>Through a collaboration of clinical and qualitative researchers, we develop<\/span>ed<\/span> individualized risk prediction and communication methods to <\/span>facilitate<\/span> NAC decision making<\/span> using the <\/span>National Cancer Database. <\/span>Based on <\/span>state-transition modeling, five-year cancer mortality and other-cause mortality risk estimates were generated and integrated within a web-based tool. Clinicians thought the prediction<\/span> tool was<\/span> \u201ceasy to use\u201d (100%) and would <\/span>use <\/span>it<\/span> \u201c<\/span>frequently in their practice\u201d (92%).<\/span><\/span>\u00a0<\/span><\/p>\n[\/et_pb_text][et_pb_button button_url=”https:\/\/pubmed.ncbi.nlm.nih.gov\/30657403\/” url_new_window=”on” button_text=”Find Out More ” _builder_version=”4.16″ _module_preset=”default” custom_button=”on” button_text_color=”#d80b8c” button_bg_color=”#FFFFFF” button_border_radius=”0px” box_shadow_style=”preset1″ global_colors_info=”{}”][\/et_pb_button][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][et_pb_row column_structure=”1_2,1_2″ _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”1_2″ _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.16″ _module_preset=”default” text_text_color=”#00AEEF” header_text_color=”#FFFFFF” header_2_text_color=”#00AEEF” global_colors_info=”{}”]<\/p>\n
Clinical informatics with MSHS data science team develops a malnutrition screening tool to increase diagnosis rate\u00a0 <\/strong><\/h2>\n[\/et_pb_text][et_pb_text _builder_version=”4.16″ text_font=”||||||||” text_text_color=”#141414″ text_line_height=”22px” header_font=”||||” header_text_color=”#FFFFFF” header_4_text_color=”#FFFFFF” header_5_text_color=”#FFFFFF” background_size=”initial” background_position=”top_left” background_repeat=”repeat” max_width=”700px” custom_padding=”0px|||||” animation_style=”slide” animation_direction=”top” animation_intensity_slide=”4%” global_colors_info=”{}”]<\/p>\n
The Joint Commission has mandated universal screening and assessment of hospitalized patients for malnutrition because of its association with increased morbidity, mortality, and healthcare costs. Early detection is extremely important for timely intervention but difficult to achieve due to need for specialized skills from registered dieticians (RDs).\u00a0 Machine learning (ML) algorithms, which process massive amounts of data and learn continuously have the potential to identify those at risk for malnutrition more efficiently and effectively than standard screening tools. A clinical decision support tool, MUST-Plus, developed by this team reduced lag between admission and diagnosis, had high usability (> 90%), and increased the rate of malnutrition diagnoses and its documentation.<\/p>\n
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[\/et_pb_text][et_pb_button button_url=”https:\/\/clinicaldatascience.mountsinai.org\/” url_new_window=”on” button_text=”Find Out More ” _builder_version=”4.16″ _module_preset=”default” custom_button=”on” button_text_color=”#FFFFFF” button_bg_color=”#D80B8C” button_border_radius=”0px” box_shadow_style=”preset1″ global_colors_info=”{}”][\/et_pb_button][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n
Dietitian Workflow<\/strong><\/h3>\n[\/et_pb_text][et_pb_image src=”https:\/\/labs.icahn.mssm.edu\/tcibci\/wp-content\/uploads\/sites\/505\/2023\/10\/ML-and-malnutrition.png” alt=”Dietitian Workflow flowchart for malnutrition assessment” title_text=”ML and malnutrition” disabled_on=”off|off|off” _builder_version=”4.16″ animation_style=”slide” animation_direction=”left” animation_intensity_slide=”10%” box_shadow_style=”preset1″ box_shadow_color=”#58595B” always_center_on_mobile=”off” global_colors_info=”{}”][\/et_pb_image][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ disabled_on=”off|off|off” admin_label=”Fullwidth Video over Background – Section 9″ module_class=”fullwidth-video-over-background-section-9″ _builder_version=”4.16″ _module_preset=”default” background_color=”#ededed” background_image=”https:\/\/labs.icahn.mssm.edu\/ferketlab\/wp-content\/uploads\/sites\/505\/2022\/10\/4.png” height_tablet=”” height_phone=”” height_last_edited=”on|desktop” 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Accessibility<\/a><\/p>\n[\/et_pb_text][\/et_pb_column][et_pb_column type=”1_3″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_text _builder_version=”4.16″ text_font=”||||||||” text_text_color=”#FFFFFF” text_line_height=”1.25em” link_text_color=”#fff60c” header_font=”||||||||” header_4_font=”|700||on|||||” header_4_font_size=”16px” header_4_letter_spacing=”1px” header_4_line_height=”1.5em” background_layout=”dark” locked=”off” global_colors_info=”{}”]<\/p>\n
Gathering and synthesizing large amounts of cancer data from multiple sources, as well as efficiently implementing such information into clinical practice has become increasingly important. The multiple domains of data in the cancer control continuum frequently raises complex challenges in data management, integration, and analysis. Data scientists and informaticians of many stripes play a key role in these\u202fefforts. Successful internal and external collaboration is often required for high-impact publications, grantsmanship, and advances in clinical care.\u00a0\u00a0<\/span>\u00a0<\/span><\/p>\n Our clinical informatics experts\u202fsupport clinical, translational, and basic science\u202fTCI\u202finvestigators\u202fby\u202fenhancing\u202fand extending Mount Sinai\u2019s<\/span>\u202f<\/span>informatics infrastructure to\u202fmake cancer research data\u202fand software tools<\/span>\u202f<\/span>easily\u202faccessible.<\/span>\u202f<\/span>In addition, we aim to\u202festablish<\/span>\u202f<\/span>clinical\u202finformatics as an academic discipline at\u202fTCI<\/span>\u202f<\/span>and\u202ffoster\u202fnational collaborations\u202fto accelerate informatics ideas, best practices, technologies, and standards.\u202f<\/span>\u202f<\/span>\u00a0<\/span><\/p>\n<\/article>\n [\/et_pb_text][et_pb_text disabled_on=”on|on|on” _builder_version=”4.16″ text_font=”||||||||” text_text_color=”#141414″ text_line_height=”1.9em” header_font=”||||||||” header_2_font=”||||||||” module_alignment=”center” custom_margin=”||30px|” disabled=”on” global_colors_info=”{}”]<\/p>\n Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ornare suspendisse sed nisi lacus sed viverra tellus in.<\/p>\n [\/et_pb_text][et_pb_button button_text=”Lorem ipsum dolor sit amet” disabled_on=”on|on|on” _builder_version=”4.16″ custom_button=”on” button_text_size=”16px” button_text_color=”#FFFFFF” button_bg_color=”#00AEEF” button_border_width=”0px” button_border_radius=”0px” button_font=”||||||||” button_icon=”5||divi||400″ button_icon_color=”#FFFFFF” button_on_hover=”off” custom_margin=”20px|||” custom_padding=”12px|28px|12px|28px” box_shadow_style=”preset1″ box_shadow_vertical=”10px” box_shadow_blur=”30px” box_shadow_color=”#58595B” button_letter_spacing_hover=”2px” disabled=”on” locked=”off” global_colors_info=”{}” button_text_size__hover_enabled=”off” button_one_text_size__hover_enabled=”off” button_two_text_size__hover_enabled=”off” button_text_color__hover_enabled=”off” button_one_text_color__hover_enabled=”off” button_two_text_color__hover_enabled=”off” button_border_width__hover_enabled=”off” 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global_colors_info=”{}”][et_pb_column type=”1_2″ _builder_version=”4.16″ background_position=”top_left” custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_text admin_label=”Bayesian” _builder_version=”4.16″ text_font=”||||||||” text_text_color=”#FFFFFF” text_line_height=”22px” header_font=”||||” header_text_color=”#FFFFFF” header_2_font=”|700|||||||” header_2_text_color=”#221F72″ header_4_text_color=”#FFFFFF” header_5_text_color=”#FFFFFF” background_size=”initial” background_position=”top_left” background_repeat=”repeat” max_width=”700px” custom_padding=”0px|||||” animation_style=”slide” animation_direction=”top” animation_intensity_slide=”4%” global_colors_info=”{}”]<\/p>\n <\/p>\n [\/et_pb_text][et_pb_text _builder_version=”4.16″ text_font=”||||||||” text_text_color=”#FFFFFF” text_line_height=”22px” header_font=”||||” header_text_color=”#FFFFFF” header_4_text_color=”#FFFFFF” header_5_text_color=”#FFFFFF” background_size=”initial” background_position=”top_left” background_repeat=”repeat” max_width=”700px” custom_padding=”0px|||||” animation_style=”slide” animation_direction=”top” animation_intensity_slide=”4%” global_colors_info=”{}”]<\/p>\n For many novel therapies where the efficacy does not necessarily increase with the dose, the maximum tolerated dose (MTD) may not be the optimal dose for treating patients and the goals of dose-finding trials needs to be identifying the optimal biologic dose (OBD) that optimizes patients\u2019 risk-benefit trade-off. A flexible Bayesian optimal interval phase I\/II (BOIN12) design is introduced to find that OBD. This design makes the decision of dose escalation and de-escalation by simultaneously taking account of efficacy and toxicity and adaptively allocating patients to the dose that optimizes the toxicity-efficacy trade-off.\u00a0Our physician investigators found this design to be simpler to comprehend and implement because it overcomes the computational and implementation complexity that plagues existing Bayesian phase I\/II dose-finding designs.<\/p>\n [\/et_pb_text][et_pb_button button_url=”https:\/\/labs.icahn.mssm.edu\/design\/option1-research” button_text=”Find Out More ” disabled_on=”on|on|on” _builder_version=”4.16″ _module_preset=”default” custom_button=”on” button_text_color=”#d80b8c” button_bg_color=”#FFFFFF” button_border_radius=”0px” box_shadow_style=”preset1″ disabled=”on” global_colors_info=”{}”][\/et_pb_button][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.16″ background_position=”top_left” custom_padding=”|||” global_colors_info=”{}” 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src=”https:\/\/labs.icahn.mssm.edu\/ferketlab\/wp-content\/uploads\/sites\/505\/2022\/07\/ai-008.png” alt=”Spiral ” title_text=”ai-008″ _builder_version=”4.16″ _module_preset=”default” background_color=”#FFFFFF” global_colors_info=”{}”][\/et_pb_image][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ admin_label=”Two Column Image and Text over Button – Section 4″ module_class=”section4_two_col_img_txt_over_btn” _builder_version=”4.16″ background_color=”#00AEEF” background_image=”https:\/\/labs.icahn.mssm.edu\/ferketlab\/wp-content\/uploads\/sites\/505\/2022\/12\/Background-1ALT.png” parallax=”on” width=”100%” custom_margin=”30px||||false|false” custom_padding=”80px|0px|63px|0px||” mix_blend_mode=”multiply” bottom_divider_style=”ramp2″ bottom_divider_color=”#FFFFFF” bottom_divider_height=”150px” bottom_divider_flip=”horizontal” bottom_divider_arrangement=”above_content” locked=”off” collapsed=”off” global_colors_info=”{}”][et_pb_row column_structure=”1_2,1_2″ admin_label=”Clinical informatics facilitates risk prediction to guide personalized bladder cancer treatment decisions” _builder_version=”4.16″ background_color=”RGBA(0,0,0,0)” background_size=”initial” background_position=”top_left” background_repeat=”repeat” custom_margin=”|||” custom_padding=”|||” global_colors_info=”{}”][et_pb_column type=”1_2″ _builder_version=”4.16″ background_position=”top_left” custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_image src=”https:\/\/labs.icahn.mssm.edu\/tcibci\/wp-content\/uploads\/sites\/505\/2023\/09\/Galasky-et-al-figure.png” alt=”risk prediction to guide personalized bladder cancer treatment decisions Cystectomy versus neoadjuvant chemotherapy followed by Cystectomy ” title_text=”Galasky et al figure” disabled_on=”off|off|off” _builder_version=”4.16″ animation_style=”slide” animation_direction=”left” animation_intensity_slide=”10%” box_shadow_style=”preset1″ box_shadow_color=”#58595B” always_center_on_mobile=”off” global_colors_info=”{}”][\/et_pb_image][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.16″ background_color=”RGBA(0,0,0,0)” background_position=”top_left” custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_text _builder_version=”4.16″ _module_preset=”default” text_text_color=”#FFFFFF” header_font=”|700|||||||” header_text_color=”#FFFFFF” header_2_font=”|700|||||||” header_2_text_color=”#221F72″ global_colors_info=”{}”]<\/p>\n [\/et_pb_text][et_pb_text _builder_version=”4.16″ text_font=”||||||||” text_text_color=”#FFFFFF” text_line_height=”22px” header_font=”||||” header_text_color=”#FFFFFF” header_2_text_color=”#FFFFFF” header_4_text_color=”#FFFFFF” header_5_text_color=”#FFFFFF” background_size=”initial” background_position=”top_left” background_repeat=”repeat” max_width=”700px” custom_padding=”0px|||||” animation_style=”slide” animation_direction=”top” animation_intensity_slide=”4%” global_colors_info=”{}”]<\/p>\n Clinical guidelines support the use of neoadjuvant chemotherapy (NAC) for the treatment of muscle-invasive bladder cancer (MIBC), but NAC <\/span>remains<\/span> used in only a small subset of patients. A<\/span>n important <\/span>barrier is the difficulty in predicting<\/span> and communicating<\/span> individual <\/span>survival <\/span>estimates <\/span>with or without NAC<\/span>. <\/span>Through a collaboration of clinical and qualitative researchers, we develop<\/span>ed<\/span> individualized risk prediction and communication methods to <\/span>facilitate<\/span> NAC decision making<\/span> using the <\/span>National Cancer Database. <\/span>Based on <\/span>state-transition modeling, five-year cancer mortality and other-cause mortality risk estimates were generated and integrated within a web-based tool. Clinicians thought the prediction<\/span> tool was<\/span> \u201ceasy to use\u201d (100%) and would <\/span>use <\/span>it<\/span> \u201c<\/span>frequently in their practice\u201d (92%).<\/span><\/span>\u00a0<\/span><\/p>\n [\/et_pb_text][et_pb_button button_url=”https:\/\/pubmed.ncbi.nlm.nih.gov\/30657403\/” url_new_window=”on” button_text=”Find Out More ” _builder_version=”4.16″ _module_preset=”default” custom_button=”on” button_text_color=”#d80b8c” button_bg_color=”#FFFFFF” button_border_radius=”0px” box_shadow_style=”preset1″ global_colors_info=”{}”][\/et_pb_button][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][et_pb_row column_structure=”1_2,1_2″ _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”1_2″ _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.16″ _module_preset=”default” text_text_color=”#00AEEF” header_text_color=”#FFFFFF” header_2_text_color=”#00AEEF” global_colors_info=”{}”]<\/p>\n [\/et_pb_text][et_pb_text _builder_version=”4.16″ text_font=”||||||||” text_text_color=”#141414″ text_line_height=”22px” header_font=”||||” header_text_color=”#FFFFFF” header_4_text_color=”#FFFFFF” header_5_text_color=”#FFFFFF” background_size=”initial” background_position=”top_left” background_repeat=”repeat” max_width=”700px” custom_padding=”0px|||||” animation_style=”slide” animation_direction=”top” animation_intensity_slide=”4%” global_colors_info=”{}”]<\/p>\n The Joint Commission has mandated universal screening and assessment of hospitalized patients for malnutrition because of its association with increased morbidity, mortality, and healthcare costs. Early detection is extremely important for timely intervention but difficult to achieve due to need for specialized skills from registered dieticians (RDs).\u00a0 Machine learning (ML) algorithms, which process massive amounts of data and learn continuously have the potential to identify those at risk for malnutrition more efficiently and effectively than standard screening tools. A clinical decision support tool, MUST-Plus, developed by this team reduced lag between admission and diagnosis, had high usability (> 90%), and increased the rate of malnutrition diagnoses and its documentation.<\/p>\n <\/p>\n [\/et_pb_text][et_pb_button button_url=”https:\/\/clinicaldatascience.mountsinai.org\/” url_new_window=”on” button_text=”Find Out More ” _builder_version=”4.16″ _module_preset=”default” custom_button=”on” button_text_color=”#FFFFFF” button_bg_color=”#D80B8C” button_border_radius=”0px” box_shadow_style=”preset1″ global_colors_info=”{}”][\/et_pb_button][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n [\/et_pb_text][et_pb_image src=”https:\/\/labs.icahn.mssm.edu\/tcibci\/wp-content\/uploads\/sites\/505\/2023\/10\/ML-and-malnutrition.png” alt=”Dietitian Workflow flowchart for malnutrition assessment” title_text=”ML and malnutrition” disabled_on=”off|off|off” _builder_version=”4.16″ animation_style=”slide” animation_direction=”left” animation_intensity_slide=”10%” box_shadow_style=”preset1″ box_shadow_color=”#58595B” always_center_on_mobile=”off” global_colors_info=”{}”][\/et_pb_image][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ disabled_on=”off|off|off” admin_label=”Fullwidth Video over Background – Section 9″ module_class=”fullwidth-video-over-background-section-9″ _builder_version=”4.16″ _module_preset=”default” background_color=”#ededed” background_image=”https:\/\/labs.icahn.mssm.edu\/ferketlab\/wp-content\/uploads\/sites\/505\/2022\/10\/4.png” height_tablet=”” height_phone=”” height_last_edited=”on|desktop” max_height=”100%” custom_margin=”||4%||false|false” custom_padding=”0px||0px||false|false” locked=”off” collapsed=”off” global_colors_info=”{}”][et_pb_row column_structure=”1_5,3_5,1_5″ _builder_version=”4.16″ _module_preset=”default” 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url_new_window=”on”]instagram[\/et_pb_social_media_follow_network][\/et_pb_social_media_follow][et_pb_text _builder_version=”4.16″ _module_preset=”default” text_text_color=”#FFFFFF” text_font_size=”24px” global_colors_info=”{}”]<\/p>\n Contact Us<\/strong><\/p>\n [\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_3,1_3,1_3″ _builder_version=”4.16″ background_color=”RGBA(0,0,0,0)” background_color_gradient_direction=”38deg” module_alignment=”center” custom_margin=”|auto|-2px|auto||” custom_padding=”0px|||||” locked=”off” collapsed=”off” global_colors_info=”{}”][et_pb_column type=”1_3″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_text _builder_version=”4.16″ _module_preset=”default” text_font=”||||||||” text_text_color=”#FFFFFF” text_line_height=”22px” global_colors_info=”{}”]<\/p>\n Contact Us [\/et_pb_text][\/et_pb_column][et_pb_column type=”1_3″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_text _builder_version=”4.16″ text_font=”||||||||” text_text_color=”#FFFFFF” text_line_height=”1.25em” link_text_color=”#fff60c” header_font=”||||||||” header_4_font=”|700||on|||||” header_4_font_size=”16px” header_4_letter_spacing=”1px” header_4_line_height=”1.5em” background_layout=”dark” locked=”off” global_colors_info=”{}”]<\/p>\nBiostatistics core utilizes Bayesian Optimal Interval Phase I\/II (BOIN12) design to assess safety and efficacy of a combination regimen for myelodysplastic syndrome\/myeloproliferative neoplasm (MDS\/MPN) overlap syndrome<\/h2>\n
Story 2 – Erin’s complicated data analysis<\/strong><\/h1>\n
Clinical informatics facilitates risk prediction to guide personalized bladder cancer treatment decisions<\/span><\/h2>\n
Clinical informatics with MSHS data science team develops a malnutrition screening tool to increase diagnosis rate\u00a0 <\/strong><\/h2>\n
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